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Dataset results
209 results for “Anonymity”
DATASET of INTEGRADDE Expression of Interest, including aggregated and anonymized results.
<p>This dataset corresponds to the one generated with information about those applying to the Expression of Interest (EOI) including participants’ attributes, e.g. country of origin, TRLs, type of applicants, scores obtained in the evaluation process, and funding status after the funnel process. All of this, for statistical purposes and analysis of the performance of the Expression of Interest</p>
Small-angle Scattering Data Analysis Round Robin: anonymized results, figures and Jupyter notebook
<p>The intent of this round robin was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.</p> <p>This zip file contains the anonymized results and the jupyter notebook used to do the data processing, analysis and visualisation. Additionally, TEM images of the samples are included. </p>
COP25 anonymized dataset
<p>This postgresql file is the result of a collective intelligence experiment which took place in the framework of COP25 in Madrid in December 2019.</p> <p>The tables in the file are described below.</p> <p>answer_distances: No relevance. </p> <p>answers: Each answer given to a question by a user is logged. Each answer is composed of 5 different items, which are individual answers to the question. The same user can make several answers, at various times during the exercise, which is also recorded.</p> <p>answers_items: Each of the items is associated with the ID of the answer to which it belongs. </p> <p>answers_solutions: Each answer is associated to a solution, that is, the position in the network associated to the user for that project.</p> <p>ar_internal_metadata: Not relevant. </p> <p>delayed_jobs: Not relevant. </p> <p>delayed_jobs_id_seq: No relevance. </p> <p>event_logs: Each of the phases of the project, when they have started and what has happened in those phases. </p> <p>history_logs: No relevance. </p> <p>item_solutions: Each item is associated to a solution.</p> <p>item_swaps: No relevance. </p> <p>items: Each item is a participant's answer to the question asked in the project. </p> <p>neighbor_logs: Not relevant. </p> <p>net_logs: Not relevant. </p> <p>options: No relevance.</p> <p>projects: Each question asked to the participants is associated to a project ID.</p> <p>questions: The different questions that have been asked in different experiments of the tool are displayed. Only those related to SDG #6 and #13 are collected in this database.</p> <p>registration_requests: Not relevant. </p> <p>schema_migrations: Not relevant. </p> <p>solution_swaps: In some phases of the projects the solution assigned to a user can change, this is the equivalent to change the user's position, having different neighbors therefore. </p> <p>solutions: For each project the user receives a solution ID, this is located in an x,y matrix, so we can know which solutions are adjacent to each other (for x, y; x-1, x+1, y-1, y+1). </p> <p>users: Each participant receives a user ID that appears in this table.</p>
FT52 Anonymous (9)6-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT52 Anonymous (9) 6-key fagottino located in the Horniman Museum, London (Reg. nr. 14.5.47/278), containing detailed external and internal measurements, photos, and an endoscopic video. </p>
FT51 Anonymous (3) 10-key tenoroon: measurements, photos, endoscopic video
<p>Dataset of FT51 Anonymous 10-key tenoroon located in the Horniman Museum, London (Reg. nr. 2004.1044), containing detailed external and internal measurements, photos, and an endoscopic video. </p> <p> </p>
FT6 Anonymous (7) 8-key tenoroon: measurements, photos, endoscopic video
<p> Dataset of FT6 Anonymous (7) 8-key tenoroon containing detailed external and internal measurements, photos, and endoscopic video.</p>
Daisy anonymized wearable raw data
<p>This is the raw wearable dataset from the paper by the authors, titled "Feasibility and patient acceptability of a commercially available wearable and a smartphone application in identification of motor states in Parkinson<strong>’</strong>s disease", PLOS Digital Health 2023, DOI 0.1371/journal.pdig.0000225</p> <p>The HDF5 file has groups as subjects, P_* denoting patients and C_* denoting controls. </p> <p>Within each group, index "1" denotes accelerometer data, other indices are heart rate and other data not used in the study. For example, /P_fd3e/1/timestamp is the timestamp of the accelerometer data of (anonymized) patient P_fd3e, and /P_fd3e/1/y are their corresponding values measured from the y-channel of the device. </p> <p> </p> <p> </p>
Investigating PhDs' early career occupational outcomes in Italy: individual motivations, role of supervisor and gender differences (Anonymized UniTo dataset)
