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747 results for “open data”
Belvedere Glacier long-term monitoring Open Data
<p><strong>Introduction </strong></p> <p>This dataset contains extensive, long-term monitoring data on the Belvedere Glacier, a debris-covered glacier located on the east face of Monte Rosa in the Anzasca Valley of the Italian Alps. The data is derived from photogrammetric 3D reconstruction of the full Belvedere Glacier and includes:</p> <ul> <li><strong>dense point clouds</strong> obtained with UAV-based MVS covering the entire glacier body</li> <li>high-resolution<strong> </strong><strong>orthophotos</strong></li> <li>high-resolution<strong> </strong><strong>DEMs</strong></li> </ul> <p>Since 2015, in-situ survey of the glacier have been conducted annually using fixed-wing UAVs until 2020 and quadcopters from 2021 to 2022 to remotely sense the glacier and build high-resolution photogrammetric models. A set of ground control points (GCPs) were materialized all over the glacier area, both inside the glacier and along the moraines, and surveyed (nearly-) yearly with topographic-grade GNSS receivers (Ioli et al., 2022).</p> <p>For the period from 1977 to 2001, historical analog images, digitalized with photogrammetric scanners and acquired from aerial platforms, were used in combination with GCPs obtained from recent photogrammetric models (De Gaetani et al., 2021).</p> <p>Before downloading them, you can explore the photogrammetric point clouds of the Belvedere Glacier within web app based on Potree from <a href="https://thebelvedereglacier.it/" target="_blank" rel="noopener">https://thebelvedereglacier.it/</a> (use a web browser from a desktop/laptop for the best experience). Additionally, from here you can also visualize and download the coordinates of the GCPs measured by GNSS every year since 2015.</p> <p> </p> <p><strong>Belvedere Glacier </strong></p> <p>The Belvedere Glacier is an important temperate alpine glacier located on the east face of Monte Rosa in the Anzasca Valley of Italy. The Belvedere Glacier is of particular importance among alpine glaciers because it is a debris-covered glacier and it reaches its lowest elevation at about 1800 m a.s.l. Over the last century, the Belvedere Glacier has experienced extraordinary dynamics, such as a surge-like movement or the formation of a supraglacial lake, which seriously threatened the nearby community of Macugnaga.</p> <p> </p> <p><strong>Data organization</strong></p> <p>The data are organized by year in compressed zip folders named <em>belvedere_YYYY.zip</em>, which can be downloaded independently. Each folder contains all data available for that year (i.e. photogrammetric point clouds, orthophotos, and DEMs) and the corresponding metadata. Metadata is provided as a .json file which contains all the main information for data usage. Point clouds are saved in compressed las format (<em>.laz</em>)<em> </em>and they can be inspected e.g., with CloudCompare. Orthophotos and DEMs are georeferenced images (<em>.tif</em>) that can be inspected with any GIS software (e.g., <em>QGIS</em>).</p> <p>Large point clouds are subdivided into regular tiles, which are numbered in a progressive row-wise order from the bottom-left corner of the point cloud bounding box.</p> <p>All the files are named according to the following naming schema:</p> <p>"belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension"</p> <p>where: </p> <ul> <li>YYYY: is the year of the survey</li> <li>surveyplatform: can be either "uav" for the UAV-based photogrammetry survey or "histo" for the historical aerial datasets.</li> <li>datatype: can be either "pcd" for point clouds, "orthophoto" for orthophotos and "dsm" for DSMs. </li> <li>resolution: on-ground resolution of each pixel in meters. This applies only to raster data (orthophoto and DSMs)</li> <li>vertical_datum: if the DSM is given in orthometric coordinates, the label "ortho" is present in the filename, otherwise the height of the dataset is supposed to be ellipsoidal.</li> <li>tile: tile number, if the data is tiled to avoid large files.</li> </ul> <p><strong>Data Usage</strong></p> <p>This dataset can be used to estimate glacier velocities, volume variations, study geomorphological processes such as the process of moraine collapse, or derive other information on glacier dynamics. If you have any requests on the data provided, data acquisition, or the raw data themselves, you are encouraged to contact us.</p> <p> </p> <p><strong>Contributions</strong></p> <p>The monitoring activity carried out on the Belvedere Glacier was designed and conducted jointly by the Department of Civil and Environmental Engineering (DICA) of Politecnico di Milano and the Department of Environment, Land and Infrastructure Engineering (DIATI) of Politecnico di Torino. The DREAM projects (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring), involving teachers and students from Alta Scuola Politecnica (ASP) of Politecnico di Torino and Milano, contributed to the campaign from 2015 to 2017.