This collection contains five sets of datasets: 1) Publication counts from two multidisciplinary humanities data journals: the Journal of Open Humanities Data and Research Data in the Humanities and Social Sciences (RDJ_JOHD_Publications.csv); 2) A large dataset about the performance of research articles in HSS exported from dimensions.ai (allhumss_dims_res_papers_PUB_ID.csv); 3) A large dataset about the performance of datasets in HSS harvested from the Zenodo REST API (Zenodo.zip); 4) Impact and usage metrics from the papers published in the two journals above (final_outputs.zip); 5) Data from Twitter analytics on tweets from the @up_johd account, with paper DOI and engagement rate (twitter-data.zip).

Please note that, as requested by the Dimensions team, for 2 and 4, we only included the Publication IDs from Dimensions rather than the full data. Interested parties only need the Dimensions publications IDs to retrieve the data; even if they have no Dimensions subscription, they can easily get a no-cost agreement with Dimensions, for research purposes, in order to retrieve the data.

BackgroundThere is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and ResultsWe reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. ConclusionA substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.

This data paper template refers to the national standards Data Paper Publishing Metadata (GB/T 42813-2023) and Academic Paper Writing Rules (GB/T 7713.2-2022), and also investigates and to some extent refers to the paper templates of domestic and foreign journals that publish data papers.

Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inclusions from multimedia, collaboration, and network visualization, for the years 2021-2022. Based on a derived index, long-term preservation and data sharing are evaluated for each venue. The index ranges from No policy to Required sharing and preservation. Additionally the verbatim statements (or the lack thereof) used to reach the concluded score are also provided. Abstract: Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

I provided my all raw data related to research study that is freely accessible to reviewer, readers and all other scientific community.

This is the dataset for the paper "Are data papers cited as research data? Preliminary analysis on interdisciplinary data paper citations" submitted to iConference 2025.

Here are some pictures and tables related to the process of the paper

Question Paper Solutions of chapter Classification and Presentation of Data of Research Methodology, 5th Semester , Bachelor of Business Administration

This publication contains the raw data as well as the evaluation scrips (written in R) of the paper "Formation of Study Groups: Exploring Students' Needs and Practical Challenges".

The evaluation data was collected using the software that is published in https://doi.org/10.5281/zenodo.10678081.

Abstract: Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai’i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science’s (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai’i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable.

This is the Altmetric.com data for the set of journal articles used in this research. The data was provided by Altmetric.com, a research metrics company who track and collect the online conversations around millions of scholarly outputs. Altmetric continually monitors a variety of non-traditional sources to provide real-time updates on new mentions and shares of individual research outputs, which are collated and presented to users via Altmetric.com. The data was collated on the 15/08/2016. Any subsequent adjustments to the original data have been made by Dr Lauren Cadwallader and are fully explained in the document.

as well as the parameters of the test system.

This dataset contains the supplementary data for the research paper "Haploinsufficiency of the intellectual disability gene SETD5 disturbs developmental gene expression and cognition".

