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2,516 results for “Seri”
Observed and model postseismic time series at GPS sites due to the 2012 Craig and 2013 Haida Gwaii earthquakes
<p><strong>Files with the observed and model displacements, along with predicted model time series</strong>, which derive from the paper of '<em>Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure' </em><strong>JGR: Soild Earth (2021), </strong><a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p> <p><strong>SITE.obs files:</strong> observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes</p> <p><strong>SITE.mod files:</strong> Model postseismic displacements, along with predicted time series. Detailed explanations please see <strong>readme.txt</strong>.</p> <p><strong>GPS site names</strong> are the same with the study of Tian et al. (2021). <a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
Time series of epileptic seizures in a 8yo male on a cannabidiol trial
<p>Time series of epileptic seizures in a 8yo male with idiopathic catastrophic epilepsy (probably of mitochondrial origin) in a cannabidiol (CBD) trial.</p> <p>The data are of <strong>observed seizures </strong>during each day by the patient's parents and caregivers and thus are not exact values for total daily seizure activity; the values in this dataset are best considered a lower bound. However, observation effort was essentially constant over the time series, so the data likely reflect overall trends even though precise daily seizure counts are not available.</p> <p>The time series includes a 21-day baseline period and a 36-day trial period. The CBD was administered via g-tube in an olive oil base. Dosage was approximately 7.5 mg/kg/day for two weeks, and approximately 8 mg/kg/day for three weeks.</p> <p>Each 1ml of oil contained 10.00mg of total CBD and 0.42mg of total THC, for a CBD:THC ratio of approximately 20:1 (analysis by HPLC).</p> <p><strong><em>Data Dictionary</em></strong></p> <ul> <li><strong>Date</strong>: YYYY-MM-DD</li> <li><strong>Tonic</strong>: tonic seizure count (http://www.epilepsy.com/learn/types-seizures/tonic-seizures)</li> <li><strong>Tonic-Clonic</strong>: tonic-clonic (aka "grand mal") seizure count (http://www.epilepsy.com/learn/types-seizures/tonic-clonic-seizures)</li> <li><strong>Spasms</strong>: myoclonic seizure count (http://www.epilepsy.com/learn/types-seizures/myoclonic-seizures)</li> <li><strong>Total</strong>: total number of seizures (sum of above)</li> <li><strong>Notes</strong>: notes by parents</li> </ul>
ODT_10000_series
<p>Supporting information associated with the publication "New insights into single-molecule junctions using a robust, unsupervised approach to data collection and analysis", <em>J. Am. Chem. Soc.</em>, 2015, DOI: 10.1021/jacs.5b05693. This data set was obtained from automated scanning tunnelling microscopy measurements of 1,8-octanedithiol (<strong>1,8-ODT</strong>) on gold. It is provided in both raw (unsorted) and processed (algorithm-sorted) forms.</p>
Classification of Matching Molecular Series on the Basis of SAR Phenotypes and Structural Relationships
<p>A database comprising a total of 13,236 pairs of MMS with different SAR characteristics is provided. For each pair the corresponding MMS-cores are provided as SMILES. In addition, for each MMS-core the number of compounds and the SAR phenotype are given. ChEMBL target IDs (CHEMBLID_Target) designate target sets from which the MMS pairs originate. </p>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo CSV - Presentamos una nueva serie de extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite</p> <p>CSV file - We present a new time series of September Arctic sea ice extent from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </p>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo NetCDF- Presentamos una nueva serie de datos raster con la extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite.</p> <p>NetCDF file - We present a new gridded September Arctic sea ice extent dataset from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </p>
Analog series from ChEMBL, PubChem, and DrugBank
<p>The datasets consist of analog series and key compounds extracted from ChEMBL, PubChem, and DrugBank. For each compound structural and activity information is provided. </p>
Analog series-based scaffolds from ChEMBL with associated activity information
<p>Reported is the activity information for the 12,294 analog series-based (ASB) scaffolds extracted from ChEMBL database. For each ASB scaffold structural and activity information for all analogs comprising the analog series is provoded. </p>
PAINS containing analog series
<p>The file contains analog series that consist exclusively of compounds with PAINS motifs and analog series that contain both PAINS and non-PAINS. For all 54,384 compounds ("cid(PubChem)") in these analog series their corresponding PAINS classes ("PAINS Class") and analog series ID ("ASs ID") are given. Compound IDs ("cid(PubChem)") refer to the original PubChem compound identifiers. All analog series are annotated as 'PAINS' or 'PAINS+non-PAINS' ("ASs Type") respectively to their overall composition. </p>