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <div>The authors thank CGR SpA for digitizing the historical images (1977, 1991, 2001, 2009) and making them available to the authors for the photogrammetric processing.</div> <div>The authors thank all students and collaborators contributing to the Alta Scuola Politecnica projects DREAM 1, DREAM 2, and DREAM 3 (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring). </div> <div> </div> <div> </div> <p><strong>If you use the data, please, cite these our pubblications:</strong></p> <p>Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L., Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. <em>PFG</em> (2024). <a href="https://doi.org/10.1007/s41064-023-00272-w" target="_blank" rel="noopener">https://doi.org/10.1007/s41064-023-00272-w</a></p> <p>Ioli, F.; Bianchi, A.; Cina, A.; De Michele, C.; Maschio, P.; Passoni, D.; Pinto, L. Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier. Remote Sensing, 14, 28 (2022). <a href="https://doi.org/10.3390/rs14010028" target="_blank" rel="noopener">https://doi.org/10.3390/rs14010028</a></p> <p>De Gaetani, C.I.; Ioli, F.; Pinto, L. Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019. Remote Sensing, 13, 3787 (2021). <a href="https://doi.org/10.3390/rs13183787" target="_blank" rel="noopener">https://doi.org/10.3390/rs13183787</a></p>
Survey Data on Current Open Access Terms and Future Trends (2024)
<p><strong>Description:</strong><br>This dataset contains the analysis, codebook, and raw survey data from the 2024 survey <em>"Open Access – Current Terms and Future Areas of Focus"</em>. The survey aimed to gather perspectives from Open Access experts in the German-speaking region, focusing on the evaluation of current Open Access terminology, concepts, and emerging trends.</p> <p>The survey highlights how Open Access terminology has evolved over the past two decades and explores current perceptions regarding key terms in the Open Access discourse, as well as the anticipated future developments in this field. A total of 131 complete responses (<em>N=131</em>) were collected, providing valuable insights into the views of professionals working in Open Access publishing, information infrastructures, and scientific publishing houses.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>codebook_oa_2024_2024-11-21.xlsx</strong>: The codebook, including detailed explanations of the variables, codes, and definitions used in the survey.</li> <li><strong>survey_results_oa_2024_2024-11-21.xlsx</strong>: Anonymized raw data from the survey, including both quantitative and qualitative responses from the participants.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: CSV file containing the key terms and concepts identified by participants in response to the question on Open Access terminology.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: An additional CSV file with detailed classification and analysis of the terms related to Open Access, including their frequency and significance based on participant responses.</li> </ol> <p><strong>Methodology:</strong><br>The survey was conducted via an online questionnaire distributed from September 7 to October 15, 2024, to professionals working in Open Access, both within information infrastructures (e.g., libraries) and in academic publishing houses. The survey gathered both qualitative and quantitative data, focusing on how Open Access terminology is understood and its future developments. The data were cleaned, anonymized, and analyzed using appropriate statistical and content analysis methods.</p> <p><strong>Purpose and Use:</strong><br>This dataset is valuable for researchers and professionals studying Open Access terminology, trends, and future developments. It provides insights into the current understanding of Open Access within the academic community and can be used for comparative studies, policy analysis, and future Open Access research.</p>
A Decade of Progress: Open Data Practices in Bioscience at the University of Edinburgh
<p><strong>General Information:</strong></p> <p>This reposotory contains the outcomes of a project executed at the Biosciences Institutes of the University of Edinburgh. This research project assesses the openness and FAIRness (Findable, Accessible, Interoperable, and Reusable) of data linked to publications from these institutes. Here, you will find datasets, analytical codes, and figures that detail our project’s methodology and results aiming to enhance data-sharing practices and promote the adherence to FAIR principles within and beyond our community. </p> <p>This repository is linked to a publication that has been submitted to: Proceedings of the Royal Sociaty B - Biological Sciences</p> <p>The main project: You can find the main repository and workspace