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4,182 results for “universities”
Fig. 2 in The Universiti Brunei Darussalam biological collections: history, present assets, and future development
Fig. 2. Map of Brunei illustrating the collection sites of plants stored in UBDH (black diamonds); grey line: district border (districts: Belait, Tutong, Muara, Temburong); black dashed line: national border; black continuous line: coastline.
Fig. 1 in The Universiti Brunei Darussalam biological collections: history, present assets, and future development
Fig. 1. UBDH botanical collections (excluding bryophytes) deposited (a) at five-year intervals, and (b) cumulatively.
Text-fig. 3. Front views of hemi-maxillaries from Prémontré (Paris Basin, MP 10), showing the large infraorbitary foramen of a) SLP29PR-1312, stored in the Montpellier University collections (ISE-M), Hartenbergeromys hautefeuillei; b) SLP29PR-960, Pantrogna marandati. Scale bar 1 mm. in A Reevaluation Of The Taxonomic Status Of The Rodent Masillamys Tobien, 1954 From Messel (Germany, Late Early To Early Middle Eocene, 48-47 M.Y.)
Text-fig. 3. Front views of hemi-maxillaries from Prémontré (Paris Basin, MP 10), showing the large infraorbitary foramen of a) SLP29PR-1312, stored in the Montpellier University collections (ISE-M), Hartenbergeromys hautefeuillei; b) SLP29PR-960, Pantrogna marandati. Scale bar 1 mm.
Dataset - University Social Responsibility in the context of Social Appropriation of Knowledge: A Systematic Literature Mapping Approach (2015-2020)
<p>From abstract:</p> <p>University Social Responsibility (USR) should be seen beyond a yearly report and be understood as a process of Social Appropriation of Knowledge (SAK) though different stakeholders around higher education. The object of this literature mapping is to analyze the recent developments regarding USR regarding SAK in a higher education context where different stakeholders are involved. Following a PRISMA flow mapping process, six general-designed questions of 286 articles are revised for this paper as to discover how SAK happens within universities. All articles were retrieved from Scopus and Web of Sciences databases. The findings include a USR field divided between models´ approach and experience such as service-learning. Furthermore, sustainability and the environment take a special role within USR in the context of SAK that must be analyzed by itself as there is a wide interest in the topic. </p>
UnivProd: A university production dataset
<p>UnivProd is a dataset that quantifies two key production measures for universities: research (academic publications) and education (graduated students). It combines data from three primary sources: Microsoft Academic Graph (MAG), the Delta cost project, and ``Chetty'' tax data. Two quality measures compliment the production measures: citations per paper provides a quality measure for research output and the mid-career salary of undergraduate alumni provides a quality measure for education output. Entries (or rows) in the dataset are ``indexed'' by institution ID and academic year.</p>
Data collected through a questionnaire regarding satisfaction in the use of technological simulation tools in learning, in a University of Peru
<p>Data collected through a questionnaire regarding satisfaction in the use of technological simulation tools in learning, in a University of Peru</p>
Montana Prairie Fish Collection, Montana State University
<p>Freshwater ecosystems may be the most endangered ecosystems globally, and the prairie biome is arguably the most endangered ecoregion in North America. Until recently there was a deficit in the knowledge of fish distributions in streams in eastern Montana. Beginning in the late 1990’s Dr. Robert G. Bramblett of the Department of Ecology at Montana State University (MSU) together with graduate students, and colleagues from the university and several agencies began studying the fishes of Montana prairie streams. These studies produced a large number of preserved fish voucher specimens which now comprise the Montana Prairie Fish Collection. The value and significance of the Montana Prairie Fish Collection is exceptional because most specimens came from never-before-sampled streams in a very scarcely collected area. In 2018, the MSU Library, the MSU Department of Ecology, the Montana Entomology Collection, and the Montana State Library were funded by the Council on Library and Information Resources “Hidden Collections” grant to produce digital metadata records for the Montana Prairie Fish Collection. The collection and its associated data comprise specimens that were collected during 1999-2018 from over 850 fish sampling events from about 360 streams in eastern Montana. At least 70 individual scientists, students, and Montana Fish, Wildlife, and Parks staff collected fish. The collection totals over 17,000 individual fish specimens representing 57 fish taxa and represents a remarkable 900% increase in Montana prairie ecoregion fish collections available online. Digitization of the collection will encourage scientists and others to ask many new research questions such as how species, biological distributions, and ecosystems have changed.</p>
