Data Science Journal最新文献_第5页

We Can Make a Better Use of ORCID: Five Observed Misapplications 我们可以更好地利用ORCID:五个观察到的错误应用

Data Science Journal Pub Date : 2021-01-01 DOI: 10.5334/dsj-2021-038

Miriam Baglioni, P. Manghi, A. Mannocci, A. Bardi

{"title":"We Can Make a Better Use of ORCID: Five Observed Misapplications","authors":"Miriam Baglioni, P. Manghi, A. Mannocci, A. Bardi","doi":"10.5334/dsj-2021-038","DOIUrl":"https://doi.org/10.5334/dsj-2021-038","url":null,"abstract":"Since 2012, the “Open Researcher and Contributor ID” organisation (ORCID) has been successfully running a worldwide registry, with the aim of “providing a unique, persistent identifier for individuals to use as they engage in research, scholarship, and innovation activities”. Any service in the scholarly communication ecosystem (e.g., publishers, repositories, CRIS systems, etc.) can contribute to a non-ambiguous scholarly record by including, during metadata deposition, referrals to iDs in the ORCID registry. The OpenAIRE Research Graph is a scholarly knowledge graph that aggregates both records from the ORCID registry and publication records with ORCID referrals from publishers and repositories worldwide to yield research impact monitoring and Open Science statistics. Graph data analytics revealed “anomalies” due to ORCID registry “misapplications”, caused by wrong ORCID referrals and misexploitation of the ORCID registry. Albeit these affect just a minority of ORCID records, they inevitably affect the quality of the ORCID infrastructure and may fuel the rise of detractors and scepticism about the service. In this paper, we classify and qualitatively document such misapplications, identifying five ORCID registrant-related and ORCID referral-related anomalies to raise awareness among ORCID users. We describe the current countermeasures taken by ORCID and, where applicable, provide recommendations. Finally, we elaborate on the importance of a community-steered Open Science infrastructure and the benefits this approach has brought and may bring to ORCID. *Author affiliations can be found in the back matter of this article 2 Baglioni et al. Data Science Journal DOI: 10.5334/dsj-2021038","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71068452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Towards Globally Unique Identification of Physical Samples: Governance and Technical Implementation of the IGSN Global Sample Number 实现物理样本的全球唯一标识:IGSN全球样本编号的治理和技术实施

Data Science Journal Pub Date : 2021-01-01 DOI: 10.5334/dsj-2021-033

J. Klump, K. Lehnert, D. Ulbricht, A. Devaraju, K. Elger, D. Fleischer, S. Ramdeen, L. Wyborn

引用次数: 9

Keeping Track of Samples in Multidisciplinary Fieldwork 多学科野外工作样本的跟踪

Data Science Journal Pub Date : 2021-01-01 DOI: 10.5334/dsj-2021-034

P. Ellingsen, L. Ferrighi, Ø. Godøy, T. Gabrielsen

引用次数: 1

Interconnecting Systems Using Machine-Actionable Data Management Plans – Hackathon Report 使用机器可操作数据管理计划的互连系统-黑客马拉松报告

Data Science Journal Pub Date : 2021-01-01 DOI: 10.5334/dsj-2021-035

João Cardoso, L. J. Castro, Tomasz Miksa

引用次数: 2

Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets 全球获取和协调单个地球科学数据集的质量信息的行动呼吁

Data Science Journal Pub Date : 2020-12-15 DOI: 10.31219/osf.io/nwe5p

G. Peng, R. Downs, C. Lacagnina, H. Ramapriyan, I. Ivánová, D. Moroni, Yaxing Wei, Larnicol Gilles, L. Wyborn, Mitchell Goldberg, J. Schulz, I. Bastrakova, A. Ganske, L. Bastin, S. Khalsa, Mingfang Wu, C. Shie, N. Ritchey, Dave Jones, T. Habermann, C. Lief, Iolanda Maggio, M. Albani, S. Stall, Lihang Zhou, M. Drévillon, Sarah M. Champion, C. Hou, F. Doblas-Reyes, K. Lehnert, E. Robinson, K. Bugbee

{"title":"Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets","authors":"G. Peng, R. Downs, C. Lacagnina, H. Ramapriyan, I. Ivánová, D. Moroni, Yaxing Wei, Larnicol Gilles, L. Wyborn, Mitchell Goldberg, J. Schulz, I. Bastrakova, A. Ganske, L. Bastin, S. Khalsa, Mingfang Wu, C. Shie, N. Ritchey, Dave Jones, T. Habermann, C. Lief, Iolanda Maggio, M. Albani, S. Stall, Lihang Zhou, M. Drévillon, Sarah M. Champion, C. Hou, F. Doblas-Reyes, K. Lehnert, E. Robinson, K. Bugbee","doi":"10.31219/osf.io/nwe5p","DOIUrl":"https://doi.org/10.31219/osf.io/nwe5p","url":null,"abstract":"Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data and need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Community practical guidelines will allow for global access and harmonization of quality information at the level of individual Earth science datasets and support open science.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46714816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation 基于agent模拟的COVID-19病例报告数据合成复制与增强

Data Science Journal Pub Date : 2020-11-10 DOI: 10.1101/2020.11.07.20227462

N. Popper, M. Zechmeister, D. Brunmeir, C. Rippinger, N. Weibrecht, C. Urach, M. Bicher, G. Schneckenreither, A. Rauber

引用次数: 12

Developing a Model Guidelines Addressing Legal Impediments to Open Access to Publicly Funded Research Data in Malaysia 制定解决马来西亚公共资助研究数据开放获取的法律障碍的示范指南

