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1
Kowalczyk, Stacy T. "Modelling the Research Data Lifecycle." International Journal of Digital Curation 12, no. 2 (2018): 331–61. http://dx.doi.org/10.2218/ijdc.v12i2.429.
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This paper develops and tests a lifecycle model for the preservation of research data by investigating the research practices of scientists. This research is based on a mixed-method approach. An initial study was conducted using case study analytical techniques; insights from these case studies were combined with grounded theory in order to develop a novel model of the Digital Research Data Lifecycle. A broad-based quantitative survey was then constructed to test and extend the components of the model. The major contribution of these research initiatives are the creation of the Digital Research Data Lifecycle, a data lifecycle that provides a generalized model of the research process to better describe and explain both the antecedents and barriers to preservation. The antecedents and barriers to preservation are data management, contextual metadata, file formats, and preservation technologies. The availability of data management support and preservation technologies, the ability to create and manage contextual metadata, and the choices of file formats all significantly effect the preservability of research data.
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Subaveerapandiyan, A., and Maurya Anuradha. "Research Data Preservation Practices of Library and Information Science Faculties." DESIDOC Journal of Library & Information Technology 42, no. 4 (2022): 259–64. https://doi.org/10.14429/djlit.42.4.17538.
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Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.
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Raman, Ganguly. "Digital Ecosystems for Data Preservation." Digital Platform: Information Technologies in Sociocultural Sphere 1, no. 1 (2018): 87–96. https://doi.org/10.31866/2617-796x.1.2018.151343.
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The purpose of the investigation is the review and research of digital ecosystems for data preservation. This paper will address issues concerning the handling of complex data such as research data, multimedia content, e‐learning content, and the use of repositories infrastructures. At the University of Vienna, an ecosystem for digital data preservation and research data management has already been established and will be subsequently be enlarged according to future needs and requirements in the future. This living digital ecosystem is the foundation for research data management and was implemented from the beginning as a central service according to the FAIR principles as stated in the first HLEG‐EOSC (https://ec.europa.eu/digitalsingle‐market) report. With the help of ten years of professional experience, a model for digital data preservation was established to address the complexity of heterogeneous data. It was necessary because of different use cases assigned to the interdisciplinary data management team based on the Computer Centre and the Library. The source for the use cases are research projects, their different approach to research and their multifaceted requirements regarding the efficient re‐use of data. The usage of this model might be considered as the foundation on which an ecosystem for digital data preservation can be built. Research methods is the management Phaidra Management created a model based on the Data Publication Pyramid, and added data not directly included in publications, such as inconclusive and negative results. Scientific novelty is to use three different models as a guide, the management redesigned the repository infrastructure, an important starting point for the transition from a simple repository concept to a living digital ecosystem concept. Conclusions. It provides a good working infrastructure, and connect with the research community and maintain links to other infrastructure projects.
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Conway, Esther, Brian Matthews, David Giaretta, Simon Lambert, Michael Wilson, and Nick Draper. "Managing Risks in the Preservation of Research Data with Preservation Networks." International Journal of Digital Curation 7, no. 1 (2012): 3–15. http://dx.doi.org/10.2218/ijdc.v7i1.210.
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Network modelling provides a framework for the systematic analysis of needs and options for preservation. A number of general strategies can be identified, characterised and applied to many situations; these strategies may be combined to produce robust preservation solutions tailored to the needs of the community and responsive to their environment. This paper provides an overview of this approach. We describe the components of a Preservation Network Model and go on to show how it may be used to plan preservation actions according to the requirements of the particular situation using illustrative examples from scientific archives.
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Navale, Vivek, and Matthew McAuliffe. "Long-term preservation of biomedical research data." F1000Research 7 (August 29, 2018): 1353. http://dx.doi.org/10.12688/f1000research.16015.1.
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Genomics and molecular imaging, along with clinical and translational research have transformed biomedical science into a data-intensive scientific endeavor. For researchers to benefit from Big Data sets, developing long-term biomedical digital data preservation strategy is very important. In this opinion article, we discuss specific actions that researchers and institutions can take to make research data a continued resource even after research projects have reached the end of their lifecycle. The actions involve utilizing an Open Archival Information System model comprised of six functional entities: Ingest, Access, Data Management, Archival Storage, Administration and Preservation Planning. We believe that involvement of data stewards early in the digital data life-cycle management process can significantly contribute towards long term preservation of biomedical data. Developing data collection strategies consistent with institutional policies, and encouraging the use of common data elements in clinical research, patient registries and other human subject research can be advantageous for data sharing and integration purposes. Specifically, data stewards at the onset of research program should engage with established repositories and curators to develop data sustainability plans for research data. Placing equal importance on the requirements for initial activities (e.g., collection, processing, storage) with subsequent activities (data analysis, sharing) can improve data quality, provide traceability and support reproducibility. Preparing and tracking data provenance, using common data elements and biomedical ontologies are important for standardizing the data description, making the interpretation and reuse of data easier. The Big Data biomedical community requires scalable platform that can support the diversity and complexity of data ingest modes (e.g. machine, software or human entry modes). Secure virtual workspaces to integrate and manipulate data, with shared software programs (e.g., bioinformatics tools), can facilitate the FAIR (Findable, Accessible, Interoperable and Reusable) use of data for near- and long-term research needs.
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Burgi, Pierre-Yves, Eliane Blumer, and Basma Makhlouf-Shabou. "Research data management in Switzerland." IFLA Journal 43, no. 1 (2017): 5–21. http://dx.doi.org/10.1177/0340035216678238.
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In this article, the authors report on an ongoing data life cycle management national project realized in Switzerland, with a major focus on long-term preservation. Based on an extensive document analysis as well as semi-structured interviews, the project aims at providing national services to respond to the most relevant researchers’ data life cycle management needs, which include: guidelines for establishing a data management plan, active data management solutions, long-term preservation storage options, training, and a single point of access and contact to get support. In addition to presenting the different working axes of the project, the authors describe a strategic management and lean startup template for developing new business models, which is key for building viable services.
