scholarly journals Cross-functional policy development for a Data Repository

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thea P Atwood ◽  
Erin Jerome ◽  
Ann Kardos ◽  
Stephen McGinty ◽  
Melanie Radik ◽  
...  
Keyword(s):  
Data Sharing ◽  
Current Literature ◽  
Policy Development ◽  
Lessons Learned ◽  
Data Repository ◽  
Data Policy ◽  
Data Collections

Policy can articulate the scope of work. For repositories that house data, policy can help users manage expectations,especially for individuals who are new to data sharing, or where expectations for sharing data havechanged. We cover some of the current literature around the process for writing policy, specifically focusingon policy for data collections and repositories, factors that encouraged us to create a repository policy, ourcollaborative process for creating the policy, and lessons learned. We hope that others can use our processesto build their own policy that reflects the needs of their campuses and scholars and further moves the needletoward the “Library as Publisher” model.

Growing controversy over “wise international water governance”

2004 ◽  
Vol 49 (7) ◽  
pp. 61-66
Author(s):  
J.M. Trondalen
Keyword(s):  
Policy Development ◽  
Water Governance ◽  
Lessons Learned ◽  
Political Climate ◽  
Trade Offs ◽  
Political Relationships ◽  
External Power ◽  
International Water ◽  
Principles And Standards ◽  
Political Economic

This article takes the perspective that when political relationships are strained, there seem to be few examples of wise international water resources governance. The Middle East is a striking example. Much effort has been put into policy development and the design of international principles, but very little into the translation of those into concrete and lasting governance. One of the theses of the article is that politics - whether domestic or international- in most cases overrides these principles and standards. Moreover ready-made regional co-operation models of water managements are not directly applicable to every geographical, political, economic and social setting. Certain factors are often under-estimated in international water negotiations, such as:• the complexity of any hydro-political negotiations, and need to develop commonly accepted standards;• the difficulty of translating policy - either politically or legally - into an operational and realistic negotiations strategy;• the format of the procedures and meetings;• recognition that third parties should have a long-term perspective on any conflict they get involved in.With reservations, the lessons learned indicate that the following factors have an impact on grid locked situations, such as: new substantive information; new trade-offs between the parties; and changed political climate or relationship with external power-brokers.

scholarly journals Data Communities: Empowering Researcher-Driven Data Sharing in the Sciences

1970 ◽  
Vol 15 (1) ◽  
pp. 7
Author(s):  
Rebecca Springer ◽  
Danielle Cooper
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Data Repository ◽  
Disciplinary Boundaries ◽  
Success Stories ◽  
Scholarly Communications ◽  
Information Technologists ◽  
Share Data ◽  
Technological Intervention ◽  
Informal Groups

There is a growing perception that science can progress more quickly, more innovatively, and more rigorously when researchers share data with each other. However many scientists are not engaging in data sharing and remain skeptical of its relevance to their work. As organizations and initiatives designed to promote STEM data sharing multiply – within, across, and outside academic institutions – there is a pressing need to decide strategically on the best ways to move forward. In this paper, we propose a new mechanism for conceptualizing and supporting STEM research data sharing.. Successful data sharing happens within data communities, formal or informal groups of scholars who share a certain type of data with each other, regardless of disciplinary boundaries. Drawing on the findings of four large-scale qualitative studies of research practices conducted by Ithaka S+R, as well as the scholarly literature, we identify what constitutes a data community and outline its most important features by studying three success stories, investigating the circumstances under which intensive data sharing is already happening. We contend that stakeholders who wish to promote data sharing – librarians, information technologists, scholarly communications professionals, and research funders, to name a few – should work to identify and empower emergent data communities. These are groups of scholars for whom a relatively straightforward technological intervention, usually the establishment of a data repository, could kickstart the growth of a more active data sharing culture. We conclude by offering recommendations for ways forward.

scholarly journals Higher education and oil: The cost of credit and crude – A strategic corollary for industry and higher education

2020 ◽  
pp. 095042222095954
Author(s):  
Joseph M. Woodside
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Policy Development ◽  
Oil Industry ◽  
Organizational Outcomes ◽  
Lessons Learned ◽  
Cycle Model ◽  
Business Cycle Model ◽  
The Cost ◽  
Support Decision Making

The market shock that accompanied COVID-19 has the potential to significantly transform higher education. At the same time, it presents an opportunity for higher education to learn from industry and adopt successful policies and practices. This paper provides lessons learned from the oil industry which may help higher education institutions to successfully navigate disruption and improve organizational outcomes. A four-phase business cycle model is presented as a strategic corollary for industry and higher education to support decision-making and provide a mechanism for discussion and policy development.

