cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21107226
title: PERCEPT: A New Online Change-Point Detection Method using Topological Data Analysis

description: Topological data analysis (TDA) provides a set of data analysis tools for extracting embedded topological structures from complex high-dimensional datasets. In recent years, TDA has been a rapidly growing field which has found success in a wide range of applications, including signal processing, neuroscience and network analysis. In these applications, the online detection of changes is of crucial importance, but this can be highly challenging since such changes often occur in low-dimensional embeddings within high-dimensional data streams. We thus propose a new method, called PERsistence diagram-based ChangE-PoinT detection (PERCEPT), which leverages the learned topological structure from TDA to sequentially detect changes. PERCEPT follows two key steps: it first learns the embedded topology as a point cloud via persistence diagrams, then applies a nonparametric monitoring approach for detecting changes in the resulting point cloud distributions. This yields a nonparametric, topology-aware framework which can efficiently detect online geometric changes. We investigate the effectiveness of PERCEPT over existing methods in a suite of numerical experiments where the data streams have an embedded topological structure. We then demonstrate the usefulness of PERCEPT in two applications on solar flare monitoring and human gesture detection.
DOI: 10.6084/m9.figshare.21107226.v2
title: PERCEPT: A New Online Change-Point Detection Method using Topological Data Analysis

description: Topological data analysis (TDA) provides a set of data analysis tools for extracting embedded topological structures from complex high-dimensional datasets. In recent years, TDA has been a rapidly growing field which has found success in a wide range of applications, including signal processing, neuroscience and network analysis. In these applications, the online detection of changes is of crucial importance, but this can be highly challenging since such changes often occur in low-dimensional embeddings within high-dimensional data streams. We thus propose a new method, called PERsistence diagram-based ChangE-PoinT detection (PERCEPT), which leverages the learned topological structure from TDA to sequentially detect changes. PERCEPT follows two key steps: it first learns the embedded topology as a point cloud via persistence diagrams, then applies a nonparametric monitoring approach for detecting changes in the resulting point cloud distributions. This yields a nonparametric, topology-aware framework which can efficiently detect online geometric changes. We investigate the effectiveness of PERCEPT over existing methods in a suite of numerical experiments where the data streams have an embedded topological structure. We then demonstrate the usefulness of PERCEPT in two applications on solar flare monitoring and human gesture detection.
DOI: 10.6084/m9.figshare.21107226.v1
title: PERCEPT: a new online change-point detection method using topological data analysis

description: Topological data analysis (TDA) provides a set of data analysis tools for extracting embedded topological structures from complex high-dimensional datasets. In recent years, TDA has been a rapidly growing field which has found success in a wide range of applications, including signal processing, neuroscience and network analysis. In these applications, the online detection of changes is of crucial importance, but this can be highly challenging since such changes often occur in low-dimensional embeddings within high-dimensional data streams. We thus propose a new method, called PERsistence diagram-based ChangE-PoinT detection (PERCEPT), which leverages the learned topological structure from TDA to sequentially detect changes. PERCEPT follows two key steps: it first learns the embedded topology as a point cloud via persistence diagrams, then applies a non-parametric monitoring approach for detecting changes in the resulting point cloud distributions. This yields a non-parametric, topology-aware framework which can efficiently detect online geometric changes. We investigate the effectiveness of PERCEPT over existing methods in a suite of numerical experiments where the data streams have an embedded topological structure. We then demonstrate the usefulness of PERCEPT in two applications on solar flare monitoring and human gesture detection.
DOI: 10.25549/usctheses-ouc11291671
title: Unknown
DOI: 10.25549/usctheses-ouc11257903
title: Unknown
DOI: 10.25549/usctheses-ouc11257888
title: Unknown
DOI: 10.25549/usctheses-ouc11276327
title: Unknown
DOI: 10.25549/usctheses-ouc11276345
title: Unknown
DOI: 10.25549/usctheses-ouc11276357
title: Unknown
DOI: 10.25549/usctheses-ouc11257809
title: Unknown
DOI: 10.25549/usctheses-ouc11292289
title: Unknown
DOI: 10.25549/usctheses-ouc11292310
title: Unknown
DOI: 10.25549/usctheses-ouc112124628
title: “‘Porque sin madres no hay revolucion’: Mothering the revolution in contemporary Chicana/Latina literature and cultural production
DOI: 10.25549/usctheses-ouc11292322
title: Unknown
DOI: 10.25549/usctheses-ouc11292336
title: Unknown
DOI: 10.25549/usctheses-ouc11292342
title: Unknown
DOI: 10.25549/usctheses-ouc11292349
title: Unknown
DOI: 10.25549/usctheses-ouc11650563
title: Unknown
DOI: 10.25549/usctheses-ouc11291986
title: Unknown
DOI: 10.25549/usctheses-ouc11292422
title: Unknown
DOI: 10.25549/usctheses-ouc11292441
title: Unknown
DOI: 10.25549/usctheses-ouc11257897
title: Unknown
DOI: 10.25549/usctheses-ouc11257901
title: Unknown
DOI: 10.25549/usctheses-ouc11257907
title: Unknown
DOI: 10.25549/usctheses-ouc11258649
title: Unknown
DOI: 10.25549/usctheses-ouc11258650
title: Unknown
DOI: 10.25549/usctheses-ouc11257923
title: Unknown
DOI: 10.25549/usctheses-ouc11257924
title: Unknown
DOI: 10.25549/usctheses-ouc11257845
title: Unknown
DOI: 10.25549/usctheses-ouc11256453
title: Unknown
DOI: 10.5683/sp2/21lh4z
title: Ontario University Applicant Survey, 2005 [Canada]