of this project on Github containing the data and code of this project and all the previous related projects: <a href="https://github.com/BioRDM/InsightsOfOpenPracticesInBiosciences">Here</a></p> <p><strong>The Protocol:</strong></p> <p>The protocol for this project can be found on Protocol.io, where detailed step-by-step guidelines are provided to ensure that the research methods are transparent and reproducible. <a href="https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2" rel="nofollow">https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2</a></p> <p>The main project</p> <p><strong>Contact us:</strong></p> <p>for General Queries, Collaboration and Data Management: <em>bio_rdm@ed.ac.uk (<a href="https://biology.ed.ac.uk/research/facilities/research-data-management">BioRDM</a>) </em>or the Principal Investigator and Corresponding Author: Andrew Millar (<em>andrew.millar@ed.ac.uk</em>) - Orcid: 0000-0003-1756-3654</p> <p><strong>Data Collection</strong></p> <p>The Dataset of this project was collected in two different periods by the honour students (Creasey, de Ugarte, Strevens, Usman, Yun Wong) in our department:<br>- Project one from Januray 2023 to June 2023<br>- Project two from January 2024 to June 2024</p>
OpenFOAM cases of the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers"
<p>This dataset contains the<em> underling data</em> for the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers”, submitted for the consideration and open review in Open Research Europe (ORE).</p> <p><strong>Validation1.tar.xz:</strong> OpenFOAM files and scripts for the simulation of flow and thermal structures in an enclosed environment (Wang and Chen, 2009).</p> <p><em>Wang, Miao; Chen, Qingyan (2009). Assessment of Various Turbulence Models for Transitional Flows in an Enclosed Environment (RP-1271). HVAC&R Research, 15(6), 1099–1119. doi:10.1080/10789669.2009.10390881</em></p> <p><strong>Validation2-kOmegaSSTModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using k-omega SST turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation2-RNGkEpsilonModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using RNG k-epsilon turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation3.tar.xz:</strong> OpenFOAM files and scripts for the simulation of strong natural convection in a model fire room (Murakami et al. 1995).</p> <p><em>Murakami, S., S. Kato, and R. Yoshie. 1995. Measurement of turbulence statistics in a model fire room by LDV. ASHRAE Transactions 101(2):287–301.</em></p> <p><strong>Validation4.tar.xz:</strong> OpenFOAM files and scripts for the simulation of thermal distribution in an open-aisle data center (Abdelmaksoud et al. 2013).</p> <p><em>W.A. Abdelmaksoud, T.Q. Dang, H. Ezzat Khalifa, R.R. Schmidt Improved computational fluid dynamics model for open-aisle air-cooled data center simulations J. Electron. Packag., 135 (2013), pp. 030901-30913</em></p> <p><strong>Results_Validation1.tar.xz:</strong> Simulation results of the Validation case 1.</p> <p><strong>Results_Validation2.tar.xz:</strong> Simulation results of the Validation case 2.</p> <p><strong>Results_Validation3.tar.xz:</strong> Simulation results of the Validation case 3.</p> <p><strong>Results_Validation4.tar.xz:</strong> Simulation results of the Validation case 4.</p> <p><strong>layout.csv:</strong> Input file for the Validation case 4.</p>
L3Pilot Open Data
<p>The L3Pilot Open Data contains processed data collected during the Piloting of pre-series automated prototype vehicles on public European roads.</p> <p>The dataset contains driving data in the form of performance indicators derived for all instances of certain driving scenarios such as Car Following or lane changes.</p> <p>Furthermore, it contains data from the questionnaires handed to both professional and ordinary driver piloting the vehicles.</p> <p>All data is provided as comma separated tables. The supplementing document provides all necessary information for working with the dataset and mentions all documents, where additional information can be found.</p> <p>Already executed analysis based on the collected data, of which this represents a subset, can be found in Deliverable D7.3 - Pilot Evaluation Results, available for Download at <a href="https://l3pilot.eu/downloads">l3pilot.eu/downloads</a><br> <br> Further publicly available dataset, such as trajectory data recorded with drones and an additional user survey, are linked on: <a href="https://l3pilot.eu/data">l3pilot.eu/data</a></p>
Unsteady Aerodynamics Open Data Set
