FAIR Convergence Matrix: Optimizing the Reuse of Existing                     FAIR-Related Resources

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.

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Influencers and Major Themes in a Gifted Education Community of Practice on Twitter

10.35542/osf.io/czhj8 ◽

2021 ◽

Author(s):

Erin Morris Miller ◽

Jennifer Jolly ◽

Jacqueline Latz ◽

Kelsi Listman

Keyword(s):

Community Of Practice ◽

Gifted Education ◽

Past Research ◽

Sources Of Information ◽

False Information ◽

Education Professionals ◽

Education Community ◽

The Matrix ◽

Online Sources ◽

Insight Into

Twitter is a valuable resource for research because of its potential to provide insight into public opinions as well as identify whom are the most influential sources of information. However, false information spreads easily on Twitter and parents often trust online sources even when those ideas run counter to the information from professionals (Külhaş Çelik et al., 2019). Convergent mixed methods exploratory analysis was used to evaluate the discourse on Twitter regarding gifted education. The results of the quantitative analysis of the matrix of connections using NodeXL Pro indicate that gifted education Twitter is a cohesive network and that the top influencers are organizations and educational consultants. The qualitative analyses suggest that gifted education professionals and parents engage in Twitter in similar ways to what has been described in past research: to share interesting articles and videos, to discuss current issues, promote one’s ideas, and seek opportunities.

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Supporting Mixed-domain Mixed-precision Matrix Multiplication within the BLIS Framework

ACM Transactions on Mathematical Software ◽

10.1145/3402225 ◽

2021 ◽

Vol 47 (2) ◽

pp. 1-26

Author(s):

Field G. Van Zee ◽

Devangi N. Parikh ◽

Robert A. Van De Geijn

Keyword(s):

High Performance ◽

Matrix Multiplication ◽

Software Framework ◽

Matrix Product ◽

Double Precision ◽

Precision Matrix ◽

Implementation Approach ◽

Mixed Precision ◽

The Matrix ◽

Performance Results

We approach the problem of implementing mixed-datatype support within the general matrix multiplication ( gemm ) operation of the BLAS-like Library Instantiation Software framework, whereby each matrix operand A , B , and C may be stored as single- or double-precision real or complex values. Another factor of complexity, whereby the matrix product and accumulation are allowed to take place in a precision different from the storage precisions of either A or B , is also discussed. We first break the problem into orthogonal dimensions, considering the mixing of domains separately from mixing precisions. Support for all combinations of matrix operands stored in either the real or complex domain is mapped out by enumerating the cases and describing an implementation approach for each. Supporting all combinations of storage and computation precisions is handled by typecasting the matrices at key stages of the computation—during packing and/or accumulation, as needed. Several optional optimizations are also documented. Performance results gathered on a 56-core Marvell ThunderX2 and a 52-core Intel Xeon Platinum demonstrate that high performance is mostly preserved, with modest slowdowns incurred from unavoidable typecast instructions. The mixed-datatype implementation confirms that combinatorial intractability is avoided, with the framework relying on only two assembly microkernels to implement 128 datatype combinations.

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Instagram as a Special Educator Professional Development Tool: A Guide to Teachergram

Journal of Special Education Technology ◽

10.1177/01626434211033596 ◽

2021 ◽

pp. 016264342110335

Author(s):

Jennifer Ryan Newton ◽

Mira Cole Williams

Keyword(s):

Professional Development ◽

Community Of Practice ◽

Special Education Teachers ◽

Service Providers ◽

Special Educator ◽

Affinity Spaces ◽

Development Tool ◽

Online Social Media ◽

Related Service ◽

Broad Community

Instagram is a free, online social media application that facilitates social networking. Since Instagram is image dependent, educators create visuals accompanied by captions of up to 2,200 characters. By adding specific hashtags to captions, educator posts are curated by the algorithm into a broad community of practice, colloquially known as “Teachergram.” As a technology-facilitated PD tool, Teachergram lends itself to many of the characteristics of high quality and sustainable professional development (PD). In this paper, we focus on how Instagram can be used as a PD tool that supports collaboration, reflection, and feedback loops of educators. We focus on how the unique characteristics of Teachergram lead to the creation of communities and affinity spaces, aligned with existing PD frameworks, built to support the retention of special education teachers and related service providers.