description: The 2005 University Applicant Survey report looks at two main chapters; applicant profile, and university funding. In particular, the report directly addresses the information needs of university marketing, recruitment, and public affairs departments. The Ontario Universities' Application Centre (OUAC) selected a sample of 40,000 participants, randomly from the total population of approximately 121,000 applicants to Ontario universities. The Ontario portion of the study achieved its highest-ever response rate of 42%. Of the participants, 1,060 were French first-language applicants, and were thus emailed French versions of the invitation and questionnaire. The questionnaire was online, which provided added functionality such as built-in skip patterns, proprietary questions directed to specific applicant segments, and the ability to over-sample applicant groups. This methodology also provided greater convenience for an internet savvy generation of young consumers. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.
DOI: 10.25549/usctheses-ouc11256454
title: Unknown
DOI: 10.25549/usctheses-ouc11256457
title: Unknown
DOI: 10.25549/usctheses-ouc11256511
title: Unknown
DOI: 10.25549/usctheses-ouc11256512
title: Unknown
DOI: 10.25549/usctheses-ouc11256513
title: Unknown
DOI: 10.25549/usctheses-ouc11256516
title: Unknown
DOI: 10.25549/usctheses-ouc11256517
title: Unknown
DOI: 10.25549/usctheses-ouc11256397
title: Unknown
DOI: 10.25549/usctheses-ouc11256398
title: Unknown
DOI: 10.25549/usctheses-ouc11256403
title: Unknown
DOI: 10.25549/usctheses-ouc11256401
title: Unknown
DOI: 10.25549/usctheses-ouc11256402
title: Unknown
DOI: 10.25549/usctheses-ouc11256405
title: Unknown
DOI: 10.25549/usctheses-ouc11256408
title: Unknown
DOI: 10.25549/usctheses-ouc11256410
title: Unknown
DOI: 10.25549/usctheses-ouc11256411
title: Unknown
DOI: 10.25549/usctheses-ouc11256419
title: Unknown
DOI: 10.25549/usctheses-ouc11256413
title: Unknown
DOI: 10.25549/usctheses-ouc11256414
title: Unknown
DOI: 10.25549/usctheses-ouc11256415
title: Unknown
DOI: 10.25549/usctheses-ouc11256416
title: Unknown
DOI: 10.25549/usctheses-ouc11256420
title: Unknown
DOI: 10.25549/usctheses-ouc11256422
title: Unknown
DOI: 10.25549/usctheses-ouc11256425
title: Unknown
DOI: 10.25549/usctheses-ouc11256427
title: Unknown
DOI: 10.25549/usctheses-ouc11256428
title: Unknown
DOI: 10.25549/usctheses-ouc11256429
title: Unknown
DOI: 10.25549/usctheses-ouc11256430
title: Unknown
DOI: 10.25549/usctheses-ouc11256434
title: Unknown
DOI: 10.25549/usctheses-ouc11256436
title: Unknown
DOI: 10.25549/usctheses-ouc11256446
title: Unknown
DOI: 10.25549/usctheses-ouc11256437
title: Unknown
DOI: 10.25549/usctheses-ouc11256647
title: Unknown
DOI: 10.25549/usctheses-ouc11256652
title: Unknown
DOI: 10.25549/usctheses-ouc11256653
title: Unknown
DOI: 10.25549/usctheses-ouc11256439
title: Unknown
DOI: 10.25549/usctheses-ouc11256440
title: Unknown
DOI: 10.25549/usctheses-ouc11256443
title: Unknown
DOI: 10.25549/usctheses-ouc11256444
title: Unknown
DOI: 10.25549/usctheses-ouc11256447
title: Unknown
DOI: 10.25549/usctheses-ouc11256449
title: Unknown
DOI: 10.25549/usctheses-ouc11262803
title: Unknown
DOI: 10.25549/usctheses-ouc11262820
title: Unknown
DOI: 10.25549/usctheses-ouc11263212
title: Unknown
DOI: 10.25549/usctheses-ouc11262952
title: Unknown
DOI: 10.25549/usctheses-ouc11262963
title: Unknown
DOI: 10.25549/usctheses-ouc11262971
title: Unknown
DOI: 10.25549/usctheses-ouc11262977
title: Unknown
DOI: 10.25549/usctheses-ouc11262981
title: Unknown
DOI: 10.25549/usctheses-ouc11262996
title: Unknown
DOI: 10.25549/usctheses-ouc11263001
title: Unknown
DOI: 10.25549/usctheses-ouc11263035
title: Unknown
DOI: 10.25549/usctheses-ouc11263097
title: Unknown
DOI: 10.25549/usctheses-ouc11263100
title: Unknown
DOI: 10.25549/usctheses-ouc11263108
title: Unknown
DOI: 10.25549/usctheses-ouc11263261
title: Unknown
DOI: 10.25549/usctheses-ouc11263266
title: Unknown
DOI: 10.6084/m9.figshare.21408489
title: Tshidi Lelaka - COREQ_checklist.pdf

