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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1848 1
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1880 1
1886 1
1888 1
1890 1
1893 1
1894 1
1895 1
1898 1
1899 2
1901 3
1902 1
1903 2
1905 1
1906 3
1908 2
1909 4
1910 2
1911 5
1912 4
1913 6
1914 2
1915 2
1916 8
1917 4
1918 2
1919 6
1920 16
1921 7
1922 7
1923 17
1924 18
1925 21
1926 20
1927 16
1928 25
1929 16
1930 21
1931 24
1932 17
1933 17
1934 8
1935 27
1936 26
1937 26
1938 25
1939 18
1940 11
1941 21
1942 35
1943 21
1944 33
1945 428
1946 1033
1947 702
1948 655
1949 573
1950 579
1951 1387
1952 1957
1953 1532
1954 1263
1955 1452
1956 1739
1957 1935
1958 1995
1959 2166
1960 2318
1961 2843
1962 2634
1963 1561
1964 1769
1965 1638
1966 1665
1967 2156
1968 2689
1969 2728
1970 2974
1971 2894
1972 2997
1973 3070
1974 3659
1975 10670
1976 11463
1977 10992
1978 12031
1979 13240
1980 14317
1981 15316
1982 17060
1983 20332
1984 22397
1985 23620
1986 25605
1987 29249
1988 36831
1989 47753
1990 56992
1991 65078
1992 72547
1993 79617
1994 87187
1995 93108
1996 91618
1997 85506
1998 90063
1999 95287
2000 103678
2001 107300
2002 111723
2003 120615
2004 132782
2005 147288
2006 161423
2007 172406
2008 183558
2009 193175
2010 209031
2011 230226
2012 252916
2013 267739
2014 281031
2015 290932
2016 286119
2017 295975
2018 318966
2019 344030
2020 393870
2021 432466
2022 427734
2023 405495
2024 454461
2025 452262
2026 1472
2027 2

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7,195,221 results

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Page 1
Showing results for data as
Search for Ata AS instead (3 results)
Toward data lakes as central building blocks for data management and analysis.
Wieder P, Nolte H. Wieder P, et al. Front Big Data. 2022 Aug 19;5:945720. doi: 10.3389/fdata.2022.945720. eCollection 2022. Front Big Data. 2022. PMID: 36072823 Free PMC article. Review.
Often associated with big data use cases, data lakes are, for example, used as central data management systems of research institutions or as the core entity of machine learning pipelines. The basic underlying idea of retaining data in its native forma …
Often associated with big data use cases, data lakes are, for example, used as central data management systems of resea …
The role of data science in healthcare advancements: applications, benefits, and future prospects.
Subrahmanya SVG, Shetty DK, Patil V, Hameed BMZ, Paul R, Smriti K, Naik N, Somani BK. Subrahmanya SVG, et al. Ir J Med Sci. 2022 Aug;191(4):1473-1483. doi: 10.1007/s11845-021-02730-z. Epub 2021 Aug 16. Ir J Med Sci. 2022. PMID: 34398394 Free PMC article. Review.
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. ...This data requires effect
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, us
Identifying and addressing data asymmetries so as to enable (better) science.
Verhulst S, Young A. Verhulst S, et al. Front Big Data. 2022 Jul 18;5:888384. doi: 10.3389/fdata.2022.888384. eCollection 2022. Front Big Data. 2022. PMID: 35923558 Free PMC article. Review.
This article seeks to start filling the analytical gap regarding data asymmetries globally, with a specific focus on the asymmetrical availability of privately-held data for open science, and a look at current efforts to address these data asymmetries. ...Mor …
This article seeks to start filling the analytical gap regarding data asymmetries globally, with a specific focus on the asymmetrical …
Missing data analysis: making it work in the real world.
Graham JW. Graham JW. Annu Rev Psychol. 2009;60:549-76. doi: 10.1146/annurev.psych.58.110405.085530. Annu Rev Psychol. 2009. PMID: 18652544 Review.