Clinical Trial Transparency at UK Universities (2018-2021)
<p>This dataset describes the analysis 20 U.K. universities' clinical trial registration and reporting policies and reporting performance of CTIMPs on EUCTR. This is supplementary information on a publication in the journal Clinical Trials: Journal of the Society for Clinical Trials. The manuscript is titled "Improving clinical trial transparency at U.K. universities: evaluating three years of policies and reporting performance on the European Clinical Trial Registry (EUCTR)". Please refer to the manuscript for more information on the methodology and analysis.</p>
African Universities Performance Data 2001-2015
<p>Dataset produced by the Higher Education Research and Advocacy Network in Africa (HERANA) containing student, staff and research output data for eight universities in Africa for the period 2001 to 2015. Universities include the University of Botswana, University of Cape Town, University of Dar es Salaam, Eduardo Mondlane University, University of Ghana, University of Mauritius, and University of Nairobi.</p> <p>Publications associated with this dataset include:</p> <ol> <li><a href="http://www.africanminds.co.za/dd-product/research-universities-in-africa/">Research Universities in Africa</a></li> <li> <p><a href="https://www.chet.org.za/papers/empirical-overview-emerging-research-universities-africa">An Empirical Overview of Emerging Research Universities in Africa 2001-2015</a></p> </li> </ol>
Auditory Attention Detection Dataset Nanjing University
<p>Auditory Attention Detection Dataset</p> <p>The experiment is conducted by Yuanming, Zhang and Ziyan, Yuan, supervised by Jing, Lu.</p> <p> </p> <p>Two zip files are included in this dataset.</p> <p>The script.zip file contains several scripts to divide each trial into several parts. These scripts are useful if you want to split the dataset into training, validation and test set.</p> <p>The other zip file contains the preprocessed EEG and audio signals. (Not the raw EEG!)</p> <p>Each mat file contains EEG and audio signals from one subject. Their are two variables named <strong><em>expinfo</em></strong> and <strong><em>data</em></strong>.</p> <p>The <em><strong>expinfo </strong></em>contains the attended direction information and audio stimulus playback information of each trial of the experiments. The azimuth column in <em><strong>expinfo </strong></em>indicates the directions of the two competing speakers. The negative directions indicated the speaker coming from the left, and positive coming from the right.</p> <p>The <strong><em>data </em></strong>contains several essential fields relative to the EEG and audio signals. The <em><strong>data.eeg</strong></em> field is a cell array with 32 cell elements. Each cell contains the EEG signals in a single trial. And the index of the cell is the number of trial. The <em><strong>data.leftWav</strong></em> and <em><strong>data.rightWav</strong></em> field are original audio stimuli, and the <em><strong>data.leftEnv</strong></em> and <em><strong>data.rightEnv</strong></em> are the envelopes of the<em><strong> data.leftWav </strong></em>and <em><strong>data.rightWav</strong></em> respectively.</p> <p>The <em><strong>data.dim</strong></em> field tells you the physical meaning of each dimension of each signal, and the <em><strong>data.dim.chan.eeg</strong></em> contains the name of each channel of the EEG signal.</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
Electronic correlations and universal long-range scaling in kagome metals
<p>Dataset for the constrained Random Phase Approximation calculations shown in the manuscript. The VASP OUTCAR (ascii plain texy file) contains the whole cRPA U_iijj, U_ijij and U_ijji tensors, from which the numbers in the table of the paper (Supplementary Material) are extracted. Input files necessary to reproduce the results are also in the repository.</p>
Swansea University Wind Tunnel Gust Generator
<p>Initial experimental test of the gust generator at the Swansea University wind tunnel. As a preliminary study smoke test has been used to prove the concept. Experimental data collected from a cross-hot wire sensor indicate that the system is reliably capable of creating single and continuous gusts.</p> <p>More information:</p> <p>[1] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. Journal of Aircraft, https://doi.org/10.2514/1.C036772.</p> <p>[2] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. AIAA 2022-2485. <em>AIAA SCITECH 2022 Forum</em>. January 2022.</p>
Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>This file is a detailed Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