<p>A selection of four different unsteady aerodynamic experiments have been done to prepare a database which will serve for the analysis, investigation and tool validation of airfoil unsteady behavior of wind turbine blades.<br> The four experiments and selected data are:</p> <ul> <li>University of Glasgow dynamic stall experiments: NACA0015 and NACA0030 airfoils tested at sinusoidal type motion of the pitch.</li> <li>NREL OSU experiments: LS(1)0417MOD, NACA4415 and S809 airfoils tested at sinusoidal type motion of the pitch.</li> <li>CENER unsteady airfoil pitching and flapping tests at DTU: NACA643-418 airfoil tested at sinusoidal type motion of the pitch, the flap and combined pitch and flap.</li> <li>ForWind airfoil tests under tailored inflow turbulence: DU00W212 airfoil with laminar flow, open grid condition and one sinusoidal dynamic grid condition.</li> </ul>
Data of European University Association (EUA) Open Access Survey 2017-2018
<p>This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html">https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:</p> <ul> <li>Questionnaire</li> <li>Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv</li> <li>Codebook: includes information on all the variables and their coding.</li> </ul>
CMS 2011A Open Data | Jet Primary Dataset | pT > 375 GeV | MOD HDF5 Format
<p>A dataset of 1,785,625 jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.UP77.P6PQ">Jet Primary Dataset of the CMS 2011A Open Data</a> reprocessed into the MOD HDF5 format. Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor. Particle Flow Candidates (PFCs) for each jet are provided and include information about the PFC kinematics, PDG ID, and vertex. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>There are corresponding datasets of simulated jets organized by hard parton <span class="math-tex">\(\hat p_T\)</span> also available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul>
Data for "Breaking the Paywall: The role of Open Journal System as key Open Science infrastructure"
<h3><strong>Context</strong></h3> <p>This research was conducted within the NSF-SEEKCommons Project, a research initiative dedicated to supporting Open Science and Open Access in disciplinary research. The project has a special interest in understanding the role that critical infrastructure has in supporting open initiatives. The Open Journal System (OJS) serves as a long-standing fundamental piece for Open Access throughout the globe. Hence, it provides valuable information about experiences developing, deploying, and maintaining open technologies. </p> <h3><strong>Methods<br></strong></h3> <div> <div>We used mixed methods for our research, triangulating repository data, installation data, interviews, and documentary analysis. We collected repository data using a report generator (Kopp [2018] 2024) that uses repository metadata to present general statistics about a Git project. The resulting information was manually curated, disambiguated, and annotated to have a homogeneous set of developers with information about their institutional affiliation and country. </div> <div> </div> <div>Names are normalized based on the information in qualitative interviews and by browsing the full-extent commits in the GitHub repository. Other sources for this were the institutional materials (available in current and archived versions of the PKP website), meeting minutes, the user forum, and further project documentation available online. GitHub handles are homologated to their most comprehensive version. For institutional and country affiliation, we resorted to GitHub profiles, PKP documentation and forums, institutional domains available in emails, and researchers' ORCID IDs. </div> </div> <h3><strong>Available files</strong></h3> <ol> <li><strong>Information about the codebase</strong> (number of files, lines of code, and timestamp) organized by <strong>month, quarter, and semester. </strong><br>See file: OJS_GitStats_04-24.csv</li> <li>Information about the historical evolution of the codebase (number of files, lines of code, and timestamp), including <strong>a description of the top committers for each month</strong>. Commiters are described by including their institutional affiliation and country of origin. <br>See file: OJS_DevStats_Institution-Country_1.tsv</li> <li>Information about the <strong>historical evolution of the codebase </strong>focusing on <strong>top committers</strong>, along with their institution and country. This file is formatted to map the co-occurrence of developers and attributes by month between 2004-2024.<br>See file: OJS_DevStats_Institution-Country_2.tsv</li> <li>Selected fields to describe<strong> working and regularly maintained plugins for OJS as of October 2024.</strong> Includes name of the plugin, homepage, description, maintainer, and institutional affiliation. <br>See file: OJS_Plugins_2024_Processed.tsv</li> <li>Details of the aggregated <strong>information</strong> included in <strong>Table</strong> <strong>5</strong> of the article.<br>See file: OJS_Plugins_2024_Table5.tsv</li> <li><strong>Snapshot</strong> to XML information of the <strong>plugin gallery of OJS </strong>(October 21) retrieved from PKP website (Smecher 2024)<br>See file: OJS_Plugins_2024.csv</li> </ol> <h3>Funding</h3> <p><span>The SEEKCommons Project is funded by the U.S. National Science Foundation (NSF), grant #2226425</span></p>
