Zeitschriftenartikel: „Data base research“

1

Stonebraker, Michael. "Data base research at Berkeley." ACM SIGMOD Record 19, no. 4 (December 1990): 113–18. http://dx.doi.org/10.1145/122058.122072.

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2

Radach, G., J. Gekeler, G. Becker, P. Bot, P. Castaing, F. Colijn, P. Damm, et al. "The NOWESP research data base." Deutsche Hydrographische Zeitschrift 48, no. 3-4 (September 1996): 241–59. http://dx.doi.org/10.1007/bf02799374.

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3

Kareiva, Peter. "Paleobiology data base catalyzes research breakthroughs." Trends in Ecology & Evolution 16, no. 11 (November 2001): 605. http://dx.doi.org/10.1016/s0169-5347(01)02346-1.

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4

Wu, Xiang Min, Cheng Lin Zhao, and Pan Cao. "Research of Data Base and Data Mining in CRM." Applied Mechanics and Materials 543-547 (March 2014): 2988–91. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2988.

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Customer Relationship Management (CRM) is becoming the focus of enterprise and an active research field of computer science. The ariticle introduces some basic concepts about CRM and data mining, and some benefits brought by data mining in CRM. At the end it points out how to apply data mining applications in CRM.

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Dungworth, Terence. "The Federal Court Data Base: New Research Opportunities." IASSIST Quarterly 12, no. 1 (July 31, 1988): 3. http://dx.doi.org/10.29173/iq266.

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6

Tate, C. Neal. "Microcomputer Data Base Programs in Social Research." Evaluation Review 10, no. 5 (October 1986): 682–93. http://dx.doi.org/10.1177/0193841x8601000507.

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7

Marshall, Fray F., Andrew K. Stewart, and Herman R. Menck. "The national cancer data base." Cancer 80, no. 11 (December 1, 1997): 2167–74. http://dx.doi.org/10.1002/(sici)1097-0142(19971201)80:11<2167::aid-cncr18>3.0.co;2-x.

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8

Zhang, Mei, Huan Liu, and Jinghua Wen. "E-Commerce Security Research in Big Data Environment." International Journal of Enterprise Information Systems 14, no. 1 (January 2018): 63–76. http://dx.doi.org/10.4018/ijeis.2018010104.

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Rapid development of e-commerce and mobile communication opens a new era of big data. In this article, the authors put big data and e-commerce security together. They construct electronic commerce security system from these aspects: the creation of database, the security of information storage, the mining of information based on big data environment thoroughly. The second-generation product distributed platform- Apache Hadoop which is more popular and instant has been brought in. what's more, this article expounds the structure and working process. On the base of this platform, this article analyses the certainty and security of e-commerce transactions data developed on the condition of big data. It puts forward a construction view that people should guide and monitor the behavior of e-commerce, and improve the security system of electronic commerce on the base of data.

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Jakobsson, Olafur P. "Computer Data Base for Cleft Lip and Palate Research." Scandinavian Journal of Plastic and Reconstructive Surgery and Hand Surgery 24, no. 1 (January 1990): 47–51. http://dx.doi.org/10.3109/02844319009004520.

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10

Research Committee on Spring Materi. "Report of Research Committee on Spring Material Data Base." Transactions of Japan Society of Spring Engineers, no. 55 (2010): 63–74. http://dx.doi.org/10.5346/trbane.2010.63.

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11

Vorster, Jan. "Paraphrasing: Extending the data base for child language research." Language Sciences 10, no. 1 (January 1988): 173–92. http://dx.doi.org/10.1016/0388-0001(88)90011-3.

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12

Palma, David A. "National Cancer Data Base: An Important Research Tool, but Not Population-Based." Journal of Clinical Oncology 35, no. 5 (February 10, 2017): 571. http://dx.doi.org/10.1200/jco.2016.69.2855.

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13

Kotarski, Rachael, and Torsten Reimer. "Making Everything Available. British Library Research Services and Research Data Strategy." International Journal of Digital Curation 13, no. 1 (December 27, 2018): 161–69. http://dx.doi.org/10.2218/ijdc.v13i1.605.