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Moore, Fred. "Long Term Data Preservation." EDPACS 27, no. 12 (2000): 1. http://dx.doi.org/10.1201/1079/43258.27.12.20000601/30347.7.
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Devouassoux, Marion, João Fernandes, Bob Jones, Ignacio Peluaga Lozada, and Jakub Urban. "ARCHIVER - Data archiving and preservation for research environments." EPJ Web of Conferences 251 (2021): 02044. http://dx.doi.org/10.1051/epjconf/202125102044.
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Over the last decades, several data preservation efforts have been undertaken by the HEP community, as experiments are not repeatable and consequently their data considered unique. ARCHIVER is a European Commission (EC) co-funded Horizon 2020 pre-commercial procurement project procuring R&D combining multiple ICT technologies including data-intensive scalability, network, service interoperability and business models, in a hybrid cloud environment. The results will provide the European Open Science Cloud (EOSC) with archival and preservation services covering the full research lifecycle. The services are co-designed in partnership with four research organisations (CERN, DESY, EMBL-EBI and PIC/IFAE) deploying use cases from Astrophysics, HEP, Life Sciences and Photon-Neutron Sciences creating an innovation ecosystem for specialist data archiving and preservation companies willing to introduce new services capable of supporting the expanding needs of research. The HEP use cases being deployed include the CERN Opendata portal, preserving a second copy of the completed BaBar experiment and the CERN Digital Memory digitising CERN’s multimedia archive of the 20th century. In parallel, ARCHIVER has established an Early Adopter programme whereby additional use cases can be incorporated at each of the project phases thereby expanding services to multiple research domains and countries.
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Mozzherin, Dmitry, and Deborah Paul. "Preservation strategies for biodiversity data." Biodiversity Information Science and Standards 7 (August 22, 2023): e111453. https://doi.org/10.3897/biss.7.111453.
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We are witnessing a fast proliferation of biodiversity informatics projects. The data accumulated by these initiatives often grows rapidly, even exponentially. Most of these projects start small and do not foresee the data achitecture challenges of their future. Organizations may lack the necessary expertise and/or money to strategically address the care and feeding of this expanding data pile. In other cases, individuals with the expertise to address these needs may be present, but lack the power or status or possibly the bandwidth to take effective actions. Over time, the data may increase in size to such an extent that handling and preserving it becomes an almost insurmountable problem. The most common technical challenges include migrating data from one physical data storage to another, organizing backups, providing fast disaster recovery, and preparing data to be accessible for posterity. Some sociotechnical and strategic hurdles noted when trying to address data stewardship include funding, data leadership (Stack and Stadolnik 2018) and vision (or lack thereof), and organizational structure and culture. The biodiversity data collected today will be indispensable for future research, and it is our collective responsibility to preserve it for current and future generations.Some of the most common information loss risk factors are the end of funding, retirement of a researcher, or the departure of a critical researcher or programmer. More risk factors, such as hardware malfunction, hurricanes, tornadoes, and severe magnetic storms, can destroy the data carefully collected by large groups of people.The co-location of original data and backups create a "Library of Alexandria" where a single disaster at this location can lead to permanent data loss and poses an existential threat to the project.Biodiversity data becomes more valuable over time and should survive for several centuries. However, SSD (solid-state drive) and HDD (hard disk drive) storage solutions have an expiration date of only a few years. We propose the following solutions (Fig. 1) to provide long-term data security:Technical tacticsUse an immutable file storage for everything that is not entered very recently.Most of the biodiversity "big data" are files that are written once and never changed again. We suggest separating storage into a read-only part and small read/write sections. Data from the read/write section can be moved to the read-only part often, for example, daily.Use a Copy-On-Write file system, such as ZFS (Zettabyte File System).The ZFS file system is widely used in industry and is known for its robustness and error resistance. It allows efficient incremental backups and much faster data transfer than other systems. Regular incremental backups can work even with slow internet connections. ZFS provides real-time data integrity checks and uses powerful tools for data healing.Split data and its backups into smaller chunks.Dividing backups into cost-effective 2–8 terabyte chunks allows running backups using cheap hardware. Assuming that the data is read-only, such data organization always splits the backup into chunks, with hardware costs changing from tens of thousands of dollars (US) to less than two thousand dollars. We recognize that with time data storage costs drop, and larger chunks will be used.Split the data even further to the size of the largest available long-term storage unit (currently an optical M-disc).The write-once optical M-DISC is analogous to a Sumerian clay tablet. Data written on such discs does not deteriorate for hundreds of years. This option addresses the need for last resort backups because the storage does not depend on magnetic properties and is impervious to electromagnetic disasters. Optical discs can be easily and cheaply copied and distributed to libraries worldwide. In the future, discs' data can be transferred to a different long-term storage medium. We also trust these discs can be deciphered by those in the future, just like clay tablets.Sociotechnical insightsThe above example of a comprehensive strategy to preserve data epitomizes "LOCKSS" (lots of copies keep stuff safe) and makes it clear that these copies need to be in multiple media types. Our suggestions here focus on projects that experience data growth pains. Such projects often look to see how others address these data needs. Recently, The Species File Group (SFG) did this exercise to evaluate and address our data growth needs (Mozzherin et al. 2023). We recognize and emphasize here the need forpersonnel with the knowledge and skills to build, maintain, and evolve robust strategies and infrastructure to make data accessible and preserve it,funding to back the most suitable architectural strategies to do so, andpeople with expertise in long-term data security to have a seat at the leadership table in our organizations.We encourage our colleagues to evaluate the status of data leadership at your organizations (Stack and Stadolnik 2018, Kalms 2012). Implementing these suggestions will help ensure the survival of the data and accompanying software for hundreds of years to come.
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Sesartic, Ana, and Matthias Töwe. "Research Data Services at ETH-Bibliothek." IFLA Journal 42, no. 4 (2016): 284–91. http://dx.doi.org/10.1177/0340035216674971.
