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13,499 results for “researcher”
Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem
<p><strong>Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong></p> <p><a href="https://github.com/yusufsn/EclipseForumData">https://github.com/yusufsn/EclipseForumData</a></p> <p>This is a research artefact for the paper: <strong>How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong>. This artifact is a repository consisting of collected dataset including (i) 289,061 threads, (ii) 216,864 extracted links from threads, (iii) 2,170 contributors, and the results of our qualitative analysis, (i) 1,142 manually annotated type of discussion and (ii) 1,142 manually annotated sentiment analysis. This artefact aims to enable researchers to replicate our mixed-methods quantitative results of the paper and reuse the dataset for further software engineering research.</p> <p>Contents</p> <ul> <li>dataset: <ul> <li><code>289061_threads.csv.zip</code> - 289,061 collected main threads of all users without post from webmaster (.zip format)</li> <li><code>216864_links.csv.zip</code> - 216,864 extracted links from collected threads (.zip format)</li> <li><code>2170_contributors.csv</code> - 2,170 list of contributions (.csv format)</li> </ul> </li> <li>Results of manual analysis: <ul> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQoRpcbrV66OEB4vaCG9Njq65zW7XpLRITYG3BlUOoa_DmeOKcdQIgYJ8y2aSlmL3y9bCUhjpP3rYmT/pubhtml">Manual annotation of discussion type</a></li> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQfyvsP1Zq3b9p_BYwkkgYXuEePJB6sIroC47jOUMcR5P8t7DCJFNpOJD565SPgqi--L3AAIQWs2kC5/pubhtml">Manual annotation of sentiment analysis</a></li> </ul> </li> </ul> <p>Authors</p> <ul> <li><a href="https://yusufsn.github.io/">Yusuf Sulistyo Nugroho</a></li> <li><a href="https://syful-is.github.io/">Syful Islam</a></li> <li>Keitaro Nakasai</li> <li><a href="https://ifrazrehman.github.io/">Ifraz Rehman</a></li> <li><a href="https://hideakihata.github.io/">Hideaki Hata</a></li> <li><a href="https://raux.github.io/">Raula Gaikovina Kula</a></li> <li><a href="https://cs.uwaterloo.ca/~m2nagapp/">Meiyappan Nagappan</a></li> <li><a href="https://matsumotokenichi.github.io/">Kenichi Matsumoto</a></li> </ul>
Raw Data related to Research Article: Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects, Optics Express, volume 29, issue 3, 2021
<p>These are the plotted and raw data used to obtain figures shown in:</p> <p>P. Tiwari, P. Wen, D. Caimi, S. Mauthe, N. Vico Triviño, M. Sousa, and K. E. Moselund, Scaling of metal-clad InP nanodisk lasers: optical performance and thermal effects., Optics Express, volume 29, issue 3, 2021</p> <p>Please comply with copyright rules of the Optical Society of America under the terms of the OSA Open Access Publishing Agreement.:</p> <p>https://www.osapublishing.org/library/license_v1.cfm#VOR-OA</p> <p> </p>
Review of OpenStreetMap research publications from 2016 to 2019
<p>This dataset consists of a <strong>review of research publications about the <a href="https://www.openstreetmap.org/">OpenStreetMap (OSM) project</a> </strong>published from 2016 to 2019.</p> <p>The dataset was obtained as follows. First, papers published between 2016 and 2019 were extracted using <a href="https://scholar.google.it/">Google Scholar</a> with a query identifying all records with at least one of the keywords “OpenStreetMap” and “OSM” in the title. The extracted records were further filtered to only keep papers having a minimum length of 4 pages and published in academic journals or conference proceedings. In addition, irrelevant papers (e.g. using “OSM” as an acronym for another purpose) and non-English papers were removed from the dataset. The remaining paper were then analyzed and manually classified.