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The way that researchers generate, analyse and share information keeps evolving at a rapid pace. To ensure that it is well equipped to serve its global user base for years to come, the British Library is transforming the way it works too, from the physical buildings to its digital service portfolio. One key programme, Everything Available, will ensure the Library’s continued support for research with services to enable access to information in an open and timely manner. This paper will describe the activities planned within Everything Available, with a particular focus on the aims of the Library’s recently refreshed Research Data Strategy. It will give an insight into the challenges and opportunities faced by a National Library in providing relevant services in an ‘open’ world.

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Yang, Chun Yan, and Zhi Ming Li. "Recent Research Progress on Extension Data Mining Methods." Applied Mechanics and Materials 263-266 (December 2012): 303–11. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.303.

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This paper is a summary about recent research progress that describes the basic methods utilizing basic theory and methods of Extenics to mine the knowledge based on transformations in various fields’ database and knowledge base (“extension knowledge” for short) . It’s shown by studies that the existing data mining theory and technology will be developed. These methods can be applied to the fields of marketing, customer relationship management, finance and securities, telecommunication, and medical treatment, etc., which will provide effective decision supports to solve the contradictory problems in these fields.

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Paas, Leo. "Marketing research education in the Big Data era." International Journal of Market Research 61, no. 3 (March 15, 2019): 233–35. http://dx.doi.org/10.1177/1470785319825535.

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The excellent and timely Nunan and Di Domenico International Journal of Market Research (IJMR) paper addressed the stagnant state of many marketing research courses. Following up on this contribution, I will propose some specific changes for enhancing relevance of marketing research curricula, emphasizing transactional data and Big Data. Moreover, I will propose that marketing research education should develop analysts who can convert data into advice for decision makers and we should educate marketing managers to become more quantitatively oriented to ensure they can base their decisions on data and analytical results.

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MILLER, K. J., and D. F. SOCIE. "AN INDEX AND DATA-BASE FOR FATIGUE AND FRACTURE RESEARCH." Fatigue & Fracture of Engineering Materials & Structures 20, no. 3 (March 1997): 257–58. http://dx.doi.org/10.1111/j.1460-2695.1997.tb01550.x.

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17

Cohen, Michael E., Patricia K. Duffner, Lawrence E. Kun, and Bernard D'Souza. "The argument for a combined cancer consortium research data base." Cancer 56, S7 (October 1, 1985): 1897–901. http://dx.doi.org/10.1002/1097-0142(19851001)56:7+<1897::aid-cncr2820561334>3.0.co;2-s.

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18

Meng, Xue Jun. "Some Research about Technology of Disaster Tolerance for Data Center." Advanced Materials Research 268-270 (July 2011): 482–88. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.482.

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Along with Development of digital campus, much key application system run on base of digital campus, usability of application system, integrality and confidentiality of application data arouse much recognition of university gradually. The paper gives introduction and analyzing from some key indexes, technology frame etc. and offer reference about construct of Disaster Tolerance system.

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Aas, I. H. Monrad. "Quality of Hospital Data and DRGs." Scandinavian Journal of Social Medicine 16, no. 4 (December 1988): 223–26. http://dx.doi.org/10.1177/140349488801600406.

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Research on and application of DRG is frequently performed with the aid of computerized data. This study has been made to assess the importance of errors in hospital data bases for DRG research, and the financial consequences if DRG is used as a reimbursement system. For the same hospital stays information on the DRG grouping variables has been collected from medical records and a data base. Frequencies of errors in the data base for the DRG grouping variables (principal diagnosis, secondary diagnosis, complications, procedures, age and discharge status of death) and length of stay are given. DRG grouping is performed on the basis of both medical record and data base information. The analysis shows the DRG system to be robust towards errors in the data bases. The procedure used in many countries for testing the US DRG definitions with national data found in data bases is also considered to be robust towards errors. Data quality in the studied data base is sufficient for DRG research purposes. The factors giving the robustness are of a general nature and may also apply to other data bases. Quality of computerized data is a less critical factor for DRG than for other applications of such data. If the present data base had been used in financial connection the errors would have had almost no deteriorating effect on hospital incomes.