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The management of research data throughout its life-cycle is both a key prerequisite for effective data sharing and efficient long-term preservation of data. This article summarizes the data services and the overall approach to data management as currently practised at ETH-Bibliothek, the main library of ETH Zürich, the largest technical university in Switzerland. The services offered by service providers within ETH Zürich cover the entirety of the data life-cycle. The library provides support regarding conceptual questions, offers training and services concerning data publication and long-term preservation. As research data management continues to play a steadily more prominent part in both the requirements of researchers and funders as well as curricula and good scientific practice, ETH-Bibliothek is establishing close collaborations with researchers, in order to promote a mutual learning process and tackle new challenges.
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Kim, Jeonghyun, and James Faulkner. "Appraising Research Data for Long‐Term Preservation: An Analysis of Research Data Collection Policies." Proceedings of the Association for Information Science and Technology 58, no. 1 (2021): 750–52. http://dx.doi.org/10.1002/pra2.550.
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Currie, Amy, and William Kilbride. "FAIR Forever? Accountabilities and Responsibilities in the Preservation of Research Data." International Journal of Digital Curation 16, no. 1 (2021): 16. http://dx.doi.org/10.2218/ijdc.v16i1.768.
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Digital preservation is a fast-moving and growing community of practice of ubiquitous relevance, but in which capability is unevenly distributed. Within the open science and research data communities, digital preservation has a close alignment to the FAIR principles and is delivered through a complex specialist infrastructure comprising technology, staff and policy. However, capacity erodes quickly, establishing a need for ongoing examination and review to ensure that skills, technology, and policy remain fit for changing purpose. To address this challenge, the Digital Preservation Coalition (DPC) conducted the FAIR Forever study, commissioned by the European Open Science Cloud (EOSC) Sustainability Working Group and funded by the EOSC Secretariat Project in 2020, to assess the current strengths, weaknesses, opportunities and threats to the preservation of research data across EOSC, and the feasibility of establishing shared approaches, workflows and services that would benefit EOSC stakeholders.
 This paper draws from the FAIR Forever study to document and explore its key findings on the identified strengths, weaknesses, opportunities, and threats to the preservation of FAIR data in EOSC, and to the preservation of research data more broadly. It begins with background of the study and an overview of the methodology employed, which involved a desk-based assessment of the emerging EOSC vision, interviews with representatives of EOSC stakeholders, and focus groups with digital preservation specialists and data managers in research organizations. It summarizes key findings on the need for clarity on digital preservation in the EOSC vision and for elucidation of roles, responsibilities, and accountabilities to mitigate risks of data loss, reputation, and sustainability. It then outlines the recommendations provided in the final report presented to the EOSC Sustainability Working Group.
 To better ensure that research data can be FAIRer for longer, the recommendations of the study are presented with discussion on how they can be extended and applied to various research data stakeholders in and outside of EOSC, and suggest ways to bring together research data curation, management, and preservation communities to better ensure FAIRness now and in the long term.
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Humphrey, Chuck. "The Preservation of Research Data in a Postmodern Culture." IASSIST Quarterly 29, no. 1 (2006): 24. http://dx.doi.org/10.29173/iq601.
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ZHANG, Kun, Qing-Zhong LI, and Yu-Liang SHI. "Research on Data Combination Privacy Preservation Mechanism for SaaS." Chinese Journal of Computers 33, no. 11 (2010): 2044–54. http://dx.doi.org/10.3724/sp.j.1016.2010.02044.
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Griffin, Tina, Kristin Lee, and Lora Leligdon. "Special Issue: 2018 Research Data Access and Preservation Summit." Journal of eScience Librarianship 7, no. 3 (2018): e1158. http://dx.doi.org/10.7191/jeslib.2018.1158.
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Griffin, Tina, Rebekah Kati, Alicia Krzton, and Lora Leligdon. "Special Issue: 2019 Research Data Access and Preservation Summit." Journal of eScience Librarianship 8, no. 2 (2020): e1182. http://dx.doi.org/10.7191/jeslib.2019.1182.
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Shao, Bilin, Genqing Bian, Xirui Quan, and Zhixian Wang. "Research of privacy preservation method based on data coloring." China Communications 13, no. 10 (2016): 181–97. http://dx.doi.org/10.1109/cc.2016.7733043.
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Rasmussen, Karsten Boye, and Grant Blank. "The data documentation initiative: a preservation standard for research." Archival Science 7, no. 1 (2007): 55–71. http://dx.doi.org/10.1007/s10502-006-9036-0.
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Frank, Rebecca D., Elizabeth Yakel, and Ixchel M. Faniel. "Destruction/reconstruction: preservation of archaeological and zoological research data." Archival Science 15, no. 2 (2015): 141–67. http://dx.doi.org/10.1007/s10502-014-9238-9.
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Davis, Peter T. "Data Management: Data Destruction and Preservation, Part 1." EDPACS 31, no. 3 (2003): 1–15. http://dx.doi.org/10.1201/1079/43620.31.3.20030901/76975.1.
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Davis, Peter T. "Data Management: Data Destruction and Preservation, Part 2." EDPACS 31, no. 4 (2003): 1–8. http://dx.doi.org/10.1201/1079/43663.31.4.20031001/77388.1.
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Maurya, Anuradha, and Subaveerapandiyan A. "Research Data Preservation Practices of Library and Information Science Faculties." DESIDOC Journal of Library & Information Technology 42, no. 4 (2022): 259–64. http://dx.doi.org/10.14429/djlit.42.4.17538.
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 Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.
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Rechert, Klaus, Oleg Stobbe, Oleg Zharkow, Rafael Gieschke, and Dennis Wehrle. "CiTAR - Preserving Software-based Research." International Journal of Digital Curation 15, no. 1 (2020): 13. http://dx.doi.org/10.2218/ijdc.v15i1.716.
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 In contrast to books or published articles, pure digital output of research projects is more fragile and, thus, more difficult to preserve and more difficult to be made available and to be reused by a wider research community. Not only does a fast-growing format diversity in research data sets require additional software preservation but also today’s computer assisted research disciplines increasingly devote significant resources into creating new digital resources and software-based methods.