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
Alpha-2 Adrenoreceptor Antagonist Yohimbine Potentiates Consolidation of Conditioned Fear (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Panitz, C., Skoluda, N., Nater, U. M., Pizzagalli, D. A., Hermann, C., & Mueller, E. M. (2022). Alpha-2 adrenoreceptor antagonist yohimbine potentiates consolidation of conditioned fear. <em>International Journal of Neuropsychopharmacology</em>, 25(9), 759–773.</strong></p> <p><em>Background:</em> Hyperconsolidation of aversive associations and poor extinction learning have been hypothesized to be crucial in the acquisition of pathological fear. Previous animal and human research points to the potential role of the catecholaminergic system, particularly noradrenaline and dopamine, in acquiring emotional memories. Here, we investigated in a between-participants design with 3 groups whether the noradrenergic alpha-2 adrenoreceptor antagonist yohimbine and the dopaminergic D2-receptor antagonist sulpiride modulate long-term fear conditioning and extinction in humans.<br><em>Methods:</em> Fifty-five healthy male students were recruited. The final sample consisted of n = 51 participants who were explicitly aware of the contingencies between conditioned stimuli (CS) and unconditioned stimuli after fear acquisition. The participants were then randomly assigned to 1 of the 3 groups and received either yohimbine (10 mg, n = 17), sulpiride (200 mg, n = 16), or placebo (n = 18) between fear acquisition and extinction. Recall of conditioned (non-extinguished CS+ vs CS−) and extinguished fear (extinguished CS+ vs CS−) was assessed 1 day later, and a 64-channel electroencephalogram was recorded.<br><em>Results:</em> The yohimbine group showed increased salivary alpha-amylase activity, confirming a successful manipulation of central noradrenergic release. Elevated fear-conditioned bradycardia and larger differential amplitudes of the N170 and late positive potential components in the event-related brain potential indicated that yohimbine treatment (compared with a placebo and sulpiride) enhanced fear recall during day 2.<br><em>Conclusions:</em> These results suggest that yohimbine potentiates cardiac and central electrophysiological signatures of fear memory consolidation. They thereby elucidate the key role of noradrenaline in strengthening the consolidation of conditioned fear associations, which may be a key mechanism in the etiology of fear-related disorders.</p>
Open Surface Drifter Data - Pirita river
<p>This dataset contains the surface drifter tracks collected in Pirita River (Estonia) to test the open drifter presented in the publication https://doi.org/10.3390/s22249918</p>
Introductory Motus Prioritization Tool Data (Open first)
Addressing survival and movement of priority migratory avian species of concern along the Pacific Flyway is paramount for their conservation. Yet, the migratory life stage is understudied in many avian species. The Motus radiotelemetry receiver network is an established system for tracking survival and movement of avian species. This network is an international collaborative that successfully identifies stopover site duration, connected migratory routes, post-fledging dispersal and survival, and adult survival and fidelity on a landscape-scale; parameters that cannot be easily estimated using non-tagged birds. While the Motus network is highly connected in eastern North America, the western part of the continent is lagging in coverage and connectivity, limiting the ability to obtain sample sizes large enough to robustly model demographic parameters from tagged birds. Thus, the expansion of the Motus network is a high priority for Pacific Flyway State Agencies. To date, no method exists for determining priority locations for new Motus receiving stations. With collaborations from States and the Canadian Province of British Columbia, we used eBird citizen scientist data to prioritize strategic locations for new Motus receiving stations throughout the Pacific Flyway. We model priority species’ co-occupancy of varying abundance states (i.e., absent, present, abundant, abundant in multiple weeks) with spatially varying Landsat (red and near infrared), water, land cover types, and weather covariates while accounting for variable detection with temporally varying survey effort covariates. Using occupancy model predictions, we identify high-use areas of the Pacific Flyway for establishing new Motus receiving towers that have high probabilities of intercepting high presence and /or abundance of multiple species of interest in a series of predictive occupancy maps. This package contains required files to recreate the data analysis, print out maps based on predictions from the
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.