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Brin, Lauren, Mary Warlaumont, Timothy Fuller, and Peter T. Silberstein. "A National Cancer Data Base report on mesothelioma." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): e18501-e18501. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e18501.

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e18501 Background: This is the first report on mesothelioma using Commission on Cancer National Cancer Database information and this is the largest study on mesothelioma to date. Methods: Using the National Cancer Database (NCDB) 26,605 patients diagnosed with mesothelioma between 2000 and 2010 were identified and analyzed. The NCDB contains data from more than 70% of all new cancers diagnosed in the U.S. each year. Results: The majority of mesothelioma patients are Caucasian (89%), males (78%), over the age of 70 (60%), and are diagnosed with stage IV disease (41%). The majority of patients (41%) received no first course treatment. The patients that were most likely to have surgery include patients <70 years old, at academic centers, with stage I-III disease, and of these only 37% received surgery and 15% received no first course treatment. The youngest patients <50 years received the most surgical therapy (36%) and the oldest patients >80 received the least (12%). Patients with private insurance received more surgical treatment (28%) than patients with Medicaid (22%), Medicare (18%), and VHA (12%)(p<0.009). Private insurance holders also received no first course treatment less often (29.6%) than Medicaid (37%), Medicare (46%), VHA (52%), and uninsured patients (37%)(p<0.002). Conclusions: The majority of patients with mesothelioma are elderly, Caucasian males and many of which do not receive treatment. The standard of care for resectable stage I-III mesothelioma includes surgery and chemotherapy. The results from this study indicate that 41% of all patients received no first course treatment and only 21% of patients received surgical treatment. Of patients most likely to receive surgery, only 37% of patients received surgical treatment and 15% of these patients did not receive any first course treatment. Age and insurance status also affect treatment as the youngest patients and private insurance holders received the most surgical treatment. [Table: see text]

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Urist, Marshall M., and Lucy Hynds Karnell. "The national cancer data base. Report on melanoma." Cancer 74, no. 2 (July 15, 1994): 782–88. http://dx.doi.org/10.1002/1097-0142(19940715)74:2<782::aid-cncr2820740236>3.0.co;2-g.

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Daly, John M., Lucy H. Karnell, and Herman R. Menck. "National Cancer Data Base report on esophageal carcinoma." Cancer 78, no. 8 (October 15, 1996): 1820–28. http://dx.doi.org/10.1002/(sici)1097-0142(19961015)78:8<1820::aid-cncr25>3.0.co;2-z.

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23

Koltay, Tibor. "Data governance, data literacy and the management of data quality." IFLA Journal 42, no. 4 (November 30, 2016): 303–12. http://dx.doi.org/10.1177/0340035216672238.

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Data governance and data literacy are two important building blocks in the knowledge base of information professionals involved in supporting data-intensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data.

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Greco, A. O., C. M. Licavoli, L. A. White, J. R. Eckardt, K. O. Easley, and N. N. Sjak-Shie. "Multiple uses for a clinical research database." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 17037. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.17037.

