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Dataset results
773 results for “data science”
Aurorasaurus Real-Time Citizen Science Aurora Data
<p>Aurorasaurus citizen science data is a collection of auroral sightings submitted to the project via its website (aurorasaurus.org) or apps and mined from social media. It is a robust data set and particularly abundant during strong geomagnetic storms. This data is offered to the scientific community for research use through an open-access database in its raw and scientific formats for the 2015-2016 period, each of which is described in detail in the following technical report:</p> <p>Kosar, B. C., MacDonald, E. A., Case, N. A., & Heavner, M. (2018). Aurorasaurus Database of Real‐Time, Crowd‐Sourced Aurora Data for Space Weather Research. <em>Earth and Space Science</em>, <em>5</em>(12), 970-980.</p> <p>For more information on the project, please contact the project leaders at aurorasaurus.info@gmail.com.</p> <p> </p>
Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science
<p>Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science</p> <p> </p> <ul> <li>info.txt contains a general description of how the data was processed and the main results.</li> <li>pipeline.tar contains the data pipeline used to process the e-MERLIN observations</li> <li>imaging.py is the script used to produce the final images</li> <li>CY6213_images.tar contain the final images of the target source (not corrected by calibration factor, described in the imaging script).</li> </ul> <p> </p>
Data Potential Bias in Peer Review of Grant Applications at the Swiss National Science Foundation
<p>Potential biases in the peer review of grant applications at the Swiss National Science Foundation.</p> <p> </p>
Supporting Material for article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences"
<p>This data set is the Supporting Material referred to in the Supplementary Data for the article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences" (Drysdale, et al.) submitted for publication in April 2019.</p> <p> </p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Participant survey data from the citizen science project FLOW, 2021
<p>This dataset is linked to the following publication:</p> <p>von Gönner, J., Masson, T., Köhler, S., Fritsche, I., Bonn, A. (in press): Citizen science promotes knowledge, skills and collective action to monitor and protect freshwater streams. People and Nature.</p> <p> </p>
Experimental data for nonlinear dynamics and energy harvesting of bistable cantilever shells (grant 2021/41/B/ST8/03190 from the National Science Centre, Poland)
<p>An experimental tests on a cantilever composite bistable shell with a piezoelectric patch is reported. The shell is characterized by two stable configurations. Periodic force is applied at the shell’s clamped side through an electrodynamic shaker and the dynamic response is measured through an embedded strain gauge. Selected dynamic regimes are recorded by performing excitation frequency and amplitude sweeps. The resonance scenarios around the two natural frequencies corresponding to the stable configurations show different softening behavior. The excitation amplitude threshold level for snap-through motion is identified. The thested thin-walled pseudo-conical shell has been made of carbon-epoxy composite prepreg tape (AS4-GP-12K_40gsm-<br>300mm-ThinPreg135EP). The composite single layer is characterized by unidirectional reinforcement, thickness 0.04 mm. The manufacturing standard samples for strength tests in the autoclave process reduces to about 0.038 mm. </p>
Data accessibility in the chemical sciences: an analysis of recent practice in organic chemistry journals
<div> <p>Data is the analysis of the data outputs of 240 randomly selected research papers from 12 top-ranked journals published in early 2023. We investigate author compliance with recommended (but not compulsory) data policies, whether there is evidence to suggest that authors apply FAIR data guidance in their data publishing, and if the existence of specific recommendations for publishing NMR data by some journals encourages compliance. Files in the data package have been provided in both human and machine-readable forms. The main dataset is available in the Excel file Data worksheet.XLSX, the contents of which can also be found in Main_dataset.CSV, Data_types.CSV, and Article_selection.CSV with explanations of the variable coding used in the studies in Variable_names.CSV, Codes.CSV, and FAIR_variable_coding.CSV. The R code used for the article selection can be found in Article_selection.R. Data about article types from the journals that contain original research data is in Article_types.CSV. Data collected for analysis in our sister paper[4] can be found in Extended_Adherence.CSV, Extended_Crystallography.CSV, Extended_DAS.CSV, Extended_File_Types.CSV, and Extended_Submission_Process.CSV. A full list of files in the data package and a short description for each is given in README.TXT.</p> </div>
