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867 results for “repositories”
IXI025 - IT'IS Template Head Segmentation Repository
<p>The IXI025 head model is based on the IXI dataset (subject 025) and includes a whole head segmentation, surface-based model, anatomical images, diffusion weighted images, and fiducials for 10-10 system.</p>
Research data repository survey data (European Research Data Landscape study)
<p>Anonymised data of the research data repository survey for the European Research Data Landscape study.</p>
Data Repository for MYRiAD: A Multi-Array Room Acoustic Database
<p>In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1s and 0.5s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. For a detailed description, please refer to the paper (<a href="https://arxiv.org/abs/2301.13057">preprint</a>, <a href="https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-023-00284-9">published</a>).</p> <p>Two files are provided, containing two different versions of the database:</p> <table> <tbody> <tr> <td><strong>MYRiAD_V2.zip</strong></td> <td>The full version of the database (31.3 GB).</td> </tr> <tr> <td><strong>MYRiAD_V2</strong><strong>_econ</strong><strong>.zip </strong></td> <td>The economy-sized version, containing source signals and RIRs only (201.7 MB).</td> </tr> </tbody> </table> <p>If you use the database, please cite the paper as follows:</p> <p>@article{dietzen2023myriad,<br> author = {Dietzen, T. and Ali, R. and Taseska, M. and van Waterschoot, T.},<br> title = {{MYRiAD}: A Multi-Array Room Acoustic Database},<br> journal = {EURASIP J. Audio Speech Music Process.},<br> volume = {2023, article no. 17},<br> number = {},<br> month = {Apr.},<br> year = {2023},<br> pages = {1--14}<br> }</p> <p>___________________________________________________________________________________________________________</p> <p>Change log (as compared to Version 1.0):</p> <ol> <li>Fixed erroneous file names in /audio/AIL/SU1/P2/.</li> <li>In the full version, applied a time shift to some of the speech, noise, and music recordings in the SAL (at most 2 samples, compensating for a slow phase drift, see manuscript for further details).</li> <li>Created an economy-sized version of the database containing source signals and RIRs only.</li> <li>Adjusted the following scripts for the economy-sized version: <br> - /tools/MATLAB/load_audio_data.m<br> - /tools/Python/load_audio_data.py</li> </ol>
The National Archives Accessions to Repositories Data c.2007 - 2020
<p>The Annual Accessions to Repositories survey is a UK-wide exercise conducted by the National Archives that assesses what is being collected by UK repositories. The primary purpose of this exercise is to place some of this information onto TNA’s search engine Discovery. More recently, the data has been used to communicate accessions trends to the wider archives sector including information on what is being collected and where. Each year, TNA sends out survey templates in the form of Excel spreadsheets that are sent out to repositories in each part of the UK. The returns sent to TNA include information on the size of the record, the dates it covers, the creator of the record and a description of the record. Work has been undertaken since October 2021 to to merge and standardise the accessions data held by TNA. This data repository presents the merged dataset.</p>
iDPP@CLEF 2022 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge
<p><a href="https://brainteaser.health/open-evaluation-challenges/idpp-2022/">iDPP@CLEF 2022</a> (Intelligent Disease Progression Prediction at CLEF) is a challenge organised by the <a href="https://brainteaser.health/">BRAINTEASER</a> Horizon 2020 project and co-located with <a href="https://clef2022.clef-initiative.eu/">CLEF 2022</a> (Conference and Labs of the Evaluation Forum). </p> <p>BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.</p> <p>The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to:</p> <ul> <li> <p>Better describe<strong> disease mechanisms</strong>.</p> </li> <li> <p><strong>Stratify patients </strong>according to their phenotype assessed all over the disease evolution.</p> </li> <li> <p><strong>Predict disease progression</strong> in a probabilistic, time dependent fashion.</p> </li> </ul> <p>iDPP@CLEF 2022 offered the following tasks:</p> <ul> <li> <p><strong>Pilot Task 1 – Ranking Risk of Impairment</strong>: It focuses on ranking of patients based on the risk of impairment in specific domains. More in detail, we will use the ALSFRS-R scale to monitor speech, swallowing, handwriting, dressing/hygiene, walking and respiratory ability in time and will ask participants to rank patients based on time to event risk of experiencing impairment in each specific domain.