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17037 Background: In Sept 2000, the research staff at The Center for Cancer Care and Research (TCCCR) developed an excel data base to track new consults referred to the practice. It is used to identify pts for participation in Cooperative Group Clinical Trials and to identify gaps in the active protocol list. Several additional uses for the data base have evolved. Methods: Medical records provided by referring physicians for each new consult are evaluated by a Research Coordinator. Information including the pt's name, date of visit, physician, referring physician, diagnosis, protocol for which the pt is evaluated, and eligibility information is entered in the data base. Results: The data base provides a method by which we can follow pts through the protocol selection and informed consent process. Early on, the data base identified a site need for trials in metastatic breast cancer prompting us to search other sources such as the CTSU and industry. Additionally, the percentage of new consults actually enrolled on a clinical trial can be determined as well as tracking eligibility/ineligibility trends. The Pharmaceutical Research Dept can use the information to complete feasibility studies prior to participating in industry trials. The data base can be used to evaluate trends in referral patterns and has helped identify referring physicians who support our research efforts. In 2006, TCCCR had a protocol available for 45% of new consults with a cancer dx. Of those pts, 16% enrolled on a Cooperative Group Clinical Trial. According to published evaluations, 16% is well above the national average of pts who participate in oncology clinical trials. It is our assessment that TCCCR's success is due in part to the data base. Conclusions: It is well known that in order to improve treatment outcomes and diminish treatment toxicity, oncology practices and pts must participate in clinical research. It is also well known that the numbers of pts who participate in oncology clinical trials is dismal. This data base has become a valuable tool providing a method to identify and evaluate some of the reasons why pts do not enroll in clinical trials and given our practice guidance to increase pt participation. Our next goal is to evaluate the differences between the rural and urban population at TCCCR's two sites to identify additional trends in clinical trial participation. No significant financial relationships to disclose.

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Buiatti, Eva, Emanuele Crocetti, Ettore Conti, Fabio Falcini, and Lorenzo Gafà. "Comparability Issues within the Itacare Data Base." Tumori Journal 83, no. 1 (January 1997): 25–32. http://dx.doi.org/10.1177/030089169708300107.

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Aims and background The aim of the study was to describe the extent of variability among Italian cancer registries in data managing practices that may affect differences in incidence and possibly in survival estimates. Methods a self-administered questionnaire was sent to each participating registry. The definitions of the disease, of the start point and of the end point of survival computation were investigated. Moreover, information on the proportion of histologic confirmation, of ill-defined sites and of DCO (death certificate only) was also considered. Results There were some differences in cancer registration techniques among Italian cancer registries. As regards disease definition, the most relevant problems arose for urinary bladder. Skin melanoma should also be considered with some caution, due to variability among registries in coding in situ cases. For the CNS and meninges, the proportion of cases that could be differently considered was so limited that no effect on survival is expected. For female breast, colorectum and cervix uteri, the effect of early diagnosis services (which are active only in some areas) may lead to better survival estimates. The variability in incidence date definition was high among registries and sites, but its effect on survival was very limited. There was a wide variability in the proportion of DCOs and of DCIs (initially known from death certificate), which should be considered in survival comparisons. All the registries stated that they carried out an active follow-up of their patients. Conclusions In general, quality standards of the registries are good and allow comparability of survival data. The variability of rules adopted by Italian registries may affect geographic survival differences only in a limited number of cancer sites, so that results should be interpreted with caution.

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Hintze, Bradley, Huiqing Zhou, Hashim Al-Hashimi, David Richardson, and Jane Richardson. "Hidden Hoogsteens in the Data." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1508. http://dx.doi.org/10.1107/s2053273314084915.

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Although extremely rare in the crystallographic database, Hoogsteen (HG) base pairs have been modeled, especially at duplex termini, protein-DNA interfaces, damaged DNA and DNA mismatches. Formation of HG base pairs in duplex DNA requires a 180 degree rotation along the glycosidic bond of the purine relative to the canonical Watson-Crick (WC) base-pair. In a recent survey of the PDB we identified 75 HS base pairs in 2,910 x-ray derived models. Recent evidence suggests that the prevalence of HG base pairs in nature to be greater than previously thought. Due to electron density density ambiguity at mid to low resolution, it is possible for bases to be modeled incorrectly. We have developed a high-throughput method which examines the difference density around DNA to identify mis-modeled bases. Here we show where bases were incorrectly modeled and our subsequent fixes which show pairs modeled as WC that really are HG, vice versa, and evidence of WC-HG alternates. This research shows that base pairing conformations in DNA are more heterogeneous than previously thought.

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Denikin, Andrey, Alexander Karpov, Mikhail Naumenko, Vladimir Rachkov, Viacheslav Samarin, and Vycheslav Saiko. "Synergy of Nuclear Data and Nuclear Theory Online." EPJ Web of Conferences 239 (2020): 03021. http://dx.doi.org/10.1051/epjconf/202023903021.