Visualization and perception of data gaps in the context of Citizen Science projects: Gradation of Reporting Activity
<p>Online experiment about the influence of different numbers of levels of representation of reporting activity (total number of reports for all birds in the given time span and region) on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Effects of representation with three (3) levels and effects of representation with five (5) levels are investigated. Two groups of members of ornitho.de were tested: experts - persons with access to database (more than 10 reports per month in average) and novices - persons without access to database (less than 10 reports per month in average). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Visualization and perception of data gaps in the context of Citizen Science projects: Video tutorial support
<p>Online experiment about the influence of the availability of a video tutorial on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Open Science Team 7 Data Set and Bibliography
<p>As a small exercise before delving into a group research project we generated a small data set based on the review of 7 websites ranging in subject matter. We provide the Data set and bibliography here. </p>
Data and figures for "A half-century of global collaboration in science and the 'Shrinking World'"
<p>This package supplements the paper entitled <i>"A half-century of global collaboration in science and the 'Shrinking World'"</i> published in <i>Quantitative Science Studies</i> (doi: <a href="https://doi.org/10.1162/qss_a_00268">10.1162/qss_a_00268</a>).</p><p>It contains the datasets and figures used in the original paper based on bibliometric data from a broad set of scientific publications (works), including journal articles, preprints and datasets; see the subfolder named "all_works".</p><p>In addition, for reference, it also contains datasets and figures based on bibliometric data from journal articles only; see the subfolder named "journal_only". The bottom-level files in this subfolder are suffixed with '_j' for identification.</p><p> </p><p><strong>Contents and Instructions</strong></p><p>The datasets and figures in this package are based on the data obtained via <a href="https://docs.openalex.org/api/">OpenAlex API</a>. See the original paper for details. The following file and folders are found at the next level of the subfolders named "all_works" or "journal_only".</p><p> </p><p><strong>- nworks_intlrate_master</strong> (.csv file)</p><ul><li>This file contains information on the number of works ('nworks_all') produced in each of the 15 research disciplines ('discipline' and 'disc_ID'; see below) by 18 countries (Australia, Canada, China, France, Germany, India, Indonesia, Iran, Italy, Japan, Netherlands, Poland, Russia, South Korea, Spain, Switzerland, UK and US) ('country' and 'country_code') from 1970 to 2021 ('year'), the number of international collaborative works among them ('nworks_intl'), and the international collaboration rate ('intlrate') calculated from the ratio of the two.</li><li>The 15 disciplines are Artificial Intelligence ('disc_ID' = 1; 'ai'), Quantum Science (2; 'quantum'), Biotechnology (3; 'bio'), Nanotechnology (4; 'nano'), Agricultural Engineering (5; 'agri'), Particle Physics (6; 'particle'), Aerospace Engineering (7; 'aerospace'), Nuclear Engineering (8; 'nuclear'), Marine Engineering (9; 'marine'), Neuroscience (10; 'neuro'), Condensed Matter Physics (11; 'condensed'), Environmental Engineering (12; 'envi'), Earth Science (13; 'earth'), Astronomy (14; 'astro') and Pure Mathematics (15; 'math'). See the original paper for the definitions of these disciplines.</li><li>The figures contained in the folders '[line]_nworks' and '[line]_intlrate' are based on this dataset.</li></ul><p> </p><p><strong> - [line]_nworks</strong> (Folder)</p><ul><li>This folder contains line plots (.pdf/.png) representing the trends in the number of works by discipline and country, corresponding to the left-hand side diagrams of Fig. 1 and Suppl. Fig. S2 in the v1 preprint.