This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normal-model multiple imputation (MI) and maximum likelihood methods. ...Solutions are given for missing data challenges suc …
This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descrip …
Reusing Biomedical Data as Agreed - Towards Structured Metadata for Data Use Agreements.
Bönisch C, Hanß S, Spicher N, Sax U, Krefting D. Bönisch C, et al. Stud Health Technol Inform. 2023 Sep 12;307:31-38. doi: 10.3233/SHTI230690. Stud Health Technol Inform. 2023. PMID: 37697835
INTRODUCTION: With increasing availability of reusable biomedical data - from cohort studies to clinical routine data, data re-users face the problem to manage transferred data according to the heterogeneous data use agreements. ...CONCLUSION: W …
INTRODUCTION: With increasing availability of reusable biomedical data - from cohort studies to clinical routine data, data
Data as an economic good, data as a commons, and data governance.
Purtova N, van Maanen G. Purtova N, et al. Law Innov Technol. 2023 Nov 1;16(1):1-42. doi: 10.1080/17579961.2023.2265270. eCollection 2024. Law Innov Technol. 2023. PMID: 39634789 Free PMC article.
This paper provides a systematic and critical review of the economics literature on data as an economic good and draws lessons for data governance. We conclude that focusing on data as an economic good in governance efforts is hardwired to only result in more …
This paper provides a systematic and critical review of the economics literature on data as an economic good and draws lessons for …
Citizen science as a data-based practice: A consideration of data justice.
Christine DI, Thinyane M. Christine DI, et al. Patterns (N Y). 2021 Mar 23;2(4):100224. doi: 10.1016/j.patter.2021.100224. eCollection 2021 Apr 9. Patterns (N Y). 2021. PMID: 33982019 Free PMC article.
Despite the potential of citizen science to achieve social justice agendas through a data-intensive and data-driven participatory scientific enquiry, scholarship in critical data studies offers several problematizations of data-based practices, highlig …
Despite the potential of citizen science to achieve social justice agendas through a data-intensive and data-driven participat …
Qualitative Data Sharing: Data Repositories and Academic Libraries as Key Partners in Addressing Challenges.
Mannheimer S, Pienta A, Kirilova D, Elman C, Wutich A. Mannheimer S, et al. Am Behav Sci. 2019 May;63(5):643-664. Epub 2018 Jun 28. Am Behav Sci. 2019. PMID: 31693016 Free PMC article.
As qualitative researchers are faced with new expectations to share their data, data repositories and academic libraries are working to address the specific challenges of qualitative research data. This paper describes how data repositories and academi …
As qualitative researchers are faced with new expectations to share their data, data repositories and academic libraries are w …
[Data Storytelling as a Critical Success Factor for Data Science].
Neifer T, Lawo D, Bossauer P, Esau M, Jerofejev AM. Neifer T, et al. HMD Prax Wirtsch Inform. 2020;57(5):1033-1046. doi: 10.1365/s40702-020-00662-3. Epub 2020 Oct 8. HMD Prax Wirtsch Inform. 2020. PMID: 40477177 Free PMC article. German.
However, challenges arise from the integration of data into corporate strategy and culture. For example, complex data and analysis results must be communicated to stakeholders who are not data-affine. ...However, data storytelling is still a niche topi …
However, challenges arise from the integration of data into corporate strategy and culture. For example, complex data and anal …
A review of data abstraction.
Cima G, Console M, Lenzerini M, Poggi A. Cima G, et al. Front Artif Intell. 2023 Jun 23;6:1085754. doi: 10.3389/frai.2023.1085754. eCollection 2023. Front Artif Intell. 2023. PMID: 37426303 Free PMC article. Review.
It is well-known that Artificial Intelligence (AI), and in particular Machine Learning (ML), is not effective without good data preparation, as also pointed out by the recent wave of data-centric AI. Data preparation is the process of gathering, transforming …
It is well-known that Artificial Intelligence (AI), and in particular Machine Learning (ML), is not effective without good data prepa …
7,195,221 results
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