Merit Review Policy – [U of Maryland, Psychology Department]

“Examples of specific evaluative criteria to be used in merit review, based on professional standards for evaluating faculty performance….Openness and transparency: Degree to which research, data, procedures, code, and research products are made openly available where appropriate; the use of registered reports or pre-registration. Committee should recognize that researchers may not be able to share some types of data, such as when data are proprietary or subject to ethical concerns over confidentiality[7, 1, 6, 2, 5] These limitations should be documented by faculty.”

 

Management and maintenance of research data by researchers in Zimbabwe | Emerald Insight

Abstract:  Purpose

The concept of research data management (RDM) is new in Zimbabwe and other developing countries. Research institutions are developing research data repositories and promoting the archiving of research data. As a way of creating awareness to researchers on RDM, the purpose of this paper is to determine how researchers are managing their research data and whether they are aware of the developments that are taking place in RDM.

Design/methodology/approach

A survey using a mixed method approach was done and an online questionnaire was administered to 100 researchers in thirty research institutions in Zimbabwe. Purposive sampling was done by choosing participants from the authors of articles published in journals indexed by Google Scholar, Scopus and Web of Science. Interviews were done with five top researchers. The data was analysed using NVIVO. The results were presented thematically. The questionnaire was distributed using the research offices of the selected 30 research institutions. There was a 75% response rate.

Findings

The findings indicated that all the researchers are aware of the traditional way of managing research data. A total of 70% of the respondents are not aware of the current trends in RDM services, as they are keeping their data on machines and external hard drives, while 97.3% perceive RDM services as useful, as it is now a requirement when applying for research grants. Librarians have a bigger role to play in creating awareness on RDM among researchers and hosting the data repositories for archiving research data.

Practical implications

Research institutions can invest in research data services and develop data repositories. Librarians will participate in educating researchers to come up with data management plans before they embark on a research project. This study also helps to showcase the strategies that can be used in awareness creation campaigns. The findings can also be used in teaching RDM in library schools and influence public policy both at institutional and national level.

Social implications

This study will assist in building capacity among stakeholders about RDM. Based on the findings, research institutions should prioritise research data services to develop skills and knowledge among librarians and researchers.

Originality/value

Few researches on RDM practices in Zimbabwe were done previously. Most of the papers that were published document the perception of librarians towards RDM, but this study focused mainly on researchers’ awareness and perception. The subject is still new and people are beginning to research on it and create awareness amongst the stakeholders in Zimbabwe.

Using current research information systems to investigate data acquisition and data sharing practices of computer scientists – Antti Mikael Rousi, 2022

Abstract:  Without sufficient information about research data practices occurring in a particular research organisation, there is a risk of mismatching research data service efforts with the needs of its researchers. This study describes how data acquiring and data sharing occurring within a particular research organisation can be investigated by using current research information system publication data. The case study organisation’s current research information system was used to identify the sample of investigated articles. A sample of 193 journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a classification of the main study types was developed to accommodate the multidisciplinary nature of the case department’s research agenda. Furthermore, a coding framework was developed to capture the key elements of data acquiring and data sharing. The articles representing life sciences and computational research relatively frequently reused open data, whereas data acquisition of experimental research, human interaction studies and human intervention studies often relied on collecting original data. Data sharing also differed between the computationally intensive study types of life sciences and computational research and the study types relying on collection of original data. Research data were not available for reuse in only a minority of life science (n?=?2; 7%) and computational research (n?=?15; 14%) studies. The study types of experimental research, human interaction studies and human intervention studies less frequently made their data available for reuse. The findings suggest that research organisations representing computer sciences may include different subfields that have their own cultures of data sharing. This study demonstrates that analyses of publications listed in current research information systems provide detailed descriptions how the affiliated researchers acquire and share research data.

Supporting knowledge creation and sharing by building a standardised interconnected repository of biodiversity data | Zenodo

“This EOSC in practice story was developed within the Cos4cloud project and targets a very wide user base as it is addressed to any researchers, teachers, students, companies, institutions and, more generally, anyone interested in knowing, studying or analysing biodiversity information.

The story presents Cos4Bio, a co-designed, interoperable and open-source service that integrates biodiversity observations from multiple citizen observatories in one place, allowing experts to save time in the species identification process and get access to an enormous number of biodiversity observations. This resource is available on the EOSC Portal Catalogue and Marketplace …”

Supporting knowledge creation and sharing by building a standardised interconnected repository of biodiversity data | EOSC Portal

“This EOSC in practice story targets a very wide user base as it is addressed to any researchers, teachers, students, companies, institutions and, more generally, anyone interested in knowing, studying or analysing biodiversity information. It was developed within the Cos4cloud project….

Cos4Bio is a co-designed, interoperable and open-source service that integrates biodiversity observations from multiple citizen observatories in one place, allowing experts to save time in the species identification process and get access to an enormous number of biodiversity observations….”