description: COREQ Checklist
DOI: 10.25549/usctheses-ouc11268483
title: Unknown
DOI: 10.25549/usctheses-ouc11268836
title: Unknown
DOI: 10.25549/usctheses-ouc11269603
title: Unknown
DOI: 10.25549/usctheses-ouc11269754
title: Unknown
DOI: 10.25549/usctheses-ouc11269805
title: Unknown
DOI: 10.25549/usctheses-ouc11270062
title: Unknown
DOI: 10.25549/usctheses-ouc11270119
title: Unknown
DOI: 10.25549/usctheses-ouc11270149
title: Unknown
DOI: 10.25549/usctheses-ouc11270219
title: Unknown
DOI: 10.25549/usctheses-ouc11270966
title: Unknown
DOI: 10.25549/usctheses-ouc11271008
title: Unknown
DOI: 10.5683/sp2/ys02ac
title: Pan Canadian High School Follow-Up Survey (Class of 2003), 2005 [Canada]

description: The Pan-Canadian High School Follow-Up Survey (Class of 2003) presents the latest evidence concerning motivational/ informational, financial and academic barriers to PSE. Also, because individuals tend to be affected by more than one factor limiting access, the report explores how specific groups of youth are differently impacted by multiple barriers to PSE. In particular, the research presents data on the effects of these barriers on first-generation students (i.e., the first generation of students in a family to pursue PSE) and Aboriginal students. The Pan-Canadian High School Follow-Up Survey (Class of 2003) was conducted by the Canada Millennium Scholarship Foundation in collaboration with R.A. Malatest & Associates Ltd. and the provinces of Alberta, Manitoba, Saskatchewan and New Brunswick. The final survey instrument contained questions organized into the following modules: High School Experience, Students Currently Enrolled in a Post-Secondary Program, Respondents Previously Enrolled in a Post-Secondary Program, Respondents Not in a Post-Secondary Program, and Demographics. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.
DOI: 10.25549/usctheses-ouc11271124
title: Unknown
DOI: 10.25549/usctheses-ouc11271248
title: Unknown
DOI: 10.25549/usctheses-ouc11271304
title: Unknown
DOI: 10.25549/usctheses-ouc11271313
title: Unknown
DOI: 10.25549/usctheses-ouc11271456
title: Unknown
DOI: 10.25549/usctheses-ouc11272121
title: Unknown
DOI: 10.25549/usctheses-ouc11272192
title: Unknown
DOI: 10.25549/usctheses-ouc11272935
title: Unknown
DOI: 10.25549/usctheses-ouc11272944
title: Unknown
DOI: 10.25549/usctheses-ouc11272263
title: Unknown
DOI: 10.25549/usctheses-ouc11272970
title: Unknown
DOI: 10.25549/usctheses-ouc11272988
title: Unknown
DOI: 10.25549/usctheses-ouc11272331
title: Unknown
DOI: 10.25549/usctheses-ouc11272341
title: Unknown
DOI: 10.25549/usctheses-ouc11272362
title: Unknown
DOI: 10.25549/usctheses-ouc11273054
title: Unknown
DOI: 10.25549/usctheses-ouc11272386
title: Unknown
DOI: 10.25549/usctheses-ouc11273092
title: Unknown
DOI: 10.25549/usctheses-ouc11272406
title: Unknown
DOI: 10.25549/usctheses-ouc11273127
title: Unknown
DOI: 10.25549/usctheses-ouc11272423
title: Unknown
DOI: 10.25549/usctheses-ouc11272424
title: Unknown
DOI: 10.25549/usctheses-ouc11272431
title: Unknown
DOI: 10.25549/usctheses-ouc11272475
title: Unknown
DOI: 10.25549/usctheses-ouc11273162
title: Unknown
DOI: 10.25549/usctheses-ouc11272478
title: Unknown
DOI: 10.25549/usctheses-ouc11272490
title: Unknown
DOI: 10.25549/usctheses-ouc11273184
title: Unknown
DOI: 10.25549/usctheses-ouc11272529
title: Unknown
DOI: 10.25549/usctheses-ouc11273211
title: Unknown
DOI: 10.25549/usctheses-ouc11273213
title: Unknown
DOI: 10.25549/usctheses-ouc11273220
title: Unknown
DOI: 10.25549/usctheses-ouc11273221
title: Unknown
DOI: 10.5281/zenodo.6703953
title: Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor

description: Data corresponding to the figures of the main text of "Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor"
DOI: 10.5281/zenodo.6703952
title: Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor

description: Data corresponding to the figures of the main text of "Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor"
DOI: 10.25549/usctheses-ouc11273219
title: Unknown
DOI: 10.25549/usctheses-ouc11273236
title: Unknown
DOI: 10.25549/usctheses-ouc11273254
title: Unknown
DOI: 10.25549/usctheses-ouc11273268
title: Unknown
DOI: 10.25549/usctheses-ouc11272525
title: Unknown
DOI: 10.25549/usctheses-ouc11273281
title: Unknown
DOI: 10.25549/usctheses-ouc11273287
title: Unknown
DOI: 10.25549/usctheses-ouc11273316
title: Unknown
DOI: 10.25549/usctheses-ouc11273336
title: Unknown
DOI: 10.25549/usctheses-ouc11272551
title: Unknown
DOI: 10.25549/usctheses-ouc11272557
title: Unknown
DOI: 10.25549/usctheses-ouc11273341
title: Unknown
DOI: 10.25549/usctheses-ouc11273345
title: Unknown
DOI: 10.25549/usctheses-ouc11273353
title: Unknown
DOI: 10.25549/usctheses-ouc11273355
title: Unknown
DOI: 10.25549/usctheses-ouc11273361
title: Unknown
DOI: 10.25549/usctheses-ouc11273383
title: Unknown
DOI: 10.25549/usctheses-ouc11273385
title: Unknown
DOI: 10.25549/usctheses-ouc11273403
title: Unknown
DOI: 10.25549/usctheses-ouc11273447
title: Unknown
DOI: 10.25549/usctheses-ouc11273430
title: Unknown
DOI: 10.25549/usctheses-ouc11273451
title: Unknown
DOI: 10.25549/usctheses-ouc11273456
title: Unknown
DOI: 10.25549/usctheses-ouc11273468
title: Unknown
DOI: 10.25549/usctheses-ouc11272574
title: Unknown
DOI: 10.25549/usctheses-ouc11273502
title: Unknown
DOI: 10.25549/usctheses-ouc11273520
title: Unknown
DOI: 10.25549/usctheses-ouc11273549
title: Unknown
DOI: 10.25549/usctheses-ouc11273571
title: Unknown
DOI: 10.25549/usctheses-ouc11273577
title: Unknown
DOI: 10.25549/usctheses-ouc11273599
title: Unknown
DOI: 10.25549/usctheses-ouc11273616
title: Unknown
DOI: 10.25549/usctheses-ouc11272854
title: Unknown
DOI: 10.25549/usctheses-ouc11274189
title: Unknown
DOI: 10.25549/usctheses-ouc11274195
title: Unknown
DOI: 10.25549/usctheses-ouc11273952
title: Unknown
DOI: 10.25549/usctheses-ouc11274034
title: Unknown
DOI: 10.25549/usctheses-ouc11274054
title: Unknown
DOI: 10.25549/usctheses-ouc11274116
title: Unknown
DOI: 10.25549/usctheses-ouc11274202
title: Unknown
DOI: 10.25549/usctheses-ouc11274205
title: Unknown
DOI: 10.25549/usctheses-ouc11274245
title: Unknown
DOI: 10.25549/usctheses-ouc11274262
title: Unknown
DOI: 10.25549/usctheses-ouc11274295
title: Unknown
DOI: 10.25549/usctheses-ouc11274313
title: Unknown
DOI: 10.25549/usctheses-ouc11274327
title: Unknown
DOI: 10.25549/usctheses-ouc11274334
title: Unknown
DOI: 10.25549/usctheses-ouc11274338
title: Unknown
DOI: 10.25549/usctheses-ouc11274343
title: Unknown
DOI: 10.25549/usctheses-ouc11274363
title: Unknown
DOI: 10.25549/usctheses-ouc11274378