Dataset of the survey on the digital transformation of business processes at universities in Germany
<p>In spring 2022, a broad survey was conducted among all 423 German universities on the status and the future of the digitalisation of administrative business processes.</p> <p>During the research survey, participants gave or rejected their consent for the publication of their contribution as open data. Respecting the roughly 30% of participants who declined, we published all remaining data here on Zenodo. The raw version of the dataset contains filtered, but pseudonymised data. The second version of the dataset is modified by the quality control process as described in section 3.3. of the corresponding Data Paper.</p> <p>The published data also includes the catalogue of questions indicating the respective category of time and competency pattern in which the question was used. This catalogue also contains the match with the PDF printout of the web-based questionnaire and the corresponding variables (V1-V1073) of the data records.</p> <p>Finally, the general introduction as well as the proposed letter to the target groups are included in the data files for reference.</p>
Predicting mobility and research performance of the faculty members in the economics departments at Turkish public universities
<p>The data used for the regression analysis is in Table 5 on page 17 of the paper. </p>
Role of the RIO (Research Integrity Officer) at the University of Florida
<p><strong>Role of the RIO</strong><br> Learn more about the importance of research integrity at UF and the role of the Research Integrity Officer (RIO) in helping you navigate potential instances of research misconduct. Contact <a href="mailto:RIO@research.ufl.edu">RIO@research.ufl.edu</a> for assistance.</p>
Data from paper analysing the Quota Law in Federal Universities of Brazil in SISU 2021
<p><strong>Portuguese: </strong>Material Suplementar de artigo analisando cotistas preenchendo vagas de ampla concorrência em universidades federais: a respeito da concorrência concomitante no SISU 2021-1, relacionada à Lei de Cotas e ações afirmativas no Brasil.</p><p><strong>English: </strong>Supplementary Material for paper about affirmative actions and Quota Law in Brazil in SISU 2021.</p>
Data Availability Statements in the 2020 and 2021 scientific publications of Tampere University
<p>For this dataset, scientific peer-reviewed articles by Tampere University researchers from the years 2020 and 2021 were extracted from the TUNICRIS. A random sample of 40 percent was taken from the listed 4,922 publications according to faculties and years. There were 2,085 analyzed articles, i.e. more than 42 percent of the total number. </p> <p>To find Data Availability Statements, articles were opened one by one and searched for mentions of research data and its availability. For each article, it was written down whether DAS existed and where in the article it was located. From the contents of DAS, information about data availability, location, openness and possible restrictions on use was written down. </p> <p>Dataset also includes information about the journals and publications taken from TUNICRIS. </p> <p>The prevalence of DAS and data openness were examined in relation to different variables. Tampere University faculty information has been removed from the dataset. </p> <p>Related slides: https://doi.org/10.5281/zenodo.7655892</p> <p>Related article (in Finnish): Toikko, T., & Kylmälä, K. (2023). Tutkimusdatan saatavuustiedot tieteellisissä artikkeleissa: Raportti Data Availability Statementien käytöstä Tampereen yliopistossa. <em>Informaatiotutkimus</em>, 42(1-2), 31–50. https://doi.org/10.23978/inf.126098</p>
Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)
<p>This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen.</p> <p>In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data.</p> <p>Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from <strong>one </strong>CPS component (i.e., production station) at <strong>one </strong>point in time.</p> <p>The data set contains the following files</p> <ul> <li>low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators <ul> <li>*.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory</li> </ul> </li> <li>camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution</li> <li>camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution</li> </ul> <p>Check the following publications to learn more about our research using the model factory:</p> <p>Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In <em>Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18</em> (pp. 95-107). Springer International Publishing.</p> <p>Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In <em>2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)</em> (pp. 20-26). IEEE.</p> <p>Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. <em>Journal of Manufacturing Systems</em>, <em>63</em>, 575-592.</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.