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The paper describes the NRV web knowledge base on low-energy nuclear physics developed in the Joint Institute for Nuclear Research. The NRV knowledge base working through the Internet integrates a large amount of digitized experimental data on the properties of nuclei and nuclear reaction cross sections with a wide range of computational programs for modeling of nuclear properties and nuclear dynamics. Today, the NRV becomes a powerful instrument for nuclear physics research as well as for educational applications. Advantages of the functioning scheme of the knowledge base provide the synergy of coexistence of the experimental data and computational codes within one platform.

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Beals, Diane E. "Computer Networks as a New Data Base." Journal of Educational Computing Research 8, no. 3 (August 1992): 327–45. http://dx.doi.org/10.2190/e0l5-vu5t-w3g3-8pqh.

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Because most communication on computer networks is stored on disk, a permanent record of all conversations can be reconstructed. Data collection and transcription, typically one of the most difficult problems in psychological and linguistic research, is greatly simplified. This record of communication provides a new data source for psychological, social, and linguistic research. This article outlines the features of computer communication that make networks a rich data base for research. Examples of findings are reported from three different studies of one computer network. These include the existence of a greater rate of response to personal event narratives than to more general descriptions, the effect of members' purposes and social roles on the style of communication, and a greater understanding of the needs of beginning teachers.

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Sheng, Jingyuan. "Research on SQL Injection Attack and Defense Technology of Power Dispatching Data Network: Based on Data Mining." Mobile Information Systems 2022 (July 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/6207275.

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In the process of SQL injection attack and defense of power dispatching data network, in order to ensure the accuracy of identification and defense, it is often necessary to build a rule base. However, the scale of the temporarily constructed rule base is limited, which is easy to cause false positives and omissions. Based on the traditional defense, data mining technology is introduced to design a SQL injection attack and defense technology for power dispatching data network. First, the SQL injection attack is analyzed, and the attack flow diagram is obtained as the basis of attack identification. Using the ITFIDF algorithm in data mining technology, combined with the dataset distribution diagram of conventional words and sensitive characters, the SQL injection attack is detected. Design the SQL injection attack early warning mechanism, establish the SQL injection attack defense model, and optimize the workflow of the defense model. The performance test results show that compared with the traditional defense technology, the proposed technology has certain advantages in false alarm rate and running time.

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Li, Qing, Wei Yang, Xiao Nan Ye, and Xiao Xiao Ma. "Research on Knowledge Base of Device Test Training System Based on Rough Set Data Mining." Advanced Materials Research 645 (January 2013): 232–38. http://dx.doi.org/10.4028/www.scientific.net/amr.645.232.

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The realization of a device test training system requires the use of a lot of domain knowledge, and building knowledge base will play an important role. In view of the uncertainty, inaccuracy and incompleteness of test data in the testing process, this paper makes the data mining algorithms based on rough set as knowledge acquisition algorithm, and proposes an improved algorithm for insufficient of approximate reduction of rough set knowledge based on the tolerance relation of incomplete information system. The paper studies the design and realization of knowledge base system in the developing of device simulation training system on this basis, and validates the method through a design example of knowledge base of a certain device simulation training system.

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Starik, Mark. "Using and Improving the Kld Data Base for Research and Teaching." Proceedings of the International Association for Business and Society 4 (1993): 1113–20. http://dx.doi.org/10.5840/iabsproc1993494.

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KOBAYASHI, Hideo. "Importance and Expectation of Research Information Data Base by Government Researcher." JOURNAL OF THE JAPAN WELDING SOCIETY 77, no. 1 (2008): 25–28. http://dx.doi.org/10.2207/jjws.77.25.

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Греков, Иван Михайлович, Дмитрий Александрович Субетто, Ivan Grekov, and Dmitry Subetto. "Application data base paleogeographic Kola peninsula «Q-KOLA» in paleolimnological research." Proceedings of the Karelian Research Centre of the Russian Academy of Sciences, no. 5 (June 14, 2015): 48. http://dx.doi.org/10.17076/lim50.