 In order to adapt FAIR data principles, especially to ensure re-usability of a wide variety of research outputs, novel ways for preservation of software and additional digital resources are required as well as their integration into existing research data management strategies.
 This article addresses preservation challenges and preservation options of containers and virtual machines to encapsulate software-based research methods as portable and preservable software-based research resources, provides a preservation plan as well as an implementation.
 
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Fokianos, Pamfilos, Sebastian Feger, Ilias Koutsakis, et al. "CERN Analysis Preservation and Reuse Framework: FAIR research data services for LHC experiments." EPJ Web of Conferences 245 (2020): 06011. http://dx.doi.org/10.1051/epjconf/202024506011.
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In this paper we present the CERN Analysis Preservation service as a FAIR (Findable, Accessible, Interoperable and Reusable) research data preservation repository platform for LHC experiments. The CERN Analysis Preservation repository allows LHC collaborations to deposit and share the structured information about analyses as well as to capture the individual data assets associated to the analysis. We describe the typical data ingestion pipelines, through which an individual physicist can preserve and share their final n-tuples, ROOT macros, Jupyter notebooks, or even their full analysis workflow code and any intermediate datasets of interest for preservation within the restricted context of experimental collaboration. We discuss the importance of annotating the deposited content with high-level structured information about physics concepts in order to promote information discovery and knowledge sharing inside the collaboration. Finally, we describe techniques used to facilitate the reusability of preserved data assets by capturing and re-executing reproducible recipes and computational workflows using the REANA Reusable Analysis platform.
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Conway, Esther, David Giaretta, Simon Lambert, and Brian Matthews. "Curating Scientific Research Data for the Long Term: A Preservation Analysis Method in Context." International Journal of Digital Curation 6, no. 2 (2011): 38–52. http://dx.doi.org/10.2218/ijdc.v6i2.204.
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The challenge of digital preservation of scientific data lies in the need to preserve not only the dataset itself but also the ability it has to deliver knowledge to a future user community. A true scientific research asset allows future users to reanalyze the data within new contexts. Thus, in order to carry out meaningful preservation we need to ensure that future users are equipped with the necessary information to re-use the data. This paper presents an overview of a preservation analysis methodology which was developed in response to that need on the CASPAR and Digital Curation Centre SCARP projects. We intend to place it in relation to other digital preservation practices, discussing how they can interact to provide archives caring for scientific data sets with the full arsenal of tools and techniques necessary to rise to this challenge.
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Wilson, Lee. "Exploring the Canadian Federated Research Data Repository Service." Biodiversity Information Science and Standards 1 (August 11, 2017): e20185. https://doi.org/10.3897/tdwgproceedings.1.20185.
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Good data management requires support for researchers at all stages of the data lifecycle, from policy and planning development to infrastructure that ensures data is findable, accessible, interoperable, and reusable (FAIR). While several excellent institutional, domain-specific, and general repositories currently exist both within Canada and abroad, Canada lacks nationally coordinated solutions for managing research data, and the question of where to deposit data for discovery, reuse, and preservation remains pervasive. Developed through a partnership between the Canadian Association of Research Libraries (CARL), the Portage Network, and Compute Canada, the Federated Research Data Repository (FRDR) seeks to address a longstanding gap in Canada's research infrastructure by providing a single platform from which research data can be ingested, curated, preserved, discovered, cited, and shared. The platform's federated search tool will provide a focal point to discover and access Canadian research data, while the range of services provided by FRDR will help researchers store and manage their data, preserve their research for future use, and comply with institutional and funding agency data management requirements. In this presentation, participants will learn about the development of the new system, current and planned functionality, the timeline for service launch, the proposed distributed service model to support institutions both locally and nationally, and a brief overview of research projects we will be supporting as the platform moves toward launch. Researchers will gain an understanding of how they can use FRDR to make their research data discoverable and accessible, as well as comply with increasing funder expectations for the management of research data.
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Dr., Shashidhar V., Vignesh Y, K. Varshitha, Rajesh C. Riddhi, and V. Vivek. "Disguise Guard: Annonymizing the Data to Enhance Privacy." International Journal of Multidisciplinary Research Transactions 6, no. 8 (2024): 41–60. https://doi.org/10.5281/zenodo.13371901.
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In today's data-driven world, the growth of data sharing has prompted considerable issues over privacy protection. Anonymization techniques play a significant role in minimising these issues by safeguarding people' privacy while enabling valuable data analysis and exchange. This research analyses several anonymization strategies and their consequences for privacy preservation in data sharing settings. We discuss the issues associated with balancing privacy and utility and review state-of-the-art strategies for obtaining anonymity, such as k-anonymity, l-diversity, and t-closeness. Additionally, we analyse the limitations and trade-offs of these strategies and propose future research avenues to address rising privacy challenges in the context of developing data environments.
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Minor, David, Don Sutton, Ardys Kozbial, Brad Westbrook, Michael Burek, and Michael Smorul. "Chronopolis Digital Preservation Network." International Journal of Digital Curation 5, no. 1 (2010): 119–33. http://dx.doi.org/10.2218/ijdc.v5i1.147.
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The Chronopolis Digital Preservation Initiative, one of the Library of Congress’ latest efforts to collect and preserve at-risk digital information, has completed its first year of service as a multi-member partnership to meet the archival needs of a wide range of domains.Chronopolis is a digital preservation data grid framework developed by the San Diego Supercomputer Center (SDSC) at UC San Diego, the UC San Diego Libraries (UCSDL), and their partners at the National Center for Atmospheric Research (NCAR) in Colorado and the University of Maryland's Institute for Advanced Computer Studies (UMIACS).Chronopolis addresses a critical problem by providing a comprehensive model for the cyberinfrastructure of collection management, in which preserved intellectual capital is easily accessible, and research results, education material, and new knowledge can be incorporated smoothly over the long term. Integrating digital library, data grid, and persistent archive technologies, Chronopolis has created trusted environments that span academic institutions and research projects, with the goal of long-term digital preservation.A key goal of the Chronopolis project is to provide cross-domain collection sharing for long-term preservation. Using existing high-speed educational and research networks and mass-scale storage infrastructure investments, the partnership is leveraging the data storage capabilities at SDSC, NCAR, and UMIACS to provide a preservation data grid that emphasizes heterogeneous and highly redundant data storage systems.In this paper we will explore the major themes within Chronopolis, including:a) The philosophy and theory behind a nationally federated data grid for preservation. b) The core tools and technologies used in Chronopolis. c) The metadata schema that is being developed within Chronopolis for all of the data elements. d) Lessons learned from the first year of the project.e) Next steps in digital preservation using Chronopolis: how we plan to strengthen and broaden our network with enhanced services and new customers.