Gearing Up for 2023 Part II: Implementing the NIH Data Management and Sharing Policy – NIH Extramural Nexus

“NIH has a long history of developing consent language and, as such, our team worked across the agency – and with you! – to develop a new resource that shares best practices for developing informed consents to facilitate data/biospecimen storage and sharing for future use.  It also provides modifiable sample language that investigators and IRBs can use to assist in the clear communication of potential risks and benefits associated with data/biospecimen storage and sharing.  In developing this resource, we engaged with key federal partners, as well as scientific societies and associations.  Importantly, we also considered the 102 comments from stakeholders in response to a RFI that we issued in 2021.

As for our second resource, we are requesting public comment on protecting the privacy of research participants when data is shared. I think I need to be upfront and acknowledge that we have issued many of these types of requests over the last several months and NIH understands the effort that folks take to thoughtfully respond.  With that said, we think the research community will greatly benefit from this resource and we want to hear your thoughts on whether it hits the mark or needs adjustment….”

DataWorks! Challenge | HeroX

“Share your story of how you reused or shared data to further your biological and/or biomedical research effort and get recognized!…

The Federation of American Societies for Experimental Biology (FASEB) and the National Institutes of Health (NIH) are championing a bold vision of data sharing and reuse. The DataWorks! Prize fuels this vision with an annual challenge that showcases the benefits of research data management while recognizing and rewarding teams whose research demonstrates the power of data sharing or reuse practices to advance scientific discovery and human health. We are seeking new and innovative approaches to data sharing and reuse in the fields of biological and biomedical research. 

To incentivize effective practices and increase community engagement around data sharing and reuse, the 2022 DataWorks! Prize will distribute up to 12 monetary team awards. Submissions will undergo a two-stage review process, with final awards selected by a judging panel of NIH officials. The NIH will recognize winning teams with a cash prize, and winners will share their stories in a DataWorks! Prize symposium.”

A comparison of scientometric data and publication policies of ophthalmology journals

Abstract: Purpose: 

This retrospective database analysis study aims to present the scientometric data of journals publishing in the field of ophthalmology and to compare the scientometric data of ophthalmology journals according to the open access (OA) publishing policies.

Methods: 

The scientometric data of 48 journals were obtained from Clarivate Analytics InCites and Scimago Journal & Country Rank websites. Journal impact factor (JIF), Eigenfactor score (ES), scientific journal ranking (SJR), and Hirsch index (HI) were included. The OA publishing policies were separated into full OA with publishing fees, full OA without fees, and hybrid OA. The fees were stated as US dollars (USD).

Results: 

Four scientometric indexes had strong positive correlations; the highest correlation coefficients were observed between the SJR and JIF (R = 0.906) and the SJR and HI (R = 0.798). However, journals in the first quartile according to JIF were in the second and third quartiles according to the SJR and HI and in the fourth quartile in the ES. The OA articles published in hybrid journals received a median of 1.17-fold (0.15–2.71) more citations. Only HI was higher in hybrid OA; other scientometric indexes were similar with full OA journals. Full OA journals charged a median of 1525 USD lower than hybrid journals.

Conclusion: 

Full OA model in ophthalmology journals does not have a positive effect on the scientometric indexes. In hybrid OA journals, choosing to publish OA may increase citations, but it would be more accurate to evaluate this on a journal basis.

Frontiers | The Academic, Societal and Animal Welfare Benefits of Open Science for Animal Science | Veterinary Science

Abstract:  Animal science researchers have the obligation to reduce, refine, and replace the usage of animals in research (3R principles). Adherence to these principles can be improved by transparently publishing research findings, data and protocols. Open Science (OS) can help to increase the transparency of many parts of the research process, and its implementation should thus be considered by animal science researchers as a valuable opportunity that can contribute to the adherence to these 3R-principles. With this article, we want to encourage animal science researchers to implement a diverse set of OS practices, such as Open Access publishing, preprinting, and the pre-registration of test protocols, in their workflows.

 

KU LEUVEN CENTRE FOR IT & IP LAW: RESEARCH FELLOW (M/F – SENIOR/JUNIOR) WITH FOCUS ON IP LAW

“As a legal research fellow you will assist the PI(s) in the analysis, impact and opportunities of IP regulations, in particular copyright, in relation to a variety of topics/projects including Open Science, Open Data/PSI, trade secrets, and more generally to emerging issues of data governance from a property/IP perspective. You will be assisting in research projects either as a researcher or as a coordinator (depending on junior/senior) in the aforementioned areas employing a wide range of methodological approaches (case scenarios, EU comparative analysis, historical development of legal regulations, etc) and you should be able to develop their work in close collaboration with the PI, but also show the ability to perform specific tasks with a certain degree of autonomy.  As the research may also develop in other areas of law (e.g. fundamental rights, competition law, consumer protection, property law, unfair competition), your familiarity with (some) of these topics will be an asset duly considered….”

Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

Abstract:  From a research data repositories’ perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined).

The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta)data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level.

We find that while neither one of the five valuation approaches is fully fit-for-purpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches.

Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts.

Welcome to Hotel Elsevier: you can check-out any time you like … not – Eiko Fried

“Luckily, folks over at Elsevier “take your privacy and trust in [them] very seriously”, so we used the Elsevier Privacy Support Hub to start an “access to personal information” request. Being in the EU, we are legally entitled under the European General Data Protection Regulation (GDPR) to ask Elsevier what data they have on us, and submitting this request was easy and quick.

