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56 results for “Scholarly Data”
Google Scholar as a Data Source for Research Assessment in the Social Sciences
<p>Column 1</p> <p>Source</p> <p>Data sources that the publications retrieved. Values for this column are “Google Scholar”, “Scopus”, and “Web of Science”.</p> <p>Column 2</p> <p>Authors</p> <p>The authors of the publications. This column is kept as additional information for verification of data. Not used in the analysis, it has not been standardized.</p> <p>Column 3</p> <p>Title</p> <p>Titles of the publications. For non-English publications, English titles, if available, are kept in this column. Otherwise, the original titles have been entered. The headings were checked and errors and omissions were corrected. Corrected titles are marked in red.</p> <p>Column 4</p> <p>Title translated with Google Translate</p> <p>In this Column, the English translated titles of the publications that do not have English titles are kept. Google Translate is used for detecting the language and translation. For publications with an English title, the expression [Title in English] has been entered. The translations of the original titles kept in this field were used in the analysis made through VOSviewer. It is marked in red as it is newly added data.</p> <p>Column 5</p> <p>Language</p> <p>Language of the publications. The languages of all publications were checked, missing data were completed and errors were corrected. If the language of the publication could not be determined, the value is [Not found]. The cells with addition or correction are marked in red.</p> <p>Column 6</p> <p>Document type</p> <p>Types of the documents. For all publications, publication type information was checked, missing ones were completed and corrections were made. All intervened cells are marked in red. Article and Review types are referred to as “Article” in the text.</p> <p>Column 7</p> <p>Full-text available</p> <p>Values for this column are “Yes” and “No”. The values for this column are Yes and No. If there is access to the full text of the publication via the web, "Yes", otherwise the "No" value has been entered.</p> <p>Column 8</p> <p>On research evaluation</p> <p>Values for this column are “Yes” and “No”. Using the title and/or abstract information, it was tried to determine whether the publications were related to the research evaluation. “Yes”, if found relevant, and “No” if not. It is marked in red as it is newly added data.</p> <p>Column 9</p> <p>Publication year</p> <p>The publication years of the documents. If the publication years are missing, they have been completed. The current publication years have been checked and corrected if necessary. If the year of publication could not be found, it is indicated as [Not found].</p> <p>Column 10</p> <p>English abstract</p> <p>Abstracts of the publications. If there is an accessible/available English abstract for the publication, it is kept in this column. [Not found/Not available] for missing values. Abstracts that were added, changed, corrected, or completed are marked in red.</p>
Dataset for How open is innovation research?–An empirical analysis of data sharing among innovation scholars
<p>This dataset comprises responses from 241 innovation researchers on their personal data sharing behavior as well as their perceptions of and attitudes towards open research data. This dataset is the supplementary material to Barczak et al. (2021) (<a href="https://doi.org/10.1080/13662716.2021.1967727" target="_blank" rel="noopener">https://doi.org/10.1080/13662716.2021.1967727</a>).</p>
Data for: Conservation scholars' perspectives on the morality of trophy hunting for the sake of conservation
<ol> <li>Trophy hunting is one of the most contentious issues in recent biodiversity conservation discourse, eliciting opposition and support for the practice. Ethical concerns are often at the heart of the debate.</li> <li>To investigate moral views about trophy hunting, we conducted an online survey of randomly selected scholars worldwide who had published on biodiversity conservation (n = 2,315).</li> <li>Scholars expressed divergent views on the moral acceptability of trophy hunting as a conservation practice. Moral convictions were significantly related to the perspectives</li> <li>The most important factor in predicting the moral views of the respondents was the consequences of trophy hunting for local human communities.</li> <li>The results also indicated that utilitarian (versus deontological) decision-making in conservation, ecological consequences of trophy hunting, and animal welfare issues contribute to the divergent views.</li> <li>The findings emphasize the need for interdisciplinary work on ethical issues concerning animal rights and welfare in conservation, as well as providing robust and comprehensive evidence on the consequences of trophy hunting for local communities.</li> <li>We caution that polarization among conservation scholars may negatively affect conservation efforts. Based on the literature and our findings, we provide some recommendations to narrow the gap and consider different management options.</li> </ol>
Data for: Conservation scholars’ perspectives on the morality of trophy hunting for the sake of conservation
Open the record for dataset details and reuse information.
