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46 results for “Academic Research”
Implementing virtual workspaces for clinical research at an academic health center: A case report
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Research Data Services in Academic Libraries: A Survey of North American Academic Libraries in 2019
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Learn2Link: Linking the Social and Academic Profiles of Researchers
<p>Data and code to reproduce work in Learn2Link: Linking the Social and Academic Profiles of Researchers.</p>
Features of research data policies of Earth science and biodiversity academic society journals
<p>Dataset related to Hübner (2020) Earth science and biodiversity journals can improve support for data publication. Preprint: https://doi.org/10.23689/fidgeo-3818</p> <p>This study reviews research data policies and author instructions of 31 journals from the Earth sciences and from biodiversity that are published by German learned societies or research institutions. The statements on data publishing of the journal´s data policies / author guidelines were matched to 14 pre-defined features of journal research data policies from Hrynaszkiewicz, I. et al. (2020) Developing a Research Data Policy Framework for All Journals and Publishers. Data Science Journal, 19: 5, pp. 1–15. <a href="https://doi.org/10.5334/dsj-2020-005">https://doi.org/10.5334/dsj-2020-005</a>.</p>
Survey dataset for project "US-based legal interpreters and their use of academic research"
<p>Dataset of article entitled <span>US-based legal interpreters and their use of academic research: expectations, practices, and potential for collaboration between academia and the profession. Includes survey questions (PDF) and survey raw data (Excel, .xlsx file)</span></p>
Qualitative research: Behavioral antecedents of crowdsourcing in science: academic teachers' perspective
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Focus research group: Behavioral antecedents of crowdsourcing in science: academic teachers' perspective
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Academic Meta Tool Samian Research Punzen-Töpfer-Netzwerk Ontology
<p>Academic Meta Tool Samian Research Punzen-Töpfer-Netzwerk Ontology</p>
Gulustan-Black Sea Scientific Journal of Academic Research
<p>AGRICULTURAL, ENVIRONMENTAL & NATURAL SCIENCES</p> <p>SOCIAL, PEDAGOGY SCIENCES & HUMANITIES</p> <p>MEDICINE, VETERINARY MEDICINE, PHARMACY AND BIOLOGY SCIENCES</p> <p>TECHNICAL AND APPLIED SCIENCES</p> <p>REGIONAL DEVELOPMENT AND INFRASTRUCTURE</p> <p>ECONOMIC, MANAGEMENT & MARKETING SCIENCES</p> <p>LEGAL, LEGISLATION AND POLITICAL SCIENCES</p>
Supplementary material 1 from: Van Tuyl S, Whitmire A (2018) Investigation of Non-Academic Data Management Practices to Inform Academic Research Data Management. Research Ideas and Outcomes 4: e30829. https://doi.org/10.3897/rio.4.e30829
Interview protocol used for the project, "Investigation of Non-Academic Data Management Practices to Inform Academic Research Data Management"
Data set for: Research trends of Academic Performance and Social Networking Sites
<p>Data set for: Research trends of Academic Performance and Social Networking Sites.</p>
Figure 3 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 3 Types of campus groups that provide RDS (n=103). Type codes are defined as follows: Admin: a campus administrative unit that does not fall into any other category; Center: research centers or institutes excluding HPC groups; Dept = Departments or colleges; HPC: High Performance Computing and research computing units including HPC run by IT units; Individuals: Individual staff, faculty, students, etc.; IT: Information Technology associated with the entire campus, colleges, or departments excluding HPC groups; Lab: Various labs on campus that do not fall into any other category; Research Office: Groups that oversee university research; Other: Groups that cannot be categorized under any other code.
Figure 2 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 2 Breakdown of the workshops or topics with a tool or programming language code applied (n=47). Only tool codes that have a frequency >1 are shown. Tool code names are self-explanatory (i.e. the name of tool).