title: Unknown
DOI: 10.25549/usctheses-ouc11274374
title: Unknown
DOI: 10.25549/usctheses-ouc11274403
title: Unknown
DOI: 10.25549/usctheses-ouc11274379
title: Unknown
DOI: 10.25549/usctheses-ouc11274384
title: Unknown
DOI: 10.25549/usctheses-ouc11274389
title: Unknown
DOI: 10.25549/usctheses-ouc11274390
title: Unknown
DOI: 10.25549/usctheses-ouc11274409
title: Unknown
DOI: 10.25549/usctheses-ouc11274430
title: Unknown
DOI: 10.25549/usctheses-ouc11274437
title: Unknown
DOI: 10.25549/usctheses-ouc11274446
title: Unknown
DOI: 10.25549/usctheses-ouc11274452
title: Unknown
DOI: 10.25549/usctheses-ouc11274451
title: Unknown
DOI: 10.25549/usctheses-ouc11274457
title: Unknown
DOI: 10.25549/usctheses-ouc11274458
title: Unknown
DOI: 10.25549/usctheses-ouc11274473
title: Unknown
DOI: 10.25549/usctheses-ouc11274481
title: Unknown
DOI: 10.25549/usctheses-ouc11274482
title: Unknown
DOI: 10.25549/usctheses-ouc11274502
title: Unknown
DOI: 10.25549/usctheses-ouc11274515
title: Unknown
DOI: 10.25549/usctheses-ouc11274561
title: Unknown
DOI: 10.25549/usctheses-ouc11274562
title: Unknown
DOI: 10.25549/usctheses-ouc11274575
title: Unknown
DOI: 10.25549/usctheses-ouc11274585
title: Unknown
DOI: 10.25549/usctheses-ouc11274604
title: Unknown
DOI: 10.25549/usctheses-ouc11274607
title: Unknown
DOI: 10.25549/usctheses-ouc11274608
title: Unknown
DOI: 10.25549/usctheses-ouc11274606
title: Unknown
DOI: 10.25549/usctheses-ouc11274610
title: Unknown
DOI: 10.25549/usctheses-ouc11274631
title: Unknown
DOI: 10.25549/usctheses-ouc11274634
title: Unknown
DOI: 10.25549/usctheses-ouc11274637
title: Unknown
DOI: 10.25549/usctheses-ouc11274636
title: Unknown
DOI: 10.25549/usctheses-ouc11274952
title: Unknown
DOI: 10.25549/usctheses-ouc11274954
title: Unknown
DOI: 10.25549/usctheses-ouc11275037
title: Unknown
DOI: 10.25549/usctheses-ouc11275184
title: Unknown
DOI: 10.25549/usctheses-ouc11275203
title: Unknown
DOI: 10.25549/usctheses-ouc11275229
title: Unknown
DOI: 10.25549/usctheses-ouc11275241
title: Unknown
DOI: 10.25549/usctheses-ouc11275240
title: Unknown
DOI: 10.25549/usctheses-ouc11275244
title: Unknown
DOI: 10.25549/usctheses-ouc11275248
title: Unknown
DOI: 10.25549/usctheses-ouc11275250
title: Unknown
DOI: 10.25549/usctheses-ouc11275269
title: Unknown
DOI: 10.25549/usctheses-ouc11275276
title: Unknown
DOI: 10.25549/usctheses-ouc11275283
title: Unknown
DOI: 10.25549/usctheses-ouc11275289
title: Unknown
DOI: 10.25549/usctheses-ouc11275307
title: Unknown
DOI: 10.25549/usctheses-ouc11275313
title: Unknown
DOI: 10.25549/usctheses-ouc11275329
title: Unknown
DOI: 10.25549/usctheses-ouc11275325
title: Unknown
DOI: 10.5281/zenodo.1308775
title: RDA Overview for the Social Sciences - October 2022