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Pejša, Stanislav, Shirley J. Dyke, and Thomas J. Hacker. "Building Infrastructure for Preservation and Publication of Earthquake Engineering Research Data." International Journal of Digital Curation 9, no. 2 (2014): 83–97. http://dx.doi.org/10.2218/ijdc.v9i2.335.
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The objective of this paper is to showcase the progress of the earthquake engineering community during a decade-long effort supported by the National Science Foundation in the George E. Brown Jr., Network for Earthquake Engineering Simulation (NEES). During the four years that NEES network operations have been headquartered at Purdue University, the NEEScomm management team has facilitated an unprecedented cultural change in the ways research is performed in earthquake engineering. NEES has not only played a major role in advancing the cyberinfrastructure required for transformative engineering research, but NEES research outcomes are making an impact by contributing to safer structures throughout the USA and abroad. This paper reflects on some of the developments and initiatives that helped instil change in the ways that the earthquake engineering and tsunami community share and reuse data and collaborate in general.
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Kassam-Adams, Nancy, and Miranda Olff. "Embracing data preservation, sharing, and re-use in traumatic stress research." European Journal of Psychotraumatology 11, no. 1 (2020): 1739885. http://dx.doi.org/10.1080/20008198.2020.1739885.
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Moore, Reagan, and William L. Anderson. "ASIS&T research data access and preservation summit: Conference summary." Bulletin of the American Society for Information Science and Technology 36, no. 6 (2010): 42–45. http://dx.doi.org/10.1002/bult.2010.1720360611.
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Van, Tuyl Steven, and Amanda Whitmire. "Investigation of Non-Academic Data Management Practices to Inform Academic Research Data Management." Research Ideas and Outcomes 4 (October 31, 2018): e30829. https://doi.org/10.3897/rio.4.e30829.
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In recent years, the academic research data management (RDM) community has worked closely with funding agencies, university administrators, and researchers to develop best practices for RDM. The RDM community, however, has spent relatively little time exploring best practices used in non-academic environments (industry, government, etc.) for management, preservation, and sharing of data. In this poster, we present the results of a project wherein we approached a number of non-academic corporations and institutions to discuss how data is managed in those organizations and discern what the academic RDM community could learn from non-academic RDM practices. We conducted interviews with 10-20 companies including tech companies, government agencies, and consumer retail corporations. We present the results in the form of user stories, common themes from interviews, and summaries of areas where the RDM community might benefit from further understanding of non-academic data management practices.
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Adu, Kofi Koranteng, Luyande Dube, and Emmanuel Adjei. "Digital preservation." Library Hi Tech 34, no. 4 (2016): 733–47. http://dx.doi.org/10.1108/lht-07-2016-0078.
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Purpose The purpose of this paper is to explore the extent to which digital preservation facilitate the implementation of electronic government, open data and the right to information. Design/methodology/approach A case study research which chronicles the link between transparency and data availability. It makes use of a theoretical framework based on the open archival information system to analyse, explain, clarify and justify the application of open data, electronic government and the right to information. Findings The paper argued that e-government, open data and the RTI will remain elusive if a digital preservation infrastructure is not pursued. Within the context of e-government, the paper adumbrated that government agencies can incorporate e-government legislations into their digital preservation activities, precisely because the relationship between digital preservation and e-government have always been symbiotic. It alluded to the fact that an obligation will be placed on all public authorities and private agencies covered by the RTL law to create, keep and organise an effective and efficient system of record keeping, so as to give meaning to the right to information when citizens apply for information. Practical implications Future research should examine closely the implication of open data government within the context of digital preservation. Whilst digital preservation looks forward to the longevity of digital records and its accessibility, open data focusses on the utility of these records through online services, reuse and distribution for the purposes of transparency and citizens’ participation. Originality/value The application of digital preservation to open data in this paper appears to be more relevant at a time when most governments of the world are striving to obtain data to fight poverty, achieve universal primary education, fight HIV and foster maternal health. Its originality can further be established from the symbiotic relationship between digital preservation and electronic government, open data and the right to information.
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Choudhury, Sayeed, Tim DiLauro, Alex Szalay, et al. "Digital Data Preservation for Scholarly Publications in Astronomy." International Journal of Digital Curation 2, no. 2 (2008): 20–30. http://dx.doi.org/10.2218/ijdc.v2i2.26.
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Astronomy is similar to other scientific disciplines in that scholarly publication relies on the presentation and interpretation of data. But although astronomy now has archives for its primary research telescopes and associated surveys, the highly processed data that is presented in the peer-reviewed journals and is the basis for final analysis and interpretation is generally not archived and has no permanent repository. We have initiated a project whose goal is to implement an end-to-end prototype system which, through a partnership of a professional society, that society’s scholarly publications/publishers, research libraries, and an information technology substrate provided by the Virtual Observatory, will capture high-level digital data as part of the publication process and establish a distributed network of curated, permanent data repositories. The data in this network will be accessible through the research journals, astronomy data centers, and Virtual Observatory data discovery portals.
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M., Santhiya Devi*1 &. Dr.K.Arunesh2. "PRIVACY PRESERVATION TECHNIQUES FOR PERSONALIZED DATA IN BIG DATA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 4 (2019): 335–40. https://doi.org/10.5281/zenodo.2653603.