</li></ul><p> </p><p><strong>- [line]_intlrate</strong> (Folder)</p><ul><li>This folder contains line plots (.pdf/.png) representing the trends in the international collaboration rate by discipline and country, corresponding to the right-hand side diagrams of Fig. 1 and Suppl. Fig. S2 in the v1 preprint.</li></ul><p> </p><p><strong>- [chord]_bilateral</strong> (Folder)</p><ul><li>This folder contains chord diagrams (.pdf/.png) representing the bilateral collaborative relationships by discipline and period, corresponding to Fig. 2 and Suppl. Fig. S4 in the v1 preprint. The number at the end of the file name indicates the period represented by the diagram; specifically, '1' = 1971–1990, '2' = 1991–2000, '3' = 2001–2010 and '4' = 2011–2020.</li><li>The raw data (.xlsx) to reproduce the contained diagrams are also provided by discipline in the accompanied 'Data' folder. The file named '[list]_nworks_(discipline name).xlsx' shows, for the top 30 countries ('country' and 'country_code') in work production during the period indicated by the sheet name, their work production ('nworks_all'), the number of international collaborative works among them ('nworks_intl'), and the international collaboration rate ('intlrate') calculated from the ratio of the two. The file named '[mat]_bilat_nworks_(discipline name)' shows the number of works produced by each country pair during the period indicated by the sheet name. Country names are abbreviated by two-letter country codes (ISO 3166-1 alpha-2).</li></ul><p> </p><p><strong>- [dend]_hcluster</strong> (Folder)</p><ul><li>This folder contains circularised dendrograms (.pdf/.png) representing the international research collaboration clusters by discipline and period, corresponding to Fig. 3 and Suppl. Fig. S5 in the v1 preprint.</li><li>The raw data (.xlsx) to reproduce the contained diagrams are also provided by discipline in the accompanied 'Data' folder. The file named '[mat]_bilat_dist_(discipline name)' shows the distance between each country pair for the period indicated by the sheet name, calculated based on the formula presented in the original paper. Country names are abbreviated by two-letter country codes (ISO 3166-1 alpha-2).</li></ul>
Image Data sets for use in Heritage Science
<p>The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. </p> <p><a href="https://doi.org/10.5281/zenodo.7292917">Photographs</a> </p> <p><a href="https://doi.org/10.5281/zenodo.7292961">X-Ray Fluorescence</a> </p> <p><a href="https://doi.org/10.5281/zenodo.7322908">Hyperspectral Imaging</a> </p> <p><a href="https://doi.org/10.5281/zenodo.7292714">Multispectral Imaging</a></p> <p> </p> <p>This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation.</p> <p>We have collected this as an example of typical, unprocessed imaging datasets that would be found in standard image conditions. This data is not optimized, nor do we claim it to be perfect quality, our aim is to provide users with access to a range of imaging data sets. We have included the data with minimum processing, as it is read straight from our systems, with the accompanying metadata provided from capture alone.</p> <p>We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. </p> <p>If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. </p> <p>E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a> </p> <p>Object Paradata; </p> <ul> <li><strong>Postcard – c. Early 1900's </strong></li> <li><strong>Language – Eng. </strong></li> <li><strong>Materials – colour print on card, metallic leafing. </strong></li> <li><strong>Front transcription - </strong></li> <li><strong> ‘Greetings’ </strong></li> <li><strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong></li> <li><strong>Object Dimensions – 138mm X 88mm </strong></li> </ul> <p>The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015.</p>
Data for: Luo et al., Expiratory aerosol pH: the overlooked driver of airborne virus inactivation, Environmental Science and Technology, 10.1021/acs.est.2c05777
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figures 1 and S3 (inactivation curves for IAV, SARS-CoV-2 and HCoV-229E)</p> <p>Figure 1 (rate constants)</p> <p>Figure 2 (EDB analysis of SLF)</p> <p>Figure S1A (zetasizer analysis to measure virus aggregation)</p> <p>Figure S1B (renilla and plaque assay data for viruses exposed to pH 5, 6 and 7)</p> <p>Figure S4A (EDB analysis of different SLF samples; raw data)</p> <p>Figure S4Amean (EDB analysis of different SLF samples; mean values)</p> <p>Figure S5 (EDB analysis of nasal mucus)</p> <p>Figure S8 (EDB analysis of slow crystal growth stage of SLF and nasal mucus)</p> <p>Figure S13 and S14 (literature data on inactivation of IAV and SARS-CoV-2 in aerosol particles)</p> <p> </p> <p> </p>