The contained files have the following content: 'Supplementary Figures.pdf' Additional figures (as referenced in the paper). 'Supplementary Table 1. Statistics.xlsx' Details on statistical tests performed in the paper. 'Supplementary Table 2. Differentially expressed gene analysis.xlsx' Results for the differential gene expression analysis for embryonic (E9.5; analysis with edgeR) and in vitro (ESCs, EBs, NPCs; analysis with DESeq2) samples. 'Supplementary Table 3. Gene Ontology (GO) term enrichment analysis.xlsx' Results for the GO term enrichment analysis for differentially expressed genes in embryonic (GO E9.5) and in vitro (GO ESC, GO EBs, GO NPCs) samples. Differentially expressed genes for in vitro samples were split into upregulated and downregulated genes (up/down) and the analysis was performed on each subset (e.g. GO ESC up / GO ESC down). 'Supplementary Table 4. Differentially expressed gene analysis for CFC samples.xlsx' Results for the differential gene expression analysis for samples from adult mice before (HC - Homecage) and 1h and 3h after contextual fear conditioning (1h and 3h, respectively). Each sheet shows the results for a different comparison. Sheets 1-3 show results for comparisons between timepoints for wild type (WT) samples only and sheets 4-6 for the same comparisons in mutant (Het) samples. Sheets 7-9 show results for comparisons between genotypes at each time point and sheet 10 contains the results for the analysis of differential expression trajectories between wild type and mutant. 'Supplementary Table 5. Cluster identification.xlsx' Results for k-means clustering of genes by expression. Sheet 1 shows clustering of just the genes with significantly different expression trajectories between genotypes. Sheet 2 shows clustering of all genes that are significantly differentially expressed in any of the comparisons (includes also genes with same trajectories). 'Supplementary Table 6. GO term cluster analysis.xlsx' Results for the GO term enrichment analysis and EWCE analysis for enrichment of cell type specific genes for each cluster identified by clustering genes with different expression trajectories (see Table S5, sheet 1). 'Supplementary Table 7. Setd5 mass spectrometry results.xlsx' Results showing proteins interacting with Setd5 as identified by mass spectrometry. Sheet 1 shows protein protein interaction data generated from these results (combined with data from the STRING database. Sheet 2 shows the results of the statistical analysis with limma. 'Supplementary Table 8. PolII ChIP-seq analysis.xlsx' Results for the Chip-Seq analysis for binding of RNA polymerase II (PolII). Sheet 1 shows results for differential binding of PolII at the transcription start site (TSS) between genotypes and sheets 2+3 show the corresponding GO enrichment analysis for these differentially bound genes. Sheet 4 shows RNAseq counts for genes with increased binding of PolII at the TSS.

This folder contains data underlying the research paper “Exploring potential contributions of open data intermediaries”. The research is about open data ecosystem and the role of open data intermediaries. The folder consists of 4 items:

1. Tentative interview questions (.pdf and .odt formats)

2. Informed consent form template (for verbal interview & written interview) (.pdf and .odt formats)

3. De-identified interview transcripts (.pdf and .odt formats)

4. Coding results (.pdf and .ods formats)

Note about the tentative interview questions:

The interviews were conducted between May and July 2023 based on the semi-structured approach. We customise the tentative interview questions accordingly for each interview and share them with the interviewees in advance (for the majority, at least three working days in advance). As semi-structured interviews, the ultimate interview questions may differ from the tentative questions based on the information provided by the interviewees and time constraints (refer to item #3).

Note about the informed consent form:

We sent the informed consent form to every interviewee in advance and requested them to return it to us before the interview. The consent form has been reviewed by TU Delft's Human Research Ethics Committee (HREC).

Note about the de-identified interview transcripts (and coding results):

The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of open data intermediaries. We removed personally identifiable information from the transcripts. A few interviewees may risk being identifiable if their organisation is known. Hence, we removed the identification of the organisation and country in all transcripts. Partially disclosing the organisation or country for some transcripts increases the risks of identifying the non-disclosed transcripts. With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcript as close to verbatim as possible. All interviewees whose interview transcripts are recorded in this document give permission for the anonymised transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.

Acknowledgement:

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the Aurora Universities Network. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.

17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs.

The survey contains the following questions (see inside dataset for exact wording):

• Are you familiar with this SDG?
  • Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.
• Suggest research papers that are relevant for this SDG (upload list)
  • This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)
• Select research papers that are relevant for this SDG (radio buttons: accept, reject)
  • A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.
• Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Suggest SDG related glossaries with relevant keywords (text fields: url)
  • Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.
• Suggest improvements for the current queries (text field: suggestions per target)
  • We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.