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The recent advancements in this digital world huge amount of information are generated and shared, and the management of such large data is the most difficult and challenging task. Due to its size and variety of data, its name big data was derived. In the management of this data, some information may be disclosed. This type of disclosure can lead to leakage of Personal Identifiable Information (PII), as it contains individual’s information. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However, data analytics is prone to privacy violations. Due to this, privacy has become one a major challenge of big data. There are different approaches to privacy preservation is processed like encryption based methodology, anonymization based method and noise based techniques in big data. Here the issues and methods are discussed and successfully analysed with privacy preservation and Big Data. This research paper examined various privacy threats, privacy preservation techniques and models with their limitations, and the analysis shows which one is the best method that guaranteed true privacy.
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Specht, Alison, Matthew Bolton, Bryn Kingsford, Raymond Specht, and Lee Belbin. "A story of data won, data lost and data re-found: the realities of ecological data preservation." Biodiversity Data Journal 6 (November 7, 2018): e28073. https://doi.org/10.3897/BDJ.6.e28073.
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This paper discusses the process of retrieval and updating legacy data to allow on-line discovery and delivery. There are many pitfalls of institutional and non-institutional ecological data conservation over the long term. Interruptions to custodianship, old media, lost knowledge and the continuous evolution of species names makes resurrection of old data challenging. We caution against technological arrogance and emphasise the importance of international standards. We use a case study of a compiled set of continent-wide vegetation survey data for which, although the analyses had been published, the raw data had not. In the original study, publications containing plot data collected from the 1880s onwards had been collected, interpreted, digitised and integrated for the classification of vegetation and analysis of its conservation status across Australia. These compiled data are an extremely valuable national collection that demanded publishing in open, readily accessible online repositories, such as the Terrestrial Ecosystem Research Network (http://www.tern.org.au) and the Atlas of Living Australia (ALA: http://www.ala.org.au), the Australian node of the Global Biodiversity Information Facility (GBIF: http://www.gbif.org). It is hoped that the lessons learnt from this project may trigger a sober review of the value of endangered data, the cost of retrieval and the importance of suitable and timely archiving through the vicissitudes of technological change, so the initial unique collection investment enables multiple re-use in perpetuity.
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Pinnick, Jaana. "Exploring digital preservation requirements." Records Management Journal 27, no. 2 (2017): 175–91. http://dx.doi.org/10.1108/rmj-04-2017-0009.
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Purpose The aim of this paper was to explore digital preservation requirements within the wider National Geoscience Data Centre (NGDC) organisational framework in preparation for developing a preservation policy and integrating associated preservation workflows throughout the existing research data management processes. This case study is based on an MSc dissertation research undertaken at Northumbria University. Design/methodology/approach This mixed methods case study used quantitative and qualitative data to explore the preservation requirements and triangulation to strengthen the design validity. Corporate and the wider scientific priorities were identified through literature and a stakeholder survey. Organisational preparedness was investigated through staff interviews. Findings Stakeholders expect data to be reliable, reusable and available in preferred formats. To ensure digital continuity, the creation of high-quality metadata is critical, and data depositors need data management training to achieve this. Recommendations include completing a risk assessment, creating a digital asset register and a technology watch to mitigate against risks. Research limitations/implications The main constraint in this study is the lack of generalisability of results. As the NGDC is a unique organisation, it may not be possible to generalise the organisational findings, although those relating to research data management may be transferrable. Originality/value This research examines the specific nature of geoscience data retention requirements and looks at existing NGDC procedures in terms of enhancing digital continuity, providing new knowledge on the preservation requirements for a number of national datasets.
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Pawar, Ankush Balaram, Shashikant U. Ghumbre, and Rashmi M. Jogdand. "Study and Analysis of Various Cloud Security, Authentication, and Data Storage Models." International Journal of Decision Support System Technology 15, no. 1 (2023): 1–16. http://dx.doi.org/10.4018/ijdsst.315760.
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In recent days, widespread acceptance of cloud data storage applications increases various privacy problems and security problems. Outsourced data security is considered the main confrontation for cloud clients because of data control loss. This review presents a detailed survey of 50 research papers presenting privacy preservation approaches, namely authentication-based, cloud security-based, data storage-based, data security-based, and encryption-based techniques. The analysis is considered based on the categorization of approaches, dataset employed, utilized software tools, published year, and the performance metrics are discussed. Furthermore, problems raised in existing privacy preservation techniques are elucidated in the research gaps and problems section. The future work of this study is based on the research gaps and problems recognized from present research schemes. Additionally, JAVA software language is widely utilized for implementing privacy preservation models, and the Amazon access sample database is a commonly employed dataset for the privacy preservation approach.
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Terzi, Anastasia, and Stamatia Bibi. "Opening Software Research Data 5Ws+1H." Software 3, no. 4 (2024): 411–41. http://dx.doi.org/10.3390/software3040021.
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Open Science describes the movement of making any research artifact available to the public, fostering sharing and collaboration. While sharing the source code is a popular Open Science practice in software research and development, there is still a lot of work to be done to achieve the openness of the whole research and development cycle from the conception to the preservation phase. In this direction, the software engineering community faces significant challenges in adopting open science practices due to the complexity of the data, the heterogeneity of the development environments and the diversity of the application domains. In this paper, through the discussion of the 5Ws+1H (Why, Who, What, When, Where, and How) questions that are referred to as the Kipling’s framework, we aim to provide a structured guideline to motivate and assist the software engineering community on the journey to data openness. Also, we demonstrate the practical application of these guidelines through a use case on opening research data.
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Soejono, R. P. "Archaeological Research in Indonesia." Journal of Southeast Asian Studies 18, no. 2 (1987): 212–16. http://dx.doi.org/10.1017/s0022463400020518.
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Irawan, Dasapta, and Cut Rachmi. "Promoting data sharing among Indonesian scientists: A proposal of generic university-level Research Data Management Plan (RDMP)." Research Ideas and Outcomes 4 (July 6, 2018): e28163. https://doi.org/10.3897/rio.4.e28163.