</p> <p>The attributes included in the dataset are the following:</p> <ul> <li><strong>id</strong> [paper ID]</li> <li><strong>Paper Citation </strong>[citation of the paper]</li> <li><strong>Authors’ disciplines</strong> — multiplicity: 1-7; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Journal’s discipline</strong> — multiplicity: 1; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Topic(s)</strong> — multiplicity: 1:10; allowed values: application, data quality, contribution behaviours, analyzing contributions, contributors, shaping contributions, OSM effects, data enrichment, development, review</li> <li><strong>Authors' Geography (countries) </strong>— multiplicity: 1:n; allowed values: [list of countries]</li> <li><strong>Authors' Geography (continents) </strong>— multiplicity: 1:n; allowed values: [list of continents]</li> <li><strong>Study Area Geography (countries)</strong> — multiplicity: 1:n; allowed values: [list of countries], [list of continents], Global, NA</li> <li><strong>Geographic Correspondence</strong>— multiplicity: 1; allowed values: no correspondence, partial affiliation/partial location, partial affiliation/full location , full correspondence, NA</li> <li><strong>Perspective on the community</strong> — multiplicity: 1; allowed values: OSM as a data source, data produced by contributors, a collaborative project based on contributors, contributors producing data, a unified community, a diverse community, a social product</li> <li><strong>Evidence of engagement </strong>— multiplicity: 1-8; allowed values: none, acknowledgement, meaningful development, occasional development, object of study - direct, object of study - indirect, contribution, participation</li> <li><strong>General comments </strong>[comments on the paper]</li> </ul>
Next Steps: How the FDNext Project is Tackling Research Data Management … and Farewell to Emma
<p>In this episode we talk to Kerstin Helbig about the research data management (RDM)project FDNext, which is also where our co-host Emma Harris' new role will be based. We discussed what the approach of FDNext is, the challenges of implementing effective RDM, and how it fits into the wider framework of Open and FAIR Data initiatives. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.forschungsdaten.org/index.php/FDNext">FDNext (German language)</a></p> <p><a href="https://zenodo.org/record/4071471#.X791NmhKhPY">FDMentor RDM Train-the-Trainer Concept</a></p> <p><a href="https://www.researchgate.net/profile/Kerstin_Helbig">Kerstin Helbig</a></p> <p><a href="https://www.linkedin.com/in/emma-a-harris-6bb865123/">Emma Harris</a></p>
Dataset - research methodology annotation (Information Science)
<p>Datasets used for developing text mining methods for extracting research methods reported in Information Science journal articles. </p>
Research data supporting "4D Electron Tomography of Dislocations Undergoing Electron Irradiation"
<p>All TEM micrographs and electron diffraction patterns for tomography</p>
Research data supporting for Application of electron tomography of dislocations in beam-sensitive quartz to the determination of strain components
<p>This archives contains all the raw data (micrographs) supporting the publication</p>
Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences - supplemental material & data
<p>Table and data repository for the manuscript "Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences"</p> <p><strong>Supplemental Tables:</strong></p> <ul> <li>table1_ESIT Project Table</li> <li>table2_TIN-ACT Project Table</li> <li>table3_ITN_tinnitus</li> <li>table4_ITN_other</li> <li>table5_Individual PhDs</li> </ul> <p><strong>Individual-level and de-identified survey data (raw data):</strong></p> <ul> <li>raw_data_ITN_tinnitus (survey results from PhDs as part of an ITN with a focus on tinnitus)</li> <li>raw_data_ITN_other (survey results from PhDs associated to ITNs with another focus)</li> <li>raw_data_Individual_Phds (survey results from PhDs not part of an ITN)</li> </ul>
UK Research Software Survey 2014
<p>This spreadsheet contains the anonymised data collected as part of a survey of UK researchers in their use of research software.</p> <p>We asked people specifically about “research software” which we defined as:</p> <blockquote> <p>“Software that is used to generate, process or analyse results that you intend to appear in a publication (either in a journal, conference paper, monograph, book or thesis). Research software can be anything from a few lines of code written by yourself, to a professionally developed software package. Software that does not generate, process or analyse results - such as word processing software, or the use of a web search - does not count as ‘research software’ for the purposes of this survey.”