In the dataset root you'll find the following folders and files:

• /00-survey-input/
  • This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.
• /01-raw-data/
  • This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.
• /02-aggregated-data/
  • This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.
• /03-scripts/
  • This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.
• /04-processed-data/
  • This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.
• /images/
  • images of the results used in this README.md.
• LICENSE.md
  • terms and conditions for reusing this data.
• README.md
  • description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:

• SDG-survey-questions.pdf
  • This file contains the survey questions

  </li>
  <li><strong>SDG-survey-questions.doc</strong>
  <ul>
    <li>This file contains the survey questions</li>
  </ul>
  </li>
  <li><strong>SDG-survey-respondents-per-sdg.csv</strong>
  <ul>
    <li>Basic information about the survey and responses</li>
  </ul>
  </li>
  <li><strong>SDG-survey-city-heatmap.csv</strong>
  <ul>
    <li>Origin of the respondents per SDG survey</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications.txt</strong>
  <ul>
    <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong>
  <ul>
    <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-accepted.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-rejected.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-keywords.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-keywords.csv</strong>
  <ul>
    <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-keywords.csv</strong>
  <ul>
    <li>List of keywords researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-glossaries.csv</strong>
  <ul>
    <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-journals.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-journals.csv</strong>
  <ul>
    <li>As "selected-journals", this is the list of journals
  

COVID-19++ is a citation-aware COVID-19 dataset for the analysis of research dynamics. In addition to primary COVID-19 related articles and preprints from 2020, it includes citations and the metadata of first-order cited work. All publications are annotated with MeSH terms, either from the ground truth, or via ConceptMapper, if no ground truth was available.

The data is organized in CSV files

• Paper metadata (paper_id, publdate, title, data_source): paper.csv

• Annotation data, mapping paper_id to MeSH terms: annotation.csv

• Authorship data, mapping paper_id to author, optionally with ORCID: authorship.csv

• Paired DOIs of citing and cited papers: references.csv

The column data source within the paper metadata has the value KE (for metadata from ZB MED KE), PP (for preprints) or CR (for cited resources from CrossRef)

This work was supported by BMBF within the programme ``Quantitative Wissenschaftsforschung'' under grant numbers 01PU17013A, 01PU17013B, 01PU17013C.