description: This upload is the fourth version of a dataset on the RDA groups and outputs which was generated in October 2022. It is connected to a report that described a project that assessed the RDA Groups and Outcomes and their relevance for social science researchers. This datasets gives an overview of the results of this analysis, providing a list of all RDA Groups as well as an indication of the relevance for Social Sciences researchers. The project was executed and updated by Data Archiving and Networked Services (DANS). This dataset contains: - an excel overview of the RDA Working and Interest groups - additional csv copies of the separate sheets of the excel overview
DOI: 10.5281/zenodo.1308776
title: Rda Overview For The Social Sciences

description: This upload is connected to a report that was written for the RDA Europe 4.0 project. The report described a project that assessed the RDA Groups and Outcomes and their relevance for social science researchers. This datasets gives an overview of the first results of this analysis, providing a list of all RDA Groups and Outcomes as well as an indication of the relevance of the Groups and Outcomes for Social Sciences researchers. The project was executed by Data Archiving and Networked Services (DANS) which acts as a national RDA node in the Netherlands and as an ambassador for the social sciences.

For more information on the RDA see: https://www.rd-alliance.org/rda-europe

 

This dataset contains:

- an excel overview of the RDA groups and Outcomes

- additional csv copies of the separate sheets of the excel overview

- a readme file describing the uploaded documents in more detail
DOI: 10.5281/zenodo.3580618
title: RDA Overview for the Social Sciences - October 2019

description: This upload is the second version of a dataset on the RDA groups and outputs which was generated in October 2019. It is connected to a report that was written for the RDA Europe 4.0 project. The report described a project that assessed the RDA Groups and Outcomes and their relevance for social science researchers. This datasets gives an overview of the first results of this analysis, providing a list of all RDA Groups and Outcomes as well as an indication of the relevance of the Groups and Outcomes for Social Sciences researchers. The project was executed by Data Archiving and Networked Services (DANS) which acts as a national RDA node in the Netherlands and as an ambassador for the social sciences. For more information on the RDA see: https://www.rd-alliance.org/rda-europe This dataset contains: - an excel overview of the RDA groups and Outcomes - additional csv copies of the separate sheets of the excel overview - a readme file describing the uploaded documents in more detail
DOI: 10.5281/zenodo.4776356
title: RDA Overview for the Social Sciences - May 2020

description: This upload is the third version of a dataset on the RDA groups and outputs which was generated in May 2021. It is connected to a report that described a project that assessed the RDA Groups and Outcomes and their relevance for social science researchers. This datasets gives an overview of the results of this analysis, providing a list of all RDA Groups as well as an indication of the relevance for Social Sciences researchers. The project was executed by Data Archiving and Networked Services (DANS) which acted as a national RDA node in the Netherlands and as an ambassador for the social sciences within the RDA Europe Project. This dataset contains: - an excel overview of the RDA Working and Interest groups - additional csv copies of the separate sheets of the excel overview