Data for article: Normative drift and self-correction and self-regulation in scholarly publishing: The case of scholarly books published by academics at Makerere University
<p>University academics face multiple pressures to publish. These pressures emanate from contexts with different, often competing, social norms which result in academics publishing for reasons that may run counter to accepted scientific practice. This paper asks what decisions are being taken by academics when it comes to their choice of scholarly book publisher. An analysis of books selected from more than 2,500 self-reported scholarly publications produced by academics at Makerere University in Uganda from 2011 to 2017, reveals that 31 scholarly books were published. Of these books, more than half (54%) were published by publishers that do not follow accepted scholarly publishing practice. Findings also show that there was a sharp decline in books published with suspect publishers in the second half of the seven-year period. The article discusses possible reasons for the selection of suspect publishers and considers four factors that may account for the observable decline: (1) a cyclical downturn; (2) improved research management and reporting; (3) explication of the norms of science; and (4) self-correction.</p> <p><strong>METHODS</strong></p> <p>The specific interest of this study is the publication of scholarly books by academics at Makerere University. </p> <p>The sources of the publications data were the lists of research outputs published in Makerere University’s annual reports from 2011 to 2017. Digital copies of the annual reports in PDF format were downloaded from the university’s website. Data were scraped from the annual report PDFs by exporting the publication lists from the PDFs to MSWord, removing unnecessary line breaks and spacing to create a single publication per line, and copying the publication lists into MSExcel for cleaning and analysis. Data were cleaned to remove (1) duplicate entries (e.g. identical publications in the same annual report or a publication listed as ‘under review’ or ‘in press’ in one year and listed again as a publication in a following year); (2) publications listed as ‘submitted’, unpublished masters and PhD theses, speeches, lectures, responses or retractions; (3) incomplete entries (e.g. no article, book or journal title); and (4) publications prior to 2011 (e.g. a 2008 publication listed in the 2011 annual report).</p> <p>The cleaned data were then coded for ‘year of publication’ (2011 to 2017 inclusive), ‘college’ (any of the colleges at Makerere University to which the authors were affiliated), and ‘publication type’ (book chapter, journal article, edited volume, working paper, policy brief, monograph, conference paper, textbook, report, popular article, poster presentation, newsletter, essay, catalogue, poetry, handbook). Books or parts of books (i.e. ‘book chapter’, ‘edited volume’, ‘monograph’, ‘textbook’, ‘catalogue’, ‘poetry’ and ‘handbook’) were further coded for ‘publisher’ (name); ‘publisher location’ (city, country as indicated on the imprint page of the publication); and ‘URL’ (online reference to publisher or publication). In some cases, publications were listed by type in the annual reports. Where publication lists were not presented in this way, indicators such as ‘journal of’, ‘volume’, ‘issue’ and ISSNs were used to identify journals; conference names, ‘proceedings of’, dates and cities were used to identify conference papers and presentations; ‘news’, ‘magazine’, ISSNs and the absence of ‘journal of’ were used to identify periodicals; and place of publication, publisher and ISBNs were used to identify books. Where these indicators were insufficient to determine a publication type, an online search was done to find the missing data. </p> <p>A limitation of the adopted method is that it relies on self-reporting by the university colleges and the list can therefore not be assumed to be comprehensive. Without knowledge of the reporting guidelines, it is also not possible to know whether all colleges and schools used the same criteria when selecting what to submit for reporting purposes. For example, some colleges may have decided to include working papers, policy briefs or papers presented at conferences, while others may have decided not to do so. </p>
Data from: Measuring researchers' potential scholarly impact with structural variations: four types of researchers in information science (1979–2018)
We propose a method to measure the potential scholarly impact of researchers based on network structural variations they introduced to the underlying author co-citation network of their field. We applied the method to the information science field based on 91,978 papers published between 1979 and 2018 from the Web of Science. We divided the entire period into eight consecutive intervals and measured structural variation change rates (DM) of individual authors in corresponding author co-citation networks. Four types of researchers are identified in terms of temporal dynamics of their potential scholarly impact—1) Increasing, 2) Decreasing, 3) Sustained, and 4) Transient. The study contributes to the understanding of how researchers' scholarly impact might evolve in a broad context of the corresponding research community. Specifically, this study illustrated a crucial role played by structural variation metrics in measuring and explaining the potential scholarly impact of a researcher. This method based on the structural variation analysis offers a theoretical framework and a practical platform to analyze the potential scholarly impact of researchers and their specific contributions.