<p>Anonymized dataset to replicate the UniTo analysis in Carriero <em>et al.</em> (2023).</p> <p><em>If you use the data, please cite</em>:</p> <p>Carriero, R., Coda Zabetta, M., Geuna, A., & Tomatis, F. (2023). Investigating PhDs’ early career occupational outcomes in Italy: Individual motivations, role of supervisor and gender differences. <em>Higher Education</em>. (<a href="https://doi.org/10.1007/s10734-023-01068-y">https://doi.org/10.1007/s10734-023-01068-y</a>)</p>
REACT TAM Dataset Zenodo Anonymized
<p>Dataset from TAM questionnaire performed in all three pilot islands.</p>
enCompass-datasets: Anonymized datasets of enCompass project
<p>Anonymized datasets of enCompass project.</p> <p>The enCOMPASS project collected data related to energy consumption in households, in schools and in public buildings, in three pilots located in Germany, Greece and in Switzerland.<br> <br> This repository contains enCompass datasets of behavioral data, households electrical consumption, sensor measurements data (indoor temperature, luminance and humidity) and anonymized user related data.</p> <p>Data in the repository are organized as follow:</p> <p>A metadata file enCOMPASS_METADATA.txt.<br> behavioral_datasets directory containing behavioral datasets.<br> household_consumption_and_sensor_data directory containing consumption and sensor datasets and a metadata file named enCOMPASS_consumption_sensor_METADATA.txt</p>
Open anonymous repo hosting code and data for our submission in ASE 2020
<p>This repository presents sample publicly available anonymous source code and data for our submission in ASE 2020 conference.</p> <p>ProgressDroid source code is provided.</p> <p>Data for 10 top apps with the highest number of installs from our dataset are presented.</p> <p>For each app, we provide the following information:</p> <p>- The original APK file for the examined app.</p> <p>- The instrumented APK file using the extended Instrumenter module</p> <p>- Complete trace from running the extended AndroidSlicer tool on each app</p> <p>- Complete list of all UI update points in each specific app</p> <p>- List of slicing criteria for dynamic slicing </p> <p>- List of slices from the automated dynamic slicing analysis </p> <p>- List of all progress indicator occurrences for each trace </p> <p>- Complete runtime trace info including all events and states (including screenshots) </p> <p>Upon acceptance, we’ll complete the data sharing for all our dataset.</p>
Anonymized and aggregated temporal data on the number of research papers coauthored by Slovenian researchers
<p>The dataset includes: raw aggregated Slovenian researcher network data (available at http:((www.sicris.si), Benford law distribution conformity tests. Scripts for handling the data and Benford conformity tests.</p>
Anonymized source data files for figures in: Recurrent processes support a cascade of hierarchical decisions
<p>Perception depends on a complex interplay between feedforward and recurrent processing. Yet, while the former has been extensively characterized, the computational organization of the latter remains largely unknown. Here, we use magneto-encephalography to localize, track and decode the feedforward and recurrent processes of reading, as elicited by letters and digits whose level of ambiguity was parametrically manipulated. We first confirm that a feedforward response propagates through the ventral and dorsal pathways within the first 200 ms. The subsequent activity is distributed across temporal, parietal and prefrontal cortices, which sequentially generate five levels of representations culminating in action-specific motor signals. Our decoding analyses reveal that both the content and the timing of these brain responses are best explained by a hierarchy of recurrent neural assemblies, which both maintain and broadcast increasingly rich representations. Together, these results show how recurrent processes generate, over extended time periods, a cascade of decisions that ultimately accounts for subjects' perceptual reports and reaction times.</p>
Anonymized Dataset for "Towards a Better Understanding of Reverse-Complement Equivariance for Deep Learning Models in Genomics"
<p>Anonymous dataset for the paper "Towards a Better Understanding of Reverse-Complement Equivariance for Deep Learning Models in Genomics." Includes data for simulated, binary prediction, and profile prediction tasks. </p>
Anonymized Instagram network data from Amsterdam and Copenhagen, Pajek format
<p>Networks of reciprocated recognition (mutual liking and/or commenting) among Instagram users in Amsterdam and Copenhagen, on the basis of data collected over a twelve-week period in 2015. User names are hashed to anonymize the data.</p>
Anonymized Transcripts of Change Laboratory Workshops