Factors influencing the data sharing behavior of researchers in sociology and political science

2018 ◽  
Vol 74 (5) ◽  
pp. 1053-1073 ◽  
Author(s):  
Wolfgang Zenk-Möltgen ◽  
Esra Akdeniz ◽  
Alexia Katsanidou ◽  
Verena Naßhoven ◽  
Ebru Balaban
Keyword(s):  
Political Science ◽  
Data Sharing ◽  
Behavioral Control ◽  
Statistical Tests ◽  
Data Availability ◽  
Impact Factors ◽  
Data Set ◽  
Content Type ◽  
Data Policy ◽  
Sharing Behavior

Purpose Open data and data sharing should improve transparency of research. The purpose of this paper is to investigate how different institutional and individual factors affect the data sharing behavior of authors of research articles in sociology and political science. Design/methodology/approach Desktop research analyzed attributes of sociology and political science journals (n=262) from their websites. A second data set of articles (n=1,011; published 2012-2014) was derived from ten of the main journals (five from each discipline) and stated data sharing was examined. A survey of the authors used the Theory of Planned Behavior to examine motivations, behavioral control, and perceived norms for sharing data. Statistical tests (Spearman’s ρ, χ2) examined correlations and associations. Findings Although many journals have a data policy for their authors (78 percent in sociology, 44 percent in political science), only around half of the empirical articles stated that the data were available, and for only 37 percent of the articles could the data be accessed. Journals with higher impact factors, those with a stated data policy, and younger journals were more likely to offer data availability. Of the authors surveyed, 446 responded (44 percent). Statistical analysis indicated that authors’ attitudes, reported past behavior, social norms, and perceived behavioral control affected their intentions to share data. Research limitations/implications Less than 50 percent of the authors contacted provided responses to the survey. Results indicate that data sharing would improve if journals had explicit data sharing policies but authors also need support from other institutions (their universities, funding councils, and professional associations) to improve data management skills and infrastructures. Originality/value This paper builds on previous similar research in sociology and political science and explains some of the barriers to data sharing in social sciences by combining journal policies, published articles, and authors’ responses to a survey.

scholarly journals American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange: From Inception to First Data Release and Beyond—Lessons Learned and Member Institutions’ Perspectives

2018 ◽  
pp. 1-14 ◽  
Author(s):  
Christine M. Micheel ◽  
Shawn M. Sweeney ◽  
Michele L. LeNoue-Newton ◽  
Fabrice André ◽  
Philippe L. Bedard ◽  
...  
Keyword(s):  
Cancer Research ◽  
Data Sharing ◽  
Information Exchange ◽  
American Association ◽  
Lessons Learned ◽  
Precision Oncology ◽  
Sequencing Data ◽  
Data Set ◽  
Genetic Sequencing ◽  
Share Data

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international data-sharing consortium focused on enabling advances in precision oncology through the gathering and sharing of tumor genetic sequencing data linked with clinical data. The project’s history, operational structure, lessons learned, and institutional perspectives on participation in the data-sharing consortium are reviewed. Individuals involved with the inception and execution of AACR Project GENIE from each member institution described their experiences and lessons learned. The consortium was conceived in January 2014 and publicly released its first data set in January 2017, which consisted of 18,804 samples from 18,324 patients contributed by the eight founding institutions. Commitment and contributions from many individuals at AACR and the member institutions were crucial to the consortium’s success. These individuals filled leadership, project management, informatics, data curation, contracts, ethics, and security roles. Many lessons were learned during the first 3 years of the consortium, including on how to gather, harmonize, and share data; how to make decisions and foster collaboration; and how to set the stage for continued participation and expansion of the consortium. We hope that the lessons shared here will assist new GENIE members as well as others who embark on the journey of forming a genomic data–sharing consortium.

Exploiting SeaDataCloud Temperature and Salinity time series data collections and comparing with Copernicus - a novel approach with SOURCE tool

2020 ◽  
Author(s):  
Paolo Oliveri ◽  
SImona Simoncelli ◽  
Pierluigi DI Pietro ◽  
Sara Durante
Keyword(s):  
Time Series ◽  
Best Practices ◽  
Data Management ◽  
Time Series Data ◽  
Management Plan ◽  
Global Ocean ◽  
Series Data ◽  
Data Repository ◽  
Log File ◽  
Data Collections