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Reusing Data Technical and Ethical Challenges

DESIDOC Journal of Library & Information Technology ◽

10.14429/djlit.39.06.14807 ◽

2019 ◽

Vol 39 (06) ◽

pp. 329-337

Author(s):

Juan-José Boté ◽

Miquel Termens

Keyword(s):

Data Privacy ◽

Data Reuse ◽

Ethical Challenges ◽

Different Types ◽

Management Plans ◽

Fair Principles ◽

Research Questions ◽

Public Organisations ◽

New Research ◽

Research Centres

Research centres, universities and public organisations create datasets that can be reused in research. Reusing data makes it possible to reproduce studies, generate new research questions and new knowledge, but it also gives rise to technical and ethical challenges. Part of these issues are repositories interoperability to accomplish FAIR principles or issues related to data privacy or anonymity. At the same time, funding institutions require that data management plans be submitted for grants, and research tends to be increasingly interdisciplinary. Interdisciplinarity may entail barriers for researchers to reuse data, such as a lack of skills to manipulate data, given that each discipline generates different types of data in different technical formats, often non-standardized. Additionally, the use of standards to validate data reuse and better metadata to find appropriate datasets seem necessary. This paper offers a review of the literature that addresses data reuse in terms of technical, ethical-related issues.

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FAIR Forever? Accountabilities and Responsibilities in the Preservation of Research Data

International Journal of Digital Curation ◽

10.2218/ijdc.v16i1.768 ◽

2021 ◽

Vol 16 (1) ◽

pp. 16

Author(s):

Amy Currie ◽

William Kilbride

Keyword(s):

Community Of Practice ◽

Working Group ◽

Digital Preservation ◽

Open Science ◽

Research Data ◽

Final Report ◽

Research Organizations ◽

Fair Principles ◽

Science And Research

Digital preservation is a fast-moving and growing community of practice of ubiquitous relevance, but in which capability is unevenly distributed. Within the open science and research data communities, digital preservation has a close alignment to the FAIR principles and is delivered through a complex specialist infrastructure comprising technology, staff and policy. However, capacity erodes quickly, establishing a need for ongoing examination and review to ensure that skills, technology, and policy remain fit for changing purpose. To address this challenge, the Digital Preservation Coalition (DPC) conducted the FAIR Forever study, commissioned by the European Open Science Cloud (EOSC) Sustainability Working Group and funded by the EOSC Secretariat Project in 2020, to assess the current strengths, weaknesses, opportunities and threats to the preservation of research data across EOSC, and the feasibility of establishing shared approaches, workflows and services that would benefit EOSC stakeholders. This paper draws from the FAIR Forever study to document and explore its key findings on the identified strengths, weaknesses, opportunities, and threats to the preservation of FAIR data in EOSC, and to the preservation of research data more broadly. It begins with background of the study and an overview of the methodology employed, which involved a desk-based assessment of the emerging EOSC vision, interviews with representatives of EOSC stakeholders, and focus groups with digital preservation specialists and data managers in research organizations. It summarizes key findings on the need for clarity on digital preservation in the EOSC vision and for elucidation of roles, responsibilities, and accountabilities to mitigate risks of data loss, reputation, and sustainability. It then outlines the recommendations provided in the final report presented to the EOSC Sustainability Working Group. To better ensure that research data can be FAIRer for longer, the recommendations of the study are presented with discussion on how they can be extended and applied to various research data stakeholders in and outside of EOSC, and suggest ways to bring together research data curation, management, and preservation communities to better ensure FAIRness now and in the long term.

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A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences

Nanomaterials ◽

10.3390/nano10102068 ◽

2020 ◽

Vol 10 (10) ◽

pp. 2068 ◽

Cited By ~ 2

Author(s):

Ammar Ammar ◽

Serena Bonaretti ◽

Laurent Winckers ◽

Joris Quik ◽

Martine Bakker ◽

...

Keyword(s):

Gene Expression ◽

Data Sharing ◽

Life Sciences ◽

Scientific Progress ◽

Gene Expression Omnibus ◽

Data Reuse ◽

Fair Principles ◽

Computational Workflow ◽

Broad Interpretation ◽

Scientific Questions

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.