</p> </blockquote> <p>We contacted 1,000 randomly selected researchers at each of 15 Russell Group universities. From the 15,000 invitations to complete the survey, we received 417 responses – a rate of 3% which is fairly normal for a blind survey. We used Google Forms to collect responses.</p> <p>The responses have good representation from across the disciplines, seniorities and genders. This is a statistically significant number of responses that can be used to represent the views of people in research-intensive universities in the UK.</p> <p>An overview of the data is available on the worksheet "Summary data". Responses to questions are ordered by unique respondent ID. Please read the "README" worksheet for additional information about the collection and processing of this data.</p> <p>This survey data is licensed under a Creative Commons by Attribution licence. Copyright resides with The University of Edinburgh on behalf of the Software Sustainability Institute.</p> <p>Please cite as:</p> <p><strong>APA</strong></p> <p>Hettrick. S. J., et al. (2014). UK Research Software Survey 2014 [Data set]. doi:10.5281/zenodo.14809</p> <p><strong>Chicago</strong></p> <p>S.J. Hettrick et al, UK Research Software Survey 2014 (accessed December 4, 2014), 10.5281/zenodo.14809.</p> <p><strong>MLA</strong></p> <p>Hettrick S.J., et al. “UK Research Software Survey 2014” ZENODO, 2014. Web. 4 December 2014. .</p>
Policies of Research Institutions Requiring Open Access Depositing
<p>ROARMAP (http://roarmap.eprints.org) has been queried for policies of research institutions which require Open Access depositing of a version of all publications authored by their researchers. This data was needed to explain some of the outcomes of a study about the proportions of Open Access publishing in the European Research Area (ERA) as well as in Brazil, Canada, Japan and the USA (Archambault et al. 2014).</p>
H2M survey data on commercialisation training needs of Health Researchers
<p>Health-2-Market was a 3-year long Coordination and Support Action, funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration (Grant Agreement No 305532). H2M aimed at providing training and individual support to Health / Life Sciences researchers in the process of translating their research results into successful new business ideas.</p> <p>With a view to properly adapting the training offer of the project to the needs of Health / Life Sciences researchers in terms of entrepreneurship and business skill development a Training Needs Analysis (TNA) was conducted. In this context, H2M launched an online survey targeted at Health / Life Sciences researchers who have been involved in EU health projects. In particular, the objectives of the survey were:</p> <ul> <li>To formulate a descriptive understanding of various aspects of commercialisation and training needs of the main target group of the project;</li> <li>To divide this target group into homogeneous sub-groups (clusters) along a number of key characteristics such as demographics, commercialisation attitudes and needs;</li> <li>To understand preferences and importance of different aspects and needs through the analysis of: <ul> <li>Knowledge areas that can influence commercialisation behaviour;</li> <li>Training modalities that have an effect on the intention to participate and /or on the perception of the usefulness of a commercialisation training;</li> <li>Variations identified over different sub-groups.</li> </ul> </li> </ul> <p>The survey was dispatched to a database composed of 7,991 unique contacts of participants in previous health projects, accessed through the Directorate General for Health and Food Safety of the European Commission. The initial aim of at least 50 complete responses was overwhelmingly surpassed: 637 respondents completed the survey in full.</p> <p>The “H2M survey data on commercialisation training needs of Health Researchers” dataset contains the raw, anonymised data that were collected from these respondents, along with the questionnaire items that were utilised.</p>
Parkinson Research: MARG Sensor Data of the Pronation-Supination Task [old version]