BackgroundAn understanding of the resources which engineering students use to write their academic papers provides information about student behaviour as well as the effectiveness of information literacy programs designed for engineering students. One of the most informative sources of information which can be used to determine the nature of the material that students use is the bibliography at the end of the students’ papers. While reference list analysis has been utilised in other disciplines, few studies have focussed on engineering students or used the results to improve the effectiveness of information literacy programs. Gadd, Baldwin and Norris (2010) found that civil engineering students undertaking a finalyear research project cited journal articles more than other types of material, followed by books and reports, with web sites ranked fourth. Several studies, however, have shown that in their first year at least, most students prefer to use Internet search engines (Ellis & Salisbury, 2004; Wilkes & Gurney, 2009).PURPOSEThe aim of this study was to find out exactly what resources undergraduate students studying civil engineering at La Trobe University were using, and in particular, the extent to which students were utilising the scholarly resources paid for by the library. A secondary purpose of the research was to ascertain whether information literacy sessions delivered to those students had any influence on the resources used, and to investigate ways in which the information literacy component of the unit can be improved to encourage students to make better use of the resources purchased by the Library to support their research.DESIGN/METHODThe study examined student bibliographies for three civil engineering group projects at the Bendigo Campus of La Trobe University over a two-year period, including two first-year units (CIV1EP – Engineering Practice) and one-second year unit (CIV2GR – Engineering Group Research). All units included a mandatory library session at the start of the project where student groups were required to meet with the relevant faculty librarian for guidance. In each case, the Faculty Librarian highlighted specific resources relevant to the topic, including books, e-books, video recordings, websites and internet documents. The students were also shown tips for searching the Library catalogue, Google Scholar, LibSearch (the LTU Library’s research and discovery tool) and ProQuest Central. Subject-specific databases for civil engineering and science were also referred to. After the final reports for each project had been submitted and assessed, the Faculty Librarian contacted the lecturer responsible for the unit, requesting copies of the student bibliographies for each group. References for each bibliography were then entered into EndNote. The Faculty Librarian grouped them according to various facets, including the name of the unit and the group within the unit; the material type of the item being referenced; and whether the item required a Library subscription to access it. A total of 58 references were collated for the 2010 CIV1EP unit; 237 references for the 2010 CIV2GR unit; and 225 references for the 2011 CIV1EP unit.INTERIM FINDINGSThe initial findings showed that student bibliographies for the three group projects were primarily made up of freely available internet resources which required no library subscription. For the 2010 CIV1EP unit, all 58 resources used were freely available on the Internet. For the 2011 CIV1EP unit, 28 of the 225 resources used (12.44%) required a Library subscription or purchase for access, while the second-year students (CIV2GR) used a greater variety of resources, with 71 of the 237 resources used (29.96%) requiring a Library subscription or purchase for access. The results suggest that the library sessions had little or no influence on the 2010 CIV1EP group, but the sessions may have assisted students in the 2011 CIV1EP and 2010 CIV2GR groups to find books, journal articles and conference papers, which were all represented in their bibliographiesFURTHER RESEARCHThe next step in the research is to investigate ways to increase the representation of scholarly references (found by resources other than Google) in student bibliographies. It is anticipated that such a change would lead to an overall improvement in the quality of the student papers. One way of achieving this would be to make it mandatory for students to include a specified number of journal articles, conference papers, or scholarly books in their bibliographies. It is also anticipated that embedding La Trobe University’s Inquiry/Research Quiz (IRQ) using a constructively aligned approach will further enhance the students’ research skills and increase their ability to find suitable scholarly material which relates to their topic. This has already been done successfully (Salisbury, Yager, & Kirkman, 2012)CONCLUSIONS & CHALLENGESThe study shows that most students rely heavily on the free Internet for information. Students don’t naturally use Library databases or scholarly resources such as Google Scholar to find information, without encouragement from their teachers, tutors and/or librarians. It is acknowledged that the use of scholarly resources doesn’t automatically lead to a high quality paper. Resources must be used appropriately and students also need to have the skills to identify and synthesise key findings in the existing literature and relate these to their own paper. Ideally, students should be able to see the benefit of using scholarly resources in their papers, and continue to seek these out even when it’s not a specific assessment requirement, though it can’t be assumed that this will be the outcome.REFERENCESEllis, J., & Salisbury, F. (2004). Information literacy milestones: building upon the prior knowledge of first-year students. Australian Library Journal, 53(4), 383-396.Gadd, E., Baldwin, A., & Norris, M. (2010). The citation behaviour of civil engineering students. Journal of Information Literacy, 4(2), 37-49.Salisbury, F., Yager, Z., & Kirkman, L. (2012). Embedding Inquiry/Research: Moving from a minimalist model to constructive alignment. Paper presented at the 15th International First Year in Higher Education Conference, Brisbane. Retrieved from http://www.fyhe.com.au/past_papers/papers12/Papers/11A.pdfWilkes, J., & Gurney, L. J. (2009). Perceptions and applications of information literacy by first year applied science students. Australian Academic & Research Libraries, 40(3), 159-171.

Data underlying research paper “Developing an open data intermediation business model: insights from the case of Esri”

by Ashraf Shaharudin, Bastiaan van Loenen, and Marijn Janssen from Delft University of Technology (TU Delft), the Netherlands.

This folder contains data underlying the research paper “Developing an open data intermediation business model: insights from the case of Esri”. It consists of:

1. De-identified interview transcripts

2. Informed consent form template

Note about the de-identified interview transcripts:

The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of Esri as open data intermediaries.

The 27 interviews, involving 29 interviewees, were conducted between April 2023 and April 2024 based on the semi-structured approach. We shared the tentative interview questions with the interviewees in advance (for the majority, at least three working days prior). Since they are semi-structured interviews, the ultimate interview questions may differ from the tentative questions.

We removed personally identifiable information from the transcripts. Some interviewees may risk being identifiable if their organization is known. Hence, we removed the organization and country information from all transcripts.

With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcripts as close to verbatim as possible.

Note about the informed consent form template:

We sent the informed consent form to every interviewee in advance and requested that they return it to us before or during the interview.

All interviewees whose interview transcripts are recorded in this document give permission for the anonymized transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.

Acknowledgement:

This research is part of the 'Towards a Sustainable Open Data ECOsystem' (ODECO) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

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