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Exploring the current practices, costs and benefits of FAIR Implementation in pharmaceutical Research and Development: A Qualitative Interview Study

Data Intelligence ◽

10.1162/dint_a_00109 ◽

2021 ◽

pp. 1-14

Author(s):

Ebtisam Alharbi ◽

Rigina Skeva ◽

Nick Juty ◽

Caroline Jay ◽

Carole Goble

Keyword(s):

Research And Development ◽

Cultural Change ◽

Scientific Evidence ◽

Scientific Data ◽

Data Reuse ◽

Potential Contribution ◽

Costs And Benefits ◽

Structured Interviews ◽

Qualitative Interview Study ◽

Fair Principles

Abstract The findable, accessible, interoperable, reusable (FAIR) principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines. Research and development (R&D) in the pharmaceutical industry is becoming increasingly data driven, but managing its data assets according to FAIR principles remains costly and challenging. To date, little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of datasets in pharmaceutical R&D. This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&D in 7 pharmaceutical businesses. Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification, and the elements that influence the decision-making process for FAIRifying legacy datasets. Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings. Implementation costs, however, were still considered a barrier by participants, with the need for considerable expenditure in terms of resources, and cultural change. How decisions were made about FAIRification was influenced by legal and ethical considerations, management commitment, and data prioritisation. The findings have significant implications for those in the pharmaceutical R&D industry who are engaged in driving FAIR implementation, and for external parties who seek to better understand existing practices and challenges.

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Two real use cases of FAIR maturity indicators in the life sciences

10.1101/739334 ◽

2019 ◽

Cited By ~ 1

Author(s):

Serena Bonaretti ◽

Egon Willighagen

Keyword(s):

Life Sciences ◽

Scientific Progress ◽

Gene Expression Omnibus ◽

Data Reuse ◽

Use Cases ◽

Fair Principles ◽

Computational Workflow ◽

Broad Interpretation ◽

Program Interface ◽

Scientific Questions

AbstractData sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult for lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated our method on two real use cases where researchers looked for datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress and Gene Expression Omnibus), a registry of repositories (re3data.org), and a searchable resource (Google Dataset Search) via application program interface (API) wherever possible. With our analysis, we found that the two datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and presence of specific attributes in registries of repositories could increase FAIRness of datasets.

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Using standards to model delayed mode sensor processes

10.5194/egusphere-egu2020-15466 ◽

2020 ◽

Author(s):

Alexandra Kokkinaki ◽

Justin Buck ◽

Emma Slater ◽

Julie Collins ◽

Raymond Cramer ◽

...

Keyword(s):

Scientific Discovery ◽

Data Reuse ◽

Sensor Data ◽

Easy Access ◽

Data Provenance ◽

Fair Principles ◽

Data Lifecycle ◽

Data Transferring ◽

The Eu ◽

Ogc Standards

Ocean data are expensive to collect. Data reuse saves time and accelerates the pace of scientific discovery. For data to be re-usable the FAIR principles reassert the need for rich metadata and documentation that meet relevant community standards and provide information about provenance.Approaches on sensor observations, are often inadequate at meeting FAIR; prescriptive with a limited set of attributes, while providing little or no provision for really important metadata about sensor observations later in the data lifecycle.As part of the EU ENVRIplus project, our work aimed at capturing the delayed mode, data curation process taking place at the National Oceanography Centre&#8217;s British Oceanography Data Centre (BODC). Our solution uses Unique URIs, OGC SWE standards and controlled vocabularies, commencing from the submitted originators input and ending by the archived and published dataset.&#160;The BODC delayed mode process is an example of a physical system that is composed of several components like sensors and other computations processes such as an algorithm to compute salinity or absolute winds. All components are described in sensorML identified by unique URIs and associated with the relevant datastreams, which in turn are exposed on the web via ERDDAP using unique URIs.In this paper we intend to share our experience in using OGC standards and ERDDAP to model the above mentioned process and publish the associated datasets in a unified way. The benefits attained, allow greater automation of data transferring, easy access to large volumes of data from a chosen sensor, more precise capturing of data provenance, standardization, and pave the way towards greater FAIRness of the sensor data and metadata, focusing on the delayed mode processing.

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Can smart policies solve the sand mining problem?

PLoS ONE ◽

10.1371/journal.pone.0248882 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0248882

Author(s):

Michael Hübler ◽

Frank Pothen

Keyword(s):

Land Reclamation ◽

Economic Costs ◽

Sand Mining ◽

Global Challenge ◽

Implementation Approach ◽

Essential Resource ◽

Trading Scheme ◽

Sand Extraction ◽

Policy Solutions ◽

Model Based Analysis

While sand has become a scarce essential resource for construction and land reclamation worldwide, its extraction causes severe ecological damage and high social costs. To derive policy solutions to this paramount global challenge with broad applicability, this model-based analysis exemplarily studies sand trade from Southeast Asia to Singapore. Accordingly, a coordinated transboundary sand output tax reduces sand mining to a large extent, while the economic costs are small for the sand importer and slightly positive for the exporters. As a novel policy implementation approach, a “Sand Extraction Allowances Trading Scheme” is proposed, which helps sustainably balance the importer’s economic growth with the exporters’ economic development.

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