<p>In this ZIP-file you find supplementary data to the manuscript "<strong>Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson's Disease". </strong>26 subjects (13 PD patients and 13 controls) performed Item 3.6 "Pronation-Supination Movements of Hands" of the MDS-UPDRS [1]. The ZIP-file contains anonymized subject data, 51 data features for each record, results of the different UPDRS ratings from all neurologists and the MARG sensor raw data of the pronation-supination phase in single data files (csv).</p>
Parkinson Research: MARG Sensor Data of the Pronation-Supination Task
<p>In this ZIP-file you find supplementary data to the manuscript "<strong>Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson's Disease". </strong>26 subjects (13 PD patients and 13 controls) performed Item 3.6 "Pronation-Supination Movements of Hands" of the MDS-UPDRS [1]. The ZIP-file contains anonymized subject data, 51 data features for each record, results of the different UPDRS ratings from six neurologists and the MARG sensor raw data of the pronation-supination phase in single data files (csv).</p>
Research Software Engineers Supporting Science: Survey Responses
<p>Raw survey data for "Not everyone can use git: Research Software Engineers’ recommendations for scientist-centred software support (and what researchers think of them)", a talk given by Caroline Jay at RSE16, Manchester, UK.</p>
PIR data and EEG scoring for Wellcome Open Research methods paper (Brown et al 2016)
<p>PIR data and EEG-scored sleep in the Wellcome Open Research article:</p> <p>'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'</p> <p> </p> <p>1sensorPIRvsEEGdata.csv - PIR based actigraphy for mice to compare to EEG-scored sleep</p> <p>EEG_4mice10sec.csv - Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118</p> <p>blandAltLandD.csv - paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)</p> <p><br> 1monthPIRsleep.csv - 1 month of activity for for figure 4</p> <p><br> 24mice_activity_LD1week.csv - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)<br> 24mice_sleep_LD1week.csv </p> <p> </p> <p> </p>
Research data supporting "Pericyte seeded dual peptide scaffold with improved endothelialization for vascular graft tissue engineering"
<p>Raw research data supporting the paper:</p> <p>Campagnolo, P. <em>et al</em>., Pericyte seeded dual peptide scaffold with improved endothelialization for vascular graft tissue engineering, 2016, Advanced Healthcare Materials, 5(23), 3046-3055.</p> <p> </p>
RDA Publishing Workflows_ Research Workflows (Responses)
<p>Responses to online questionnaire / call for examples from Research Data Alliance Working Group on Publishing Research Data Workflows.</p> <p>These relate to the article 'Connecting data publication to the research workflow: a preliminary analysis' by the same authors, submitted to the International Digital Curation Conference, 2017</p>
Research data supporting "Synthesis of hetero-bifunctional, end-capped oligo-EDOT derivatives"
<p>Raw research data supporting the paper "Synthesis of hetero-bifunctional, end-capped olido-EDOT derivatives", Chem, Volume 2, Issue 1, p125–138, 12 January 2017 by C. Spicer <em>et al</em>.</p>
Research data supporting "Plasmonic chirality imprinting on nucleobase-displaying supramolecular nanohelices via metal-nucleobase recognition"
<p>This file contains the raw research data supporting the publication:</p> <p>Y. Lin<em> et al</em>., Plasmonic chirality imprinting on nucleobase-displaying supramolecular nanohelices via metal-nucleobase recognition, Angew. Chem. Int. Ed. 2017, DOI: 10.1002/anie.201610976.</p> <p> </p>
Desk Research
<p>Die Forschungspraxis des "Desk Research", also der Forschung, die ausschliesslich mit recherchierten Daten auskommt, wird oft an der Schnittstelle zwischen Natur- und Geisteswissenschaften praktiziert. Der Zugang zu hochwertigen Daten ist ein Schlüsselelement in dieser Forschungspraxis. Daher ist es für den Desk Researcher von grösster Bedeutung auf Open Science bzw. auf Open Access Publikationen zugreifen zu können. Bei der Optimalen Nutzung und Verarbeitung der zur Verfügung stehenden Information ist ein Desk Researcher auf seinen Einfallsreichtum angewiesen. Die Elemente des Open Access und des Generierens von durchschlagenden Ideen wurden bei dem hier zu sehenden Keyboard integriert um jederzeit auf Tastendruck zur Verfügung zu stehen. Das Bild zeigt daher das wichtigste Instrument des Desk Researchers.</p>
ScienceDex guides
Understand access before you commit
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.