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Every researcher needs data in their working ecosystem, but despite of the resources (funding, time, and energy), that they have spent to get the data, only a few are putting more real attention to data management. This paper is mainly describing our recommendation of RDMP document at university level. This paper would be a form of our initiative to be developed at university or national level, which also in-line with current development in scientific practices mandating data sharing and data re-use. Researchers can use this article as an assessment form to describe the setting of their research and data management. Researcher can also develop more detail RDMP to cater specific project's environment. In this Research Data Management Plan (RDMP), we propose three levels of storage: offline working storage, offline backup storage and online-cloud backup storage, located on a shared-repository. We also propose two kinds of cloud repository: a dynamic repository to store live data and a static repository to keep a copy of final data. Hopefully, this RDMP could solve problems on data sharing and preservation, and additionally could increase researchers' awareness about data management to increase the value and impact of their researches.
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Kramer, Whitney. "Review of 3D Data Creation to Curation: Community Standards for 3D Data Preservation." Journal of New Librarianship 8, no. 1 (2023): 60–63. http://dx.doi.org/10.33011/newlibs/13/6.
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Review of Moore, Jennifer, Adam Rountrey, and Hannah Scates Kettler, eds., 3D Data Creation to Curation: Community Standards for 3D Data Preservation. Chicago, Illinois: Association of College and Research Libraries, 2022.
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Ambar Wati, Anindya, and Rahmi Rahmi. "LAM (Libraries, Archives, Museums) dalam preservasi pengetahuan." Berkala Ilmu Perpustakaan dan Informasi 17, no. 2 (2021): 181–94. http://dx.doi.org/10.22146/bip.v17i2.1722.
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Introduction. This paper aims to determine the collaboration of LAM (Libraries, Archives, Museums) in implementing knowledge preservation. 
 Methods. A systematic review with a qualitative approach was used, and the articles were selected from Google Scholar.
 Data Analysis. The data collection method consists of 6 (six) stages, 1) mapping the field through scoping review, 2) comprehensive search, 3) quality assessment, 4) data extraction, 5) synthesis, and 6) write-up.
 Results and Discussion. The results indicated that the selected journal articles covered collaborations and cooperations that have been established by LAM in the country and overseas. The strategy used was based on the objective of preventive and curative preservations of knowledge. Human resources were found as a main challenge for implementing knowledge preservation by LAM. 
 Conclusion. Based on the findings, research in Indonesia has led to various collaborations between LAM in implementing knowledge preservation from both preventive preservation and curative approaches.
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Dr., Shashidhar V., Kulkarni Sutej, B. Karia Suraj, Shetty Pranya, and M. H. Samyak. "Differential Privacy in Data Anonymization." International Journal of Multidisciplinary Research Transactions 6, no. 8 (2024): 26–40. https://doi.org/10.5281/zenodo.13371875.
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This study explores an effective approach to anonymizing sensitive data, particularly focusing on diabetes-related information, through the application of Laplace noise addition. The primary objective is to protect individual privacy while preserving the utility and integrity of the dataset, especially non-numeric values. The research involves adding Laplace noise to the "Age" column to anonymize the data, subsequently evaluating the error and loss metrics by comparing the original and anonymized datasets. Key statistical measures, including mean, median, and standard deviation, are computed and contrasted between the two datasets to assess the impact of the anonymization process. Additionally, the study employs visual tools such as data distribution plots and boxplots to illustrate the differences and similarities between the original and anonymized data. The findings highlight the balance achieved between data privacy and utility, demonstrating the practical implications of privacy-preserving techniques in managing sensitive health information. The anonymized dataset is ultimately saved, showcasing a robust method for protecting privacy in sensitive datasets while maintaining their analytical value.
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Achar, Sandesh. "Data Privacy-Preservation: A Method of Machine Learning." ABC Journal of Advanced Research 7, no. 2 (2018): 123–29. http://dx.doi.org/10.18034/abcjar.v7i2.654.
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The privacy-preservation field in cyber security tends to affiliate with the protection measure related to the use of data and its sharing via third parties for activities such as data analysis. The paper's main objective for this research article will be to use machine learning models that tend to aid as a privacy-preservation technique (PPT). The augmentation of machine learning as a technique for privacy preservation has been able to address the challenges facing the current field of cyber security concerning data protection and security. The paper summarizes the methods such as "federated learning" to address the current issue in the network security field relating to data protection. The rise of augmentation of machine learning in privacy preservation is due to the development of cloud-based applications that are usually prone to data protection issues. Thus, the result of machine learning was necessary to counteract data insecurity. However, the use of machine learning in privacy preservation has remained proficient; there still needs to be a literature gap between the theory and the application of machine learning.
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Grant, Rebecca. "Recordkeeping and research data management: a review of perspectives." Records Management Journal 27, no. 2 (2017): 159–74. http://dx.doi.org/10.1108/rmj-10-2016-0036.
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Purpose The purpose of this paper is to explore a range of perspectives on the relationship between research data and records and between recordkeeping and research data management. Design/methodology/approach This paper discusses literature in the field of research data management as part of preliminary work for the author’s doctoral research on the topic. The literature included in the review reflects contemporary and historical perspectives on the management and preservation of research data. Findings Preliminary findings indicate that records professionals have been involved in the management and preservation of research data since the early twentieth century. In the literature, research data is described as comparable to records, and records professionals are widely acknowledged to have skills and expertise which are applicable to research data management. Records professionals are one of a number of professions addressing research data management. However, they are not currently considered to be leaders in research data management practice. Originality/value Research data management is an emerging challenge as stakeholders in the research lifecycle increasingly mandate the publication of open, transparent research. Recent developments such as the publication of the OCLC report “The Archival Advantage: Integrating Archival Expertise into Management of Born-digital Library Materials”, and the creation of the Research Data Alliance Interest Group Archives and Records Professionals for Research Data indicates that research data is, or can be, within the remit of records professionals. This paper represents a snapshot of contemporary and historical attitudes towards research data and recordkeeping and thus contributes to this emerging area of discussion.
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Shu, Guocheng, Yuzhou Gong, Feng Xie, Nicholas C. Wu, and Cheng Li. "Effects of long-term preservation on amphibian body conditions: implications for historical morphological research." PeerJ 5 (September 15, 2017): e3805. http://dx.doi.org/10.7717/peerj.3805.