CS Track Citizen Science Survey Data 2021
<p>CS Track is launching a survey to gather citizen scientists’ (16 year old and older) perspectives on activities and forms of participation, learning and knowledge-building in citizen science (CS) projects. The aim of CS Track is to broaden our knowledge about CS and the impact CS activities can have. CS Track will do this by investigating a large and diverse set of CS activities, disseminating best practices and formulating knowledge-based policy recommendations in order to maximise the potential benefits of CS activities for individual citizens, organisations and society. This multi-perspective approach will allow us to shed light on the role of citizen science in society and social attitudes and emerging cultures in communities that engage with science and technology challenges.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.csv</strong>: CSV File. CS Track Citizen Science Survey Data in a CSV file.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.xlsx</strong>: Excel datasheet. Same file as previous in an Excel datasheet.</p> <p><strong>CSTrack_Citizen_Science_Survey_Final.pdf</strong>: PDF-file. CS Track Citizen Science Survey.</p> <p> </p> <p> </p>
Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)
<p>This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391.</p> <p>The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.</p> <p> </p> <p> </p>
MuSpinSim data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be compared to the output of simulations by MuSpinSim for dissipation of muon spins.</p> <p>The files included are:</p> <ul> <li><strong>dissipation_theory.dat:</strong> theoretical values formatted as a MuSpinSim output</li> <li><strong>experiment.dat:</strong> mock experimental values formatted as a MuSpinSim output</li> </ul>
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
Data from: Trends in butterfly populations in UK gardens – new evidence from citizen science monitoring
<p>This data package describes the annual abundance indices and trend estimates for 22 butterfly species in UK gardens for the period 2007-2020.</p> <p>These data form the basis of the results presented in: Plummer, K.E., Dadam, D., Brereton, T., Dennis, E.B., Massimino, D., Risely, K. et al. (2023) Trends in butterfly populations in UK gardens—New evidence from citizen science monitoring. <em>Insect Conservation and Diversity</em>, 1– 13. Available from: <a href="https://doi.org/10.1111/icad.12645">https://doi.org/10.1111/icad.12645</a></p> <p>Please refer to the paper for an explanation of the underlying BTO Garden BirdWatch (GBW) data and modelling protocols used to produce the datasets included here.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Data management in the social sciences in Macedonia [Webinar recording]
<p>This webinar aimed to introduce social science researchers to the basic principles of data management, including the creation of a Data management plan, which is an important tool for planning the research project.</p> <p>The webinar consisted of three parts. The first part introduced researchers with the basic principles of data management including the benefits of adopting Data management plans (DMPs). The DMP follows the research projects’ life cycle, starting with the initial phases of Planning and Organization and documentation of research data. This part also included a presentation of best practices for creation of appropriate structure of folders and data files, as well as instructions for their naming, documentation and organization.</p> <p>The second part of the webinar focused on the following three phases of the project life cycle: Data processing, Preservation and Protection. Contemporary social science presumes the respect of high level ethical standards during the handling of research data, in accordance with legal rules and best practices in this area.</p> <p>The last part of the webinar was dedicated to the phases of Publication - familiarizing the researchers with the possibilities of data preservation and publishing; and Data discovery - discussing the ways and means to acquire social science data, including the secondary use of data produced by other researchers.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=n05WTs58CMY"> the CESSDA Training YouTube channel</a>.</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.