Figure 1 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 1 Workshop topic code frequencies. Up to two topic codes were applied to each workshop (n=160). Topic codes are defined as follows: Carpentry: a data or software Carpentry workshop; Cleaning: data cleaning and related techniques; Coding: how to work with data via command line or in a specific language; General: the basics of data management; GIS: geographic information system or spatial data/tools; Grants: the word "grants" or the name of a funding agency was explicitly mentioned in the workshop's title or description; HPC: high performance computing; Locate: focused on how to search and locate datasets; Metadata: metadata and data documentation; Mining: focused on text and data mining; Org: data organization; Other: misc. topics or unclassifiable; Plans: data management plans; Repository: addresses a specific repository, how to use a repository, or data repositories in general; Reproducibility: focused on research reproducibility; StorageSec: data storage and/or security tools and topics; Tool: focused on how to use tools related to data and data management (see Fig. 2); Visualization: data visualization.
Supplementary material 1 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Links to library and university/college research data management policies.
Figure 4 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 4 Disciplinary categorization of campus groups that provide RDS (n=34). Discipline codes are defined as follows: Bio: Groups that specialize in biology, including health and medicine; Bio/Stats: Groups that specialize in biology and statistics; Data: no specific discipline but has the word 'data' in the name; GIS: Groups that specialize in spatial and GIS (Geographic Information Systems) data; Humanities: Groups specializing in humanities; Social/Stats: Groups that specialize in statistics and social science; SocialSci: Groups specializing in social science; Stats: Groups specializing in statistics.
Figure 2 from: Haak L, Greene S, Ratan K (2020) A New Research Economy: Socio-technical framework to open up lines of credit in the academic community. Research Ideas and Outcomes 6: e60477. https://doi.org/10.3897/rio.6.e60477
Figure 2 Collaborative Workflow of an Incremental Dataset in a Consortium Setting (click to view enlarged slide in Present mode). The workflow of an investigator's incremental dataset is shown as it is incorporated into the Facilitated Living Review (FLR), moving along a continuum from closed to open review. (1) After ideation and hypothesis formation by the team, early experimentation creates an incremental dataset. (2) The dataset is shared and discussed with the Project X research consortium, then iterated. (3) The v.3 dataset is shared with the consortium for further analysis, positioning, and citation in the context of the latest published evidence in the FLR. (4) The FLR with its cited, organically peer reviewed dataset is posted on a preprint server under the authorship of the FLR consortium. (5) Feedback from the broader community may lead to further iteration of the dataset at the discretion of the project team in the next round of the FLR revisions.
2015 Academic Librarians' Familiarity with Research Impact Indicators at R1 Institutions
<p>This data is from a national survey conducted during 2015, which was administered to 13,436 full-time academic librarians from 150 institutions included in the 2013 National Center for Education Statistics (NCES) list of Carnegie-classified R1 institutions in the United States. Email addresses were collected manually in order to filter out certain positions not considered professional in nature and the survey was designed to eliminate responses from outside of the US, as well as part-time, retired, and other non-full-time respondents. The Qualtrics-administered survey was sent out three times over a two-week period in 2015, resulting in a 5.3% response rate (n=707, valid responses counted only).</p> <p>The survey's purpose was to assess the levels of familiarity, the levels of frequency for different types of uses, and the purposes of those uses of research impact metrics, usage data, and qualitative measures of research impact among academic librarians at R1 institutions in the U.S.</p>
Dataset for 'Beyond the "Research vs. Scholarship" Dichotomy: the Emergence of a New Category of Academic Staff'
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Academic reception and public dissemination of neurological research between 2012 and 2021
<p>Fundamental changes in the way scientific research is disseminated have inspired the concept of altmetrics, most prominently the Altmetric Attention Score (AAS). The exact relation between the latter and traditional measures of science reception (e.g. citation count) is unknown. In this study, we determined citation counts and AAS as well as the ratio between the two (AAS-to-citation ratio) in 138,339 original research and review articles from 86 neurological journals between 2012 and 2021. The journal impact factor was closely correlated with both citation count (rs = 0.73) and AAS (rs = 0.64), whereas it showed a negative association with the AAS-to-citation ratio (rs = −0.26). Reviews accumulated more citations and a higher AAS than original research, while their AAS-to-citation ratio was significantly lower. Citation count was the only metric significantly associated with the number of publications by country (rs = 0.65). There were notable differences between major neurological subspecialties, with Alzheimer's disease the article topic having the highest average citation count, AAS, and AAS-to-citation ratio. Our findings suggest that the career of a neurological paper in the academic and public sphere is determined by various and sometimes specific factors.</p>
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.