<p>Full transcript in Italian of the Change laboratory workshops in a a secondary vocational secondary institute in the Lombardy region.</p> <p>The workshops involved an overall number of 37 participants throughout the meetings between humanity teachers, science teachers, technical teachers and workshop assistants. </p> <p>Transcripts of 7 workshops (in 2016), 2 follow-up and two department councils (in 2016 and 2017). </p> <p>The transcripts have been fully anonymized.</p> <p> </p>
Anonymous Replication Package
<p>Anonymous Replication Package</p>
GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - second part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv and .RData files converted with a 1 second epoch from raw GENEActiv .bin files recorded in Vanuatu during <a title="FALAH website" href="https://falah.unc.nc/" target="_blank" rel="noopener">FALAH</a> project. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 13 to 17 years old students.</p> <p>Number of participants: 72.</p> <p>Year of the study: 2023.</p> <p>Place of the study: Vanuatu.</p> <p>The accelerometer .csv and .RData files with a 1 second epoch and extracted from raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043332" target="_blank" rel="noopener">anonymized version (first part)</a></li> <li><a title="Anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089478" target="_blank" rel="noopener">anonymized version (second part)</a></li> </ul> <p>The accelerometer raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Non-anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">non-anonymized version (first part)</a></li> <li><a title="Non-anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089527">non-anonymized version (second part)</a></li> </ul> <p>Other participant characteristics (age, place of living, ...) and responses to questionnaires are available in <a title="Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)" href="https://doi.org/10.5281/zenodo.14189884" target="_blank" rel="noopener">a restricted non-anonymized dataset</a>.</p>
GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - first part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded in Vanuatu during <a title="FALAH website" href="https://falah.unc.nc/" target="_blank" rel="noopener">FALAH</a> project. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 13 to 17 years old students.</p> <p>Number of participants: 72.</p> <p>Year of the study: 2023.</p> <p>Place of the study: Vanuatu.</p> <p>The accelerometer .csv and .RData files with a 1 second epoch and extracted from raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043332" target="_blank" rel="noopener">anonymized version (first part)</a></li> <li><a title="Anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089478" target="_blank" rel="noopener">anonymized version (second part)</a></li> </ul> <p>The accelerometer raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Non-anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">non-anonymized version (first part)</a></li> <li><a title="Non-anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089527">non-anonymized version (second part)</a></li> </ul> <p>Other participant characteristics (age, place of living, ...) and responses to questionnaires are available in <a title="Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)" href="https://doi.org/10.5281/zenodo.14189884" target="_blank" rel="noopener">a restricted non-anonymized dataset</a>.</p>
GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - second part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded in Vanuatu during <a title="FALAH website" href="https://falah.unc.nc/" target="_blank" rel="noopener">FALAH</a> project. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 13 to 17 years old students.</p> <p>Number of participants: 72.</p> <p>Year of the study: 2023.</p> <p>Place of the study: Vanuatu.</p> <p>The accelerometer .csv and .RData files with a 1 second epoch and extracted from raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043332" target="_blank" rel="noopener">anonymized version (first part)</a></li> <li><a title="Anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089478" target="_blank" rel="noopener">anonymized version (second part)</a></li> </ul> <p>The accelerometer raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Non-anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">non-anonymized version (first part)</a></li> <li><a title="Non-anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089527">non-anonymized version (second part)</a></li> </ul> <p>Other participant characteristics (age, place of living, ...) and responses to questionnaires are available in <a title="Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)" href="https://doi.org/10.5281/zenodo.14189884" target="_blank" rel="noopener">a restricted non-anonymized dataset</a>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.