</p> </li> <li> <p><strong>Pilot Task 2 – Predicting Time of Impairment</strong>: It refines Task 1 asking participants to predict when specific impairments will occur (i.e. in the correct time-window). In this regard, we assess model calibration in terms of the ability of the proposed algorithms to estimate a probability of an event close to the true probability within a specified time-window.</p> </li> <li> <p><strong>Position Papers Task 3 – Explainability of AI algorithms</strong>: We call for proposals of different visualization frameworks able to show the multivariate nature of the data and the model predictions in an explainable, possibly interactive, way.</p> </li> </ul> <p> </p> <p>This dataset contains the repositories of the participants to iDPP@CLEF 2022. These repositories contain the output, i.e. the predictions, produced by the participating systems as well as the performance scores for those systems.</p> <p>For additional information about iDPP@CLEF 2022, please see:</p> <ul> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In Barr ́on-Cedeno, A., Da San Martino, G., Degli Es- posti, M., Sebastiani, F., Macdonald, C., Pasi, G., Hanbury, A., Potthast, M., Faggioli, G., and Ferro, N., editors, <em>Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022)</em>, pages 395–422. Lecture Notes in Computer Science (LNCS) 13390, Springer, Heidelberg, Germany.</p> </li> <li> <p>Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge. In Faggioli, G., Ferro, N., Hanbury, A., and Potthast, M., editors, <em>CLEF 2022 Working Notes</em>, pages 1130– 1210. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073. <a href="https://ceur-ws.org/Vol-3180/paper-88.pdf">http://ceur-ws.org/Vol-3180/</a>.</p> </li> </ul>
Seaweed In Nuclear Winter Data Repository
<p>The Seaweed In Nuclear Winter Data Repository contains all the necessary environmental data to run the Seaweed Growth Model and simulate the potential growth of seaweed in the aftermath of a nuclear war. This data is derived from ocean simulations for a nuclear winter scenario and includes a control run and simulations for different levels of soot emissions into the atmosphere, ranging from 5 to 150 Tg. The data in this repository can be used as input for the Seaweed Growth Model, which is available in a separate repository (<a href="https://github.com/allfed/Seaweed-Growth-Model">https://github.com/allfed/Seaweed-Growth-Model</a>). The model simulates the growth of seaweed in a nuclear winter scenario. Instructions on how to run it can be found in the code repository.</p>
WorldCereal open global harmonized reference data repository (CC-BY-SA licensed data sets)
<p>Within the<strong> ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY-SA license or a license similar to CC-BY-SA. See document "_In-situ-data-World-Cereal - license - CC-BY-SA.pdf" for an overview of the original data sets.</p>
WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)
<p>Within the <strong>ESA funded </strong>WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets. </p>
Nudging Repository – Set of Nudges (DyMoN Project)
<p>This file provides the DyMoN nudging repository that is a set of digital behaviour change techniques (i.e., nudges) which are suitable to be used as push notifications for a mobile application in order to motivate sustainable mobility (walking, bicycling, public transport).</p>
Data repository - The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios
<p>This datasets provides regional and spatial-explicit gridded data for the analysis presented in the manuscrip "The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios" under review in "Environmental Research: Climate" with reference "ERCL-100126"</p>
SMT-Solving Induction Proofs of Inequalities Benchmarking Repository
<p>This repository contains the full list of files and the benchmarking results that were used in the benchmarking processes described in the paper:<br> A.K. Uncu, J.H. Davenport and M. England. "SMT-Solving Induction Proofs of Inequalities". Proceedings of the 7th International Workshop on Satisfiability Checking and Symbolic Computation (SC^2 2022). </p> <p>The files are split in three branches. The Mathematica and Maple files include the calls that were made to the respective computer algebra systems, and the smt2 files are the ones used by the considered SMT solvers: Z3, CVC5 and Yices.</p> <p>The Benchmarking Results cvc has the results. The columns record the file names, the satisfiability outcome of the calls, then the times (in seconds) of the respective programmes. Any empty box (which the Maple:-RegularChains column has) would mean that the implementation does not accept that sort of input (this is due to rational functions - see the paper for details). Any time over 1200 seconds would mean that the program times out and the outcome of the question was not found in the given time.</p> <p> </p> <p> </p>