<p>One of the main challenges for the present and future in ocean observations is to find best practices for data management: infrastructures like Copernicus and SeaDataCloud already take responsibility for assembly, archive, update and publish data. Here we present the strengths and weaknesses in a SeaDataCloud Temperature and Salinity time series data collections, in particular a tool able to recognize the different devices and platforms and to merge them with processed Copernicus platforms.</p><p>While Copernicus has the main target to quickly acquire and publish data, SeaDataNet aims to publish data with the best quality available. This two data repository should be considered together, since the originator can ingest the data in both the infrastructures or only in one, or partially in both. This results sometimes in data partially available in Copernicus or SeaDataCloud, with great impact for the researcher who wants to access as much data as possible. The data reprocessing should not be loaded on researchers' shoulders, since only skilled users in all data management plan know how merge the data.</p><p>The SeaDataCloud time series data collections is a Global Ocean soon-to-be-published dataset that will represent a reference for ocean researchers, released in binary, user friendly Ocean Data View format. The database management plan was originally for profiles, but had been adapted for time series, resolving several issues like the uniqueness of the identifiers (ID).</p><p>Here we present an extension of the SOURCE (Sea Observations Utility for Reprocessing. Calibration and Evaluation) Python package, able to enhance the data quality with redundant sophisticated methods and simplify their usage. </p><p>SOURCE increases quality control (Q/C) performances on observations using statistical quality check procedures that follows the ocean best practices guidelines, exploiting the following  issues:</p><ol><li>Find and aggregate all broken time series using likeness in ID parameter strings;</li> <li>Find and organize in a dictionary all different metadata variables;</li> <li>Correct time series time to match simpler measure units;</li> <li>Filter devices that are outside of a selected horizontal rectangle;</li> <li>Give some information on original Q/C scheme by SeaDataCloud infrastructure;</li> <li>Give information tables on platforms and on the merged ID string duplicates together with an errors log file (missing time, depth, data, wrong Q/C variables, etc.).</li> </ol><p>In particular, the duplicates table and the log file may be helpful to SeaDataCloud partners in order to update the data collection and make it finally available for the users.</p><p>The reconstructed SeaDataCloud time series data, divided by parameter and stored in a more flexible dataset, give the possibility to ingest it in the main part of the software, allowing to compare it with Copernicus time series, find the same platform using horizontal and vertical surroundings (without looking to ID) find and cleanup  duplicated data, merge the two databases to extend the data coverage.</p><p>This allow researchers to have the most wide and the best quality possible data for the final users release and to to use these data to calibrate and validate models, in order to reach an idea of a whole area sea conditions.</p>

scholarly journals Sample and data sharing: Observations from a central data repository

2014 ◽  
Vol 47 (4-5) ◽  
pp. 252-257 ◽  
Author(s):  
Mary-Anne Ardini ◽  
Huaqin Pan ◽  
Ying Qin ◽  
Philip C. Cooley
Keyword(s):  
Data Sharing ◽  
Data Repository ◽  
Central Data

Internet researchers’ data sharing behaviors

2018 ◽  
Vol 42 (1) ◽  
pp. 124-142 ◽  
Author(s):  
Youngseek Kim ◽  
Seungahn Nah
Keyword(s):  
Social Norms ◽  
Data Sharing ◽  
Positive Impact ◽  
Scientific Data ◽  
Data Reuse ◽  
Equation Modeling ◽  
Data Repository ◽  
Data Repositories ◽  
Content Type ◽  
And Behaviors

Purpose The purpose of this paper is to examine how data reuse experience, attitudinal beliefs, social norms, and resource factors influence internet researchers to share data with other researchers outside their teams. Design/methodology/approach An online survey was conducted to examine the extent to which data reuse experience, attitudinal beliefs, social norms, and resource factors predicted internet researchers’ data sharing intentions and behaviors. The theorized model was tested using a structural equation modeling technique to analyze a total of 201 survey responses from the Association of Internet Researchers mailing list. Findings Results show that data reuse experience significantly influenced participants’ perception of benefit from data sharing and participants’ norm of data sharing. Belief structures regarding data sharing, including perceived career benefit and risk, and perceived effort, had significant associations with attitude toward data sharing, leading internet researchers to have greater data sharing intentions and behavior. The results also reveal that researchers’ norms for data sharing had a direct effect on data sharing intention. Furthermore, the results indicate that, while the perceived availability of data repository did not yield a positive impact on data sharing intention, it has a significant, direct, positive impact on researchers’ data sharing behaviors. Research limitations/implications This study validated its novel theorized model based on the theory of planned behavior (TPB). The study showed a holistic picture of how different data sharing factors, including data reuse experience, attitudinal beliefs, social norms, and data repositories, influence internet researchers’ data sharing intentions and behaviors. Practical implications Data reuse experience, attitude toward and norm of data sharing, and the availability of data repository had either direct or indirect influence on internet researchers’ data sharing behaviors. Thus, professional associations, funding agencies, and academic institutions alike should promote academic cultures that value data sharing in order to create a virtuous cycle of reciprocity and encourage researchers to have positive attitudes toward/norms of data sharing; these cultures should be strengthened by the strong support of data repositories. Originality/value In line with prior scholarship concerning scientific data sharing, this study of internet researchers offers a map of scientific data sharing intentions and behaviors by examining the impacts of data reuse experience, attitudinal beliefs, social norms, and data repositories together.

scholarly journals Ten principles for data sharing and commercialization

2020 ◽  
Author(s):  
Curtis L Cole ◽  
Soumitra Sengupta ◽  
Sarah Rossetti (née Collins) ◽  
David K Vawdrey ◽  
Michael Halaas ◽  
...  
Keyword(s):  
Intellectual Property ◽  
Data Sharing ◽  
Clinical Data ◽  
Medical Records ◽  
Multiple Stakeholders ◽  
Appropriate Use ◽  
Data Ownership ◽  
Data Policy ◽  
Conflicting Messages

Abstract Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.

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