Data Science Journal Pub Date : 2019-01-01 DOI: 10.5334/dsj-2019-027

Haswira Nor Mohamad Hashim

引用次数: 1

Indigenous Data Governance: Strategies from United States Native Nations. 本土数据治理:来自美国本土的策略。

Data Science Journal Pub Date : 2019-01-01 DOI: 10.5334/dsj-2019-031

Stephanie Russo Carroll, Desi Rodriguez-Lonebear, Andrew Martinez

{"title":"Indigenous Data Governance: Strategies from United States Native Nations.","authors":"Stephanie Russo Carroll, Desi Rodriguez-Lonebear, Andrew Martinez","doi":"10.5334/dsj-2019-031","DOIUrl":"https://doi.org/10.5334/dsj-2019-031","url":null,"abstract":"Data have become the new global currency, and a powerful force in making decisions and wielding power. As the world engages with open data, big data reuse, and data linkage, what do data-driven futures look like for communities plagued by data inequities? Indigenous data stakeholders and non-Indigenous allies have explored this question over the last three years in a series of meetings through the Research Data Alliance (RDA). Drawing on RDA and other gatherings, and a systematic scan of literature and practice, we consider possible answers to this question in the context of Indigenous peoples vis-á-vis two emerging concepts: Indigenous data sovereignty and Indigenous data governance. Specifically, we focus on the data challenges facing Native nations and the intersection of data, tribal sovereignty, and power. Indigenous data sovereignty is the right of each Native nation to govern the collection, ownership, and application of the tribe's data. Native nations exercise Indigenous data sovereignty through the interrelated processes of Indigenous data governance and decolonizing data. This paper explores the implications of Indigenous data sovereignty and Indigenous data governance for Native nations and others. We argue for the repositioning of authority over Indigenous data back to Indigenous peoples. At the same time, we recognize that there are significant obstacles to rebuilding effective Indigenous data systems and the process will require resources, time, and partnerships among Native nations, other governments, and data agents.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"18 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39613550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 86

Building Open Access to Research (OAR) Data Infrastructure at NIST. 在NIST建立开放存取研究(OAR)数据基础设施

Data Science Journal Pub Date : 2019-01-01 DOI: 10.5334/dsj-2019-030

Gretchen Greene, Raymond Plante, Robert Hanisch

{"title":"Building Open Access to Research (OAR) Data Infrastructure at NIST.","authors":"Gretchen Greene, Raymond Plante, Robert Hanisch","doi":"10.5334/dsj-2019-030","DOIUrl":"10.5334/dsj-2019-030","url":null,"abstract":"As a National Metrology Institute (NMI), the USA National Institute of Standards and Technology (NIST) scientists, engineers and technology experts conduct research across a full spectrum of physical science domains. NIST is a non-regulatory agency within the U.S. Department of Commerce with a mission to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life. NIST research results in the production and distribution of standard reference materials, calibration services, and datasets. These are generated from a wide range of complex laboratory instrumentation, expert analyses, and calibration processes. In response to a government open data policy, and in collaboration with the broader research community, NIST has developed a federated Open Access to Research (OAR) scientific data infrastructure aligned with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Through the OAR initiatives, NIST's Material Measurement Laboratory Office of Data and Informatics (ODI) recently released a new scientific data discovery portal and public data repository. These science-oriented applications provide dissemination and public access for data from across the broad spectrum of NIST research disciplines, including chemistry, biology, materials science (such as crystallography, nanomaterials, etc.), physics, disaster resilience, cyberinfrastructure, communications, forensics, and others. NIST's public data consist of carefully curated Standard Reference Data, legacy high valued data, and new research data publications. The repository is thus evolving both in content and features as the nature of research progresses. Implementation of the OAR infrastructure is key to NIST's role in sharing high integrity reproducible research for measurement science in a rapidly changing world.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46050150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What Do We Know About the Stewardship Gap. 我们对管理差距了解多少。

Data Science Journal Pub Date : 2018-01-01 Epub Date: 2018-08-17 DOI: 10.5334/dsj-2018-019

Jeremy York, Myron Gutmann, Francine Berman

{"title":"What Do We Know About the Stewardship Gap.","authors":"Jeremy York, Myron Gutmann, Francine Berman","doi":"10.5334/dsj-2018-019","DOIUrl":"10.5334/dsj-2018-019","url":null,"abstract":"In the 21st century, digital data drive innovation and decision-making in nearly every field. However, little is known about the total size, characteristics, and sustainability of these data. In the scholarly sphere, it is widely suspected that there is a gap between the amount of valuable digital data that is produced and the amount that is effectively stewarded and made accessible. The Stewardship Gap Project (http://bit.ly/stewardshipgap) investigates characteristics of, and measures, the stewardship gap for sponsored scholarly activity in the United States. This paper presents a preliminary definition of the stewardship gap based on a review of relevant literature and investigates areas of the stewardship gap for which metrics have been developed and measurements made, and where work to measure the stewardship gap is yet to be done. The main findings presented are 1) there is not one stewardship gap but rather multiple \"gaps\" that contribute to whether data is responsibly stewarded; 2) there are relationships between the gaps that can be used to guide strategies for addressing the various stewardship gaps; and 3) there are imbalances in the types and depths of studies that have been conducted to measure the stewardship gap.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"17 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6450659/pdf/nihms-1010966.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37133522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0