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Jia, Fengjiao, Shaohua Jiang, Zaoyang Wu, and Yigao Liang. "Research on LncRNA and Disease Associations Prediction Base on Data Mining." Journal of Physics: Conference Series 2219, no. 1 (April 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2219/1/012029.

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Abstract The accumulated experiments show that lncRNA has a role in biophysiological and case processes. Prediction of the relationship between diseases and lncRNA will contribute to clarify the etiology of diseases, develop new drugs and treat complex diseases. However, the traditional biological experiment method has long experiment period and high cost. Therefore, based on existing biological data and biological experimental data, data mining techniques have been used to propose many models and methods to predict the lncRNA-disease correlation. This article will provide a specific introduction to lncRNA and disease-related databases, which summarizes some relevant predictions classical models (matrix factorization, heterogeneous networks, machine learning). At the end of the article, the problems of lncRNA and the current prediction of the disease are analyzed, and some ideas and help are provided for later researchers.

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Hope, Dave. "Building an Enterprise Livestock Data Base for Extension and Research Use." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 37, no. 4 (November 13, 2008): 591–95. http://dx.doi.org/10.1111/j.1744-7976.1989.tb00779.x.

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Girija, N., and S. K. Srivatsa. "A Research Study: Using Data Mining in Knowledge Base Business Strategies." Information Technology Journal 5, no. 3 (April 15, 2006): 590–600. http://dx.doi.org/10.3923/itj.2006.590.600.

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Solheid, Peter, Catherine Constable, Anthony Koppers, and Subir Banerjee. "Research-oriented data base for rock and paleomagnetism to be developed." Eos, Transactions American Geophysical Union 83, no. 48 (2002): 560. http://dx.doi.org/10.1029/2002eo000389.

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Kam, Cindy D., Jennifer R. Wilking, and Elizabeth J. Zechmeister. "Beyond the “Narrow Data Base”: Another Convenience Sample for Experimental Research." Political Behavior 29, no. 4 (July 19, 2007): 415–40. http://dx.doi.org/10.1007/s11109-007-9037-6.

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Irving, J. M., and I. K. Crombie. "The application of microcomputer data-base management packages in medical research." Journal of Microcomputer Applications 10, no. 3 (July 1987): 211–18. http://dx.doi.org/10.1016/0745-7138(87)90014-5.

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Powers, Bernard J. "A clinical research data base system for the Apple IIe/IIc." Computers in Biology and Medicine 16, no. 5 (January 1986): 391–98. http://dx.doi.org/10.1016/0010-4825(86)90006-5.

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Tokarev, A. "Big Data Applications in Social Media Research: The Experience of Unsuccessful Data Analysis of Ukrainian Facebook." Journal of International Analytics, no. 2 (June 28, 2018): 94–105. http://dx.doi.org/10.46272/2587-8476-2018-0-2-94-105.

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This article showcases a detailed description of the first stage of research on the discourse of Ukrainian opinion leaders on Facebook conducted by a team of researchers representing MGIMO University, Lomonosov Moscow State University, and Institute of Economy at the Russian Academy of Sciences. Convinced that it is Facebook that serves as the primary means of communication of politicians with the population in Ukraine, the team built a data base consisting of posts written over a 10-month period by 176 profiles belonging to the representatives of Ukrainian elites, and applied machine data analysis. The research question was the following: What strategies on the conflict in Donbas are verbalized by the Ukrainian elites? The author faced three challenges and limitations of machine data processing and analysis:unsuccessful operationalization of terms; functional limitations of the Semantic Archive Platform, of which the author turned out to have unreasonably high expectations; lack of understanding of peculiarities of Big Data analysis. Nevertheless, it was the failure of this pilot research that helped raise crucial questions for further research, primarily on the criteria for shaping a data base and on formulating of the research questions for software. This experience turned to be essential for second and third stages of the research project that were completed a year and half after the project was launched. Hence the necessity to make public all the considerations on this research.

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Polatajko, Helen, Marilyn Ernest, and Joyce MacKinnon. "Computerized Fieldwork Data Base: Applications and Implications." Canadian Journal of Occupational Therapy 54, no. 5 (December 1987): 263–67. http://dx.doi.org/10.1177/000841748705400509.