Figure 6 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 6 - Automated creation of data paper manuscripts from Ecological Metadata Language (EML) metadata in the ARPHA Writing Tool.
Figure 5 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 5 - The metadata from the GBIF Integrated Publishing Toolkit (IPT) can be downloaded as RTF or EML files and submitted to Pensoft's journals as data paper manuscripts.
Figure 3 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 3 - The user interface of the ARPHA Writing Tool through which single or multiple specimen records from GBIF, BOLD, iDigBio and PlutoF are imported through records identifiers.
Figure 1 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 1 - Recommendation of Dryad to cite both the original article in association with which the data were published and the data themselves.
Figure 7 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 7 - Selection of the journal and "Data Paper (Biosciences)" template in the ARPHA Writing Tool.
Figure 4 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 4 - Occurrence records and taxonomic treatments (if present in the article), published in the Biodiversity Data Journal, are exported in two separate Darwin Core Archives (DwC-A) and are available for direct download or harvesting via web services.
Figure 1 from: Smith V, Rycroft S, Brake I, Scott B, Baker E, Livermore L, Blagoderov V, Roberts D (2011) Scratchpads 2.0: a Virtual Research Environment supporting scholarly collaboration, communication and data publication in biodiversity science. ZooKeys 150: 53-70. https://doi.org/10.3897/zookeys.150.2193
Figure 1 - Scratchpad usage statistics from February 2007 to September 2011. The black dashed line represents the number of Scratchpad community sites (in hundreds) and the blue solid line represents the number of registered users (in thousands). As of September 2011 we have switched to recording the number of active users (currently 4424) since this figure provides a more accurate guide to usage.
Figure 2 from: Smith V, Rycroft S, Brake I, Scott B, Baker E, Livermore L, Blagoderov V, Roberts D (2011) Scratchpads 2.0: a Virtual Research Environment supporting scholarly collaboration, communication and data publication in biodiversity science. ZooKeys 150: 53-70. https://doi.org/10.3897/zookeys.150.2193
Figure 2 - Screenshots of the Scratchpad 2 publication module showing an example workflow. Top, the section writing tool showing material and methods section; middle, the relationship selector that allows a taxon and additional materials to be associated with a section of the publication; and bottom, supplementary files such as illustrations, photos or graphs can be added to complete the publication.
Data from: An international, multi-stakeholder survey about metadata awareness, knowledge, and use in scholarly communications
Open the record for dataset details and reuse information.
Data from: Measuring researchers’ potential scholarly impact with structural variations: four types of researchers in information science (1979–2018)
Open the record for dataset details and reuse information.
Figure 11 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 11 - Submission of manuscripts to ARPHA Writing Tool through Application Programming Interface (API).
Figure 9 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 9 - The occurrence data from articles published in the Biodiversity Data Journal (in this case from the paper of Johnson 2013) are automatically indexed via Darwin Core Archive in the GBIF Integrated Publishing Toolkit.
Figure 4a from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 4a - Interactive mapping of geo-coordinated species occurrences (example from Frolov and Akhmetova 2013).
Figure 13 from: Penev L, Georgiev T, Geshev P, Demirov S, Senderov V, Kuzmova I, Kostadinova I, Peneva S, Stoev P (2017) ARPHA-BioDiv: A toolbox for scholarly publication and dissemination of biodiversity data based on the ARPHA Publishing Platform. Research Ideas and Outcomes 3: e13088. https://doi.org/10.3897/rio.3.e13088
Figure 13 - Conversion of Ecological Metadata Language (EML) metadata into data paper manuscripts in ARPHA Writing Tool.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.