After a few weeks, we both received responses by email. We had been assigned numbers 0000034 and 0000272 respectively, perhaps implying that relatively few people have made use of this system yet. The emails contained several files with a wide range of our data, in different formats. One of the attached excel files had over 700,000 cells of data, going back many years, exceeding 5mb in file size. We want to talk you through a few examples of what Elsevier knows about us….

To start with, of course they have information we have provided them with in our interactions with Elsevier journals: full names, academic affiliations, university e-mail addresses, completed reviews and corresponding journals, times when we declined review requests, and so on.

Apart from this, there was a list of IP addresses. Checking these IP addresses identified one of us in the small city we live in, rather than where our university is located. We also found several personal user IDs, which is likely how Elsevier connects our data across platforms and accounts. We were also surprised to see multiple (correct) private mobile phone numbers and e-mail addresses included….

And there is more. Elsevier tracks which emails you open, the number of links per email clicked, and so on….

We also found our personal address and bank account details, probably because we had received a small payment for serving as a statistical reviewer1. These €55 sure came with a privacy cost larger than anticipated.

Data called “Web Traffic via Adobe Analytics” appears to list which websites we visited, when, and from which IP address. “ScienceDirect Usage Data” contains information on when we looked at which papers, and what we did on the corresponding website. Elsevier appears to distinguish between downloading or looking at the full paper and other types of access, such as looking at a particular image (e.g. “ArticleURLrequestPage”, “MiamiImageURLrequestPage”, and “MiamiImageURLreadPDF”), although it’s not entirely clear from the data export. This leads to a general issue that will come up more often in this piece: while Elsevier shared what data they have on us, and while they know what the data mean, it was often unclear for us navigating the data export what the data mean. In that sense, the usefulness of the current data export is, at least in part, questionable. In the extreme, it’s a bit like asking google what they know about you and they send you a file full of special characters that have no meaning to you….”

 

 

Open Science in the recently adopted Resolution on the Slovenian Scientific Research and Innovation Strategy 2030 – OpenAIRE Blog

“At the end of March 2022, the National Assembly of the Republic of Slovenia adopted the Resolution on the Slovenian Scientific Research and Innovation Strategy 2030 which was published in the Official Gazette of the Republic of Slovenia in April 2022. This is a key Slovenian strategic document for research and innovation until 2030, which will be the basis for formulating policies related to social and economic development as well as to societal challenges. The Scientific Research and Innovation Strategy is inextricably intertwined with the Resolution on National Programme of Higher Education 2030, and both are harmonized with the Slovenian Development Strategy 2030. At the implementation level, the Scientific Research and Innovation Strategy will be supplemented by action plans and sectoral strategic documents (e.g., Research Infrastructure Development Plan, Open Science Action Plan, Action Plan for Technology Transfer Offices, Equal Opportunities Action Plan), which will define the set goals and upgrade them with measurable monitoring indicators.

The Scientific Research and Innovation Strategy, which is based on the Article 10 of the new Scientific Research and Innovation Activities Act, introduces Open Science as an important integral part of the scientific research. In the context of the horizontal objectives under Item 6.2., the Strategy defines six key measures in the field of Open Science, as follows:

1. Effective management and financing of the development of the national Open Science ecosystem and related national structures and infrastructures, ensuring their international alignment as well as integration into international associations and infrastructures.

2. Introduction of modern approaches to the evaluation of scientific research activity in accordance with Open Science principles to increase the quality and impact of research (e.g., DORA – San Francisco Declaration on Research Assessment, Leiden Manifesto for Research Metrics, ERAC – European Research Area and Innovation Committee Guidelines).

3. Ensuring that the results of scientific research comply with the FAIR principles (Findable, Accessible, Interoperable, and Reusable), and that full and immediate open access is provided (subject to legitimate exceptions).

4. Establishment of a National Open Science Community for the introduction and monitoring of Open Science in Slovenia, as well as its integration into ERA and beyond.

5. Promoting the development of citizen science and public involvement in scientific research.

6. Promoting the development of a national scientific publishing system to operate according to the principles of Open Science.

These measures, which are recognised as essential for the development of Open Science in Slovenia, are the basis for the Action Plan on Open Science in preparation. Its adoption by the Government of the Republic of Slovenia is expected later this year.”

 

An Open Science Roadmap for Swedish Higher Education Institutions | Nordic Perspectives on Open Science

Abstract:  In the spring of 2021, a National Open Science Roadmap for Swedish Higher Education Institutions (HEI) was adopted by The Association of Swedish HEIs. The roadmap’s eight principles aim to guide the HEIs’ development of local structures and processes, speed up their concrete actions and encourage their collaboration in the shift to Open Science. The recommendations are concentrated on specific measures for open access to research data and research publications at HEIs. The primary target group for the roadmap is university management at Swedish HEIs. In the spring of 2022 the roadmap is to be supplemented by an action plan for Open Science.