User-centered Usability Analysis of 41 Open Government Data Portals
<p>The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.</p>
Cayla et al., 2020, Wellcome Open Research - Underlying data
<p>Underlying dataset for the identifications of low-complexity regions (LCRs) in the proteome of Trypanosoma brucei.</p> <p>- Supplement File 2.xlsx (Position of every InterPro domain and LCR identified. All genes are provided with indication on chromosome localisation, presence of transmembrane domains, signal peptides and the localisation of the encoded proteins, either predicted using DeepLoc<sup>37</sup> or observed (Tryptag<sup>38</sup>)).</p> <p>- Supplement File 3.xlsx (List of genes and Molecular Function gene ontology (GO) enrichment analysis of proteins with predicted LCRs in the N-terminal, central part or C-terminal or the different possible combinations.)</p> <p>- Supplement File 4.xlsx (Property analysis of sequences of every InterPro and LCRs identified.)</p> <p>- Supplement File 5.xlsx (List of genes and Molecular GO enrichment analysis of proteins presenting a Low (<8) or High (>9) polarity index level.)</p> <p>- Supplement File 6.xlsx (List and position of PTMs present on InterPro domains and LCRs. The different datasets from which the PTMs have been extracted can be found in the Zhang2020, Benz2019, Cayla2019, Urbaniak2013, Ooi2020, Fisk2012, Lott2012 and Moretti2017<sup>12,13,15,17–19,27,28</sup> columns. The sequence properties of the domains/LCRs on which these PTMs are located are also indicated. The list of modifications identified in Ooi <em>et al.</em> 2020<sup>28</sup> present on LCRs are indicated in the second sheet.)</p> <p>- Supplement File 7.xlsx (List and position of LCRs, signal peptides and their overlap.)</p>
Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong></p> <p>Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.</p>
Global Naturalized Alien Flora (GloNAF). Open access data to support research on understanding global plant invasions.
<p>This dataset is a snapshot of the Global Naturalized Alien Flora (GloNAF) database, version 2.02. GloNAF is a continuously updated, curated compilation of alien naturalized vascular plant inventories for geographic regions from around the world. The dataset has 16,429 unique taxa reported as naturalized or invasive and covers 1,343 regions (including 427 islands) from 336 data sources. For each region, the status (invasive, naturalized) is provided as listed in the original source. We provide the scientific names included with the original data source, and the matching accepted name or synonym of the taxon as given in the World Checklist of Vascular Plants (WCVP) Version 12. In addition, we provide an ESRI shapefile of polygons for each region. We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources.</p> <p>The 'glonaf_flora2.csv' file lists the IDs ('taxon_wcvp_id') of all naturalized taxa contained in GloNAF and the regions they occur in. The 'glonaf_taxon_wcvp.csv' lists the original taxon names provided in the source data along with the corresponding accepted taxon name from the WCVP (version 12) for all alien taxa in GloNAF, regardless of their naturalization status. To link taxon names with naturalization records, join the 'id' column of the 'glonaf_taxon_wcvp.csv' file to the 'taxon_wcvp_id' column in 'glonaf_flora2.csv' . Additional information regarding the original source of the data ('glonaf_reference.csv'), specific attributes of the taxon lists ('glonaf_list.csv') and the region ('glonaf_region.csv') can also be joined similarly to 'glonaf_flora2.csv '. </p> <p> </p>
BASE-9 binarity and stellar masses from Gaia DR3, 2MASS, and Pan-STARRS data for six open clusters: NGC 2168, NGC 7789, NGC 6819, NGC 2682, NGC 188, NGC 6791
<h2>Data sets as described in "Goodbye to Chi-by-Eye: A Bayesian Analysis of Photometric Binaries in Six Open Clusters", Childs et al. 2023 <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract">https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract</a></h2>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></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.