Quantum critical dynamics in a 5000-qubit programmable spin glass: data repository
<p>Supporting data for "Quantum critical dynamics in a 5000-qubit programmable spin glass", Nature, 2023.</p>
Monteux et al., JGR Planets, 2023. Data Repository
<p>Source material to obtain the figures from the article entitled : Conditions for segregation of a crystal-rich layer within a convective magma ocean (JGR Planets 2023).</p> <p>The data that support the findings of this study were obtained using the commercial software COMSOL Multiphysics (version 5.4). COMSOL Multiphysics® is a simulation platform that provides fully coupled multiphysics and single-physics modelling capabilities. COMSOL Multiphysics (version 5.4) has been previously validated for two phase flow applications (Qaddah et al, 2019, Qaddah et al., 2020). To compute our simulations we used the Heat Transfer (www.comsol.com/heat-transfer-module) and Computational Flow Dynamics (www.comsol.com/cfd-module) modules in addition to the main Multiphysics platform (www.comsol.com/comsol-multiphysics). All the parameters used in our simulation are listed and described in the manuscript. The open source software used for data visualisation was Xmgrace (https://plasma-gate.weizmann.ac.il/Grace/). </p> <p>The software used for this study is the commercial software COMSOL Multiphysics (version 5.4) previously validated for two phase flow applications (Qaddah et al., 2019, Qaddah et al., 2020). User manual can be downloaded here:<br> https://doc.comsol.com/5.4/doc/com.comsol.help.cfd/CFDModuleUsersGuide.pdf. <br> A trial version of COMSOL Multiphysics may be requested (see www.comsol.com).<br> <br> More details can be found on the following COMSOL webpages:<br> - On the Foundations of the General Heat Transfer Equation:<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.heat/heat_ug_theory.07.002.html</p> <p>- Theory for Heat Transfer in Fluids :<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.heat/heat_ug_theory.07.008.html</p> <p>- The Euler–Euler Model Equations and in particular how incompressibility is handled:<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.cfd/cfd_ug_fluidflow_multi.09.153.html</p> <p>- The Boussinesq Approximation:<br> https://doc.comsol.com/6.1/docserver/#!/com.comsol.help.cfd/cfd_ug_fluidflow_noniso.07.20.html</p> <p> </p>
URLs with query strings for software in UK Academic Institutional Repositories.
<p>A set of exact URLs containing the query strings to search for software within UK Academic IRs.</p>
Data repository for manuscript "Contacting individual graphene nanoribbons using carbon nanotube electrodes"
<p>This is the raw data for the manuscript "Contacting individual graphene nanoribbons using carbon nanotube electrodes”.</p>
Data repository - BSc thesis Jara Schrandt
<p>This repository contains all data necessary to reproduce data of the BSc. thesis of Jara Schrandt [12645028]. Bsc. Future Planet Studies at the University of Amsterdam. Additionally, the thesis can be requested by contacting the author.</p>
Data repository for Pore Pressure Drop during Dynamic Rupture and Conditions for Dilatancy Hardening
<p>Simulation data to accompany publication Pore pressure drop during dynamic rupture and conditions for dilatancy hardening, submitted to Journal of Geophysical Research: Solid Earth. See Readme.txt for content of data files. The data were generated by 'GrandFrix' software and postprocessed by scripts written MATLAB – see Related identifiers.</p>
A repository of spherical (t,t)-designs
<p>Spherical (<em>t</em>,<em>t</em>)-designs in <strong>R</strong><sup>d</sup> are arrangements of points on the sphere S<sup>d-1</sup> (possibly with weights) which are spaced "far apart from each other": they are finite sets in such that the integral over the sphere of each homogeneous polynomial of degree 2<em>t</em> in <em>d</em> variables is equal to its average value on the set, and generalise the notion of a spherical <em>t</em>-design and half-design which can be found for example in Section 3.3 of Conway and Sloane (1993), and the notion of a tight frame from harmonic analysis (Waldron, 2018). There is a generalisation of the definition of a spherical (<em>t</em>,<em>t</em>)-design to complex point arrangements: a complex spherical (<em>t</em>,<em>t</em>)-design is a finite set on the complex (<em>d</em>-1)-sphere (again, possibly with weights) which integrates polynomials which are separately homogeneous in <em>d</em> variables and their conjugates, such that the total degree in the variables is <em>t</em> and the total degree in the conjugate variables is also <em>t</em>. Similar definitions can also be made over the quaternions and octonions (Waldron, 2020). For a more precise discussion, history, and a list of prior results and examples see the references list.