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Measurements of historical specimens are widely applied in studies of taxonomy, systematics, and ecology, but biologists often assume that the effects of preservative chemicals on the morphology of amphibian specimens are minimal in their analyses. We compared the body length and body mass of 182 samples of 13 live and preserved (up to 10 years) anuran species and found that the body length and body mass of preserved specimens significantly decreased by 6.1% and 24.8%, respectively, compared to those measurements of their live counterparts. The changes in body length and mass also exhibited highly significant variations between species. Similarly, there were significant differences in shrinkage of body length and body mass between sexes, where males showed greater shrinkage in body length and body mass compared to females. Preservation distorted the magnitude of the interspecific differences in body length observed in the fresh specimens. Overall, the reduction in body length or mass was greater in longer or heavier individuals. Due to the effects of preservation on amphibian morphology, we propose two parsimonious conversion equations to back-calculate the original body length and body mass of studied anurans for researchers working with historical data, since morphological data from preserved specimens may lead to incorrect biological interpretations when comparing to fresh specimens. Therefore, researchers should correct for errors due to preservation effects that may lead to the misinterpretation of results.
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C. Dearborn, Carly, Amy J. Barton, and Neal A. Harmeyer. "The Purdue University Research Repository." OCLC Systems & Services 30, no. 1 (2014): 15–27. http://dx.doi.org/10.1108/oclc-07-2013-0022.
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Purpose – The purpose of this case study is to discuss the creation of robust preservation functionality within PURR. The study seeks to discuss the customization of the HUBzero platform, composition of digital preservation policies, and the creation of a novel, machine-actionable metadata model for PURR's unique digital content. Additionally, the study will trace the implementation of the Open Archival Information System (OAIS) model and track PURR's progress towards Trustworthy Digital Repository certification. Design/methodology/approach – This case study discusses the use of the Center for Research Libraries Trusted Repository Audit Checklist (TRAC) certification process and ISO 16363 as a rubric to build an OAIS institutional repository for the publication, preservation, and description of unique datasets. Findings – ISO 16363 continues to serve as a rubric, barometer and set of goals for PURR as development continues. To become a trustworthy repository, the PURR project team has consistently worked to build a robust, secure, and long-term home for collaborative research. In order to fulfill its mandate, the project team constructed policies, strategies, and activities designed to guide a systematic digital preservation environment. PURR expects to undertake the full ISO 16363 audit process at a future date in expectation of being certified as a Trustworthy Digital Repository. Through its efforts in digital preservation, the Purdue University Research Repository expects to better serve Purdue researchers, their collaborators, and move scholarly research efforts forward world-wide. Originality/value – PURR is a customized instance of HUBzero®, an open source software platform that supports scientific discovery, learning, and collaboration. HUBzero was a research project funded by the United States National Science Foundation (NSF) and is a product of the Network for Computation Nanotechnology (NCN), a multi-university initiative of eight member institutions. PURR is only one instance of a HUBzero's customization; versions have been implemented in many disciplines nation-wide. PURR maintains the core functionality of HUBzero, but has been modified to publish datasets and to support their preservation. Long-term access to published data are an essential component of PURR services and Purdue University Libraries' mission. Preservation in PURR is not only vital to the Purdue University research community, but to the larger digital preservation issues surrounding dynamic datasets and their long-term usability.
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Mohr, Alicia Hofelich, Josh Bishoff, Carolyn Bishoff, Steven Braun, Christine Storino, and Lisa R. Johnston. "When Data Is a Dirty Word: A Survey to Understand Data Management Needs Across Diverse Research Disciplines." Bulletin of the Association for Information Science and Technology 42, no. 1 (2015): 51–53. http://dx.doi.org/10.1002/bul2.2015.1720420114.
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EDITOR'S SUMMARYPrompted by federal mandates regarding data management planning, researchers sought to understand the needs and expectations of faculty at the University of Minnesota Twin Cities for managing their data. An initial survey revealed weak identification with the word data. Researchers then customized surveys for four colleges, using terminology that would resonate best for each. Survey results showed a distinction between those who identify the primary output of their work as data vs. research materials. Among those in the College of Food, Agriculture and Natural Resource Sciences, 91% responded they generate data, along with 65% of health center researchers, 68% of College of Science and Engineering respondents and 54% of those from the College of Liberal Arts. Those whose focus was data wanted support with long term data preservation and preparing for sharing materials. Respondents who stated they produce research materials rather than data indicated different needs, though help with long term preservation and better access to resources to support data management were common themes.
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Mosha, Neema Florence, and Patrick Ngulube. "Metadata Standard for Continuous Preservation, Discovery, and Reuse of Research Data in Repositories by Higher Education Institutions: A Systematic Review." Information 14, no. 8 (2023): 427. http://dx.doi.org/10.3390/info14080427.
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This systematic review synthesised existing research papers that explore the available metadata standards to enable researchers to preserve, discover, and reuse research data in repositories. This review provides a broad overview of certain aspects that must be taken into consideration when creating and assessing metadata standards to enhance research data preservation discoverability and reusability strategies. Research papers on metadata standards, research data preservation, discovery and reuse, and repositories published between January 2003 and April 2023 were reviewed from a total of five databases. The review retrieved 1597 papers, and 13 papers were selected in this review. We revealed 13 research articles that explained the creation and application of metadata standards to enhance preservation, discovery, and reuse of research data in repositories. Among them, eight presented the three main types of metadata, descriptive, structural, and administrative, to enable the preservation of research data in data repositories. We noted limited evidence on how these metadata standards can be used to enhance the discovery and reuse of research data in repositories to enable the preservation, discovery, and reuse of research data in repositories. No reviews indicated specific higher education institutions employing metadata standards for the research data created by their researchers. Repository designs and a lack of expertise and technology know-how were among the challenges identified from the reviewed papers. The review has the potential to influence professional practice and decision-making by stakeholders, including researchers, students, librarians, information communication technologists, data managers, private and public organisations, intermediaries, research institutions, and non-profit organizations.