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Fieldwork placement is a complicated and involved task. On the one hand, it requires insightful, professional judgement, but on the other hand, it includes numerous routine administrative and clerical tasks. To deal with fieldwork placement more efficiently and effectively it was decided to computerize the fieldwork placement system at the University of Western Ontario, London, Ontario. This paper describes the system developed and the resultant data base. The potential applications of the data base and the implications for students, facilities, educational programs, professional governing bodies and research are discussed.

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Csáki, Csaba, and Andrea Kὄ. "Revisiting open data research through the Lens of the Data Value Chain." Central and Eastern European eDem and eGov Days 331 (July 12, 2018): 205–17. http://dx.doi.org/10.24989/ocg.v331.17.

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While the idea of open government data is not new, there appears to be a constant shift in leading research objectives guiding the field. This is because the reasons behind the increased research interest keep changing. The latest motivation stems from economic arguments, namely the reuse of public sector information) – which in turn creates a market for open-data based value added services. Although over the last decade many research topics have been identified and various research agendas have been proposed, most of them either focus on specific areas or are rooted in the popular approach of the time. Using the idea of the data value chain, this paper provides an integrated view of open government data research – which then allows a systematic and consistent identification of research topics and clarification of corresponding open questions in the area of open data quality. Research areas proposed are context/environment (policy and regulations), supply side (government organizations), consumer side (service providers and users), (societal or economic) impact, and technology (supporting the DSC, including the data). In addition – and what is regularly missing from most reviews – theory (providing definitions and a sound base) is considered as the sixth area.

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Aldrich, Tim, Wendy Demark-Wahnefried, Joellen M. Schildkraut, Eugene Lengerich, Elizabeth Conlisk, Curtis Mettlin, and Gerald P. Murphy. "The national cancer data base report on prostate cancer." Cancer 75, no. 1 (January 1, 1995): 136–38. http://dx.doi.org/10.1002/1097-0142(19950101)75:1<136::aid-cncr2820750126>3.0.co;2-3.

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Lawrence, Walter, Herman R. Menck, Glenn D. Steele, and David P. Winchester. "The national cancer data base report on gastric cancer." Cancer 75, no. 7 (April 1, 1995): 1734–44. http://dx.doi.org/10.1002/1097-0142(19950401)75:7<1734::aid-cncr2820750729>3.0.co;2-e.

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Lev, Robert. "The national cancer data base report on colorectal cancer." Cancer 76, no. 3 (August 1, 1995): 538–39. http://dx.doi.org/10.1002/1097-0142(19950801)76:3<538::aid-cncr2820760333>3.0.co;2-5.

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Averette, Hervy E., Mike F. Janicek, and Herman R. Menck. "The national cancer data base report on ovarian cancer." Cancer 76, no. 6 (September 15, 1995): 1096–103. http://dx.doi.org/10.1002/1097-0142(19950915)76:6<1096::aid-cncr2820760626>3.0.co;2-4.

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Mettlin, Curtis J., Gerald P. Murphy, Lamar S. McGinnis, and Herman R. Menck. "The national cancer data base report on prostate cancer." Cancer 76, no. 6 (September 15, 1995): 1104–12. http://dx.doi.org/10.1002/1097-0142(19950915)76:6<1104::aid-cncr2820760627>3.0.co;2-d.

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Niederhuber, John E., Murray F. Brennan, and Herman R. Menck. "The national cancer data base report on pancreatic cancer." Cancer 76, no. 9 (November 1, 1995): 1671–77. http://dx.doi.org/10.1002/1097-0142(19951101)76:9<1671::aid-cncr2820760926>3.0.co;2-r.

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Fry, Willard A., Herman R. Menck, and David P. Winchester. "The National Cancer Data Base report on lung cancer." Cancer 77, no. 9 (May 1, 1996): 1947–55. http://dx.doi.org/10.1002/(sici)1097-0142(19960501)77:9<1947::aid-cncr27>3.0.co;2-z.

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