</p> <p>This repository is a set of files containing various spherical (<em>t</em>,<em>t</em>)-designs and near-designs - point configurations which minimise the design potential function of Section 6.16 of Waldron (2018). These files were produced using the Manopt software (Boumal et. al., 2014), and the source code may be found in the <a href="https://github.com/aelzenaar/tightframes">aelzenaar/tightframes GitHub repository</a>.</p> <p>The easiest way to view the dataset is to download index.html and the four .tgz files; decompress the tar files so that index.html is in the same directory as the four *_out directories, and open index.html in a web browser. The design itself can then be found in either Magma format (a text file) or .mat format (open in Matlab, and then the design is found in the 'result' variable).</p> <p>In Elzenaar and Waldron (2025) we discuss many of the new designs which appear in these data.</p>
Simulated diagenesis of the iron-silica precipitates in banded iron formations: Data Repository
<p>This XRD dataset derives from experiments we performed bubbling 49 ppm O2 into simulated Archean seawater and then aging the produced precipitates at 25 degrees C, 80C, 150C, and 220C. Precipitate slurries were extracted from experimental samples and pipetted as 20 µL subsamples into Cole-Parmer Kapton tubes to keep anoxic during XRD analysis. Samples were sent to McMaster Analytical X-Ray Diffraction Facility (MAX) for XRD analysis using a Bruker D8 DISCOVER cobalt source tube (Co-XRD) with a DAVINCI.DESIGN diffractometer. More details on methods in associated article. Resultant XRD measurements of our samples yielded patterns showing increasing crystallinity with temperature. The bubbled experiment aged for 40 days at 25 °C produced a large and diffuse diffraction peak corresponding to the Kapton tube but no other sharp diffraction peaks, suggesting an amorphous to minimally crystalline product. A broad peak in the 25 °C precipitate, that persisted through the higher-temperature aging treatments, may correspond to ferrihydrite. The bubbled experiment aged at 80C contained diffraction peaks consistent with a serpentine group silicate and a spinel group oxide (like magnetite). After the 150 °C treatment, samples showed sharper peaks consistent with a serpentine group silicate and spinel group oxide. After 220 °C aging, the precipitates showed a continued narrowing of the diffraction peaks for a spinel group oxide, reflecting an increase in crystal size and/or crystallinity, but smaller and less sharp serpentine group peaks. </p>
MESOC repository of literature
<p>The MESOC repository is a database of relevant documentation reflecting the state of the art of social impacts studies in the field of cultural policy in the EU. It is the result of the collection, selection and analysis of pertinent literature covering all the three project dimensions: Health and Wellbeing, Urban and Territorial Renovation, People’s Engagement and Participation. It includes a wide array of resources, ranging from academic literature to case studies, project reports, policy briefs, etc. The online repository is instrumental for the identification of the most appropriate impact transmission variables and indicators and for analysing what have been the critical success factors in determining the final outcomes of the selected transition pathway.</p> <p>The collection process was coordinated by the University of Barcelona, with the support of all partners, including the 11 pilot cities participating in the MESOC project (Athens, Barcelona, Cluj-Napoca, Gent, Issy-les-Moulineaux, Jerez de la Frontera, Lublin, Milan, Rijeka, Turku, and Valencia). The final repository consists in 305 policy and grey documents and 672 scientific papers, leading to a total of 977 documents.</p> <p>The main purpose of the exercise of collecting the most relevant literature on social impacts of cultural policy was to answer the following research question: “What is the state of the art in terms of literature and documentation produced on the topic of social impacts of cultural policy?”.</p> <p>The contents of this repository can be searched online at the following URL: http://repository.mesoc-project.eu/</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.