Hashtag adoption time series
<p>The dataset released here has been used in our paper "#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags." [link to the paper](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6083/6376)</p> <p>In this study, we examine the growth, survival, and context of over 250 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between the hashtags' contextual features and the relative success of hashtags. This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks, which has implications for understanding social influence and collective action in social media more generally.</p> <p>The dataset was the hashtag adoption time sequences during the four debates. They are used to create **Figure 1** in the paper (Cumulative tweet volume of hashtags over time, starting from each debate). </p> <p>Each csv file has three columns:<br> time (in UTC), y (the minute-by-minute cumulative tweet count), and tag (the hashtag name).</p> <p>The onset of the four debates are:<br> Debate 1: 2012-10-04 00:00:00 UTC<br> Debate 2: 2012-10-12 00:00:00 UTC<br> Debate 3: 2012-10-17 00:00:00 UTC<br> Debate 4: 2012-10-23 00:00:00 UTC</p> <p>The raw tweet data have been released via the ICWSM Data Sharing Service. See: <br> [http://www.icwsm.org/2013/datasets/datasets/](http://www.icwsm.org/2013/datasets/datasets/)</p> <p> </p> <p><strong>Publication</strong><br> If you make use of these data sets and code, please cite:</p> <p>Lin, Y.-R., Margolin, D., Keegan, B., Baronchelli, A., Lazer, D. (2013). #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM 2013) </p>
Three-dimensional GNSS Time Series Data for Terrestrial Water Storage Changes Inversion in Yunnan, China
<p>The dataset includes three-dimensional GNSS time series data featured in the publication "Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China", published in 'Water Resources Research'.</p> <p>Reference:<br>Zhu, H., Chen, K., Hu, S., Liu, J.,Shi, H., Wei, G., et al. (2023). Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China. Water Resources Research, 59, e2022WR033126. https:// doi.org/10.1029/2022WR033126</p> <p><br>The sitelist file lists basic information about all the utilized stations, including their names and geographic coordinates. <br>The Time.mat file contains the time vectors of the data employed. <br>The Filter_time_series_N/E/U.mat files showcase the filtered time series, which have been processed using Independent Component Analysis (ICA) for the inversion of terrestrial water storage in Yunnan, after removing the effects of outliers, steps, and non-tidal atmospheric/oceanic loading.</p>
CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany
<h2>Description</h2> <p>CAMELS-DE provides a comprehensive collection of hydro-meteorological timeseries data (e.g. discharge, water level, precipitation, air temperature) and catchment attributes for 1582 streamflow gauges across Germany. The time series data is in daily resolution and spans up to 70 years, from January 1951 to December 2020. The static catchment attributes include information on topography, soils, land cover, hydrogeology and human influences. Additionally, the dataset includes discharge simulations from a regional Long-Short Term Memory (LSTM) network and a conceptual hydrological model (HBV), providing benchmark data for future hydrological modelling studies in Germany.</p> <p>The accompanying data description gives information on data sources, the structure of the data set and contains extensive information on time series and catchment attribute variables. In addition, up-to-date benchmark results of the LSTM and HBV are provided.</p> <blockquote> <p><strong><strong>Important: As CAMELS-DE is continuously developed and updated, please ensure that you cite the correct version of the dataset that you are using.<br><br></strong></strong><strong>The CAMELS-DE data description paper is available here: <a href="https://doi.org/10.5194/essd-16-5625-2024">https://doi.org/10.5194/essd-16-5625-2024</a>.</strong></p> </blockquote> <p>Information about the code and methods for generating CAMELS-DE can be found here: <a title="CAMELS-DE Processing Pipeline" href="https://doi.org/10.5281/zenodo.12760336" target="_blank" rel="noopener">CAMELS-DE Processing Pipeline</a>.</p> <p>CAMELS-DE is also part of the Caravan project, a global hydrological dataset. Due to the use of data products that are available beyond the Germany national boundaries, Caravan-DE includes 305 additional streamflow gauges, resulting in a total of 1887 streamflow gauges: <a href="https://doi.org/10.5281/zenodo.13320514">https://doi.org/10.5281/zenodo.13320514</a>.</p> <h3>Disclaimer for discharge and water level data provided by the German federal state agencies:</h3> <p>english:<em><br>The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded.</em></p> <p>german:<em><br>Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind.</em></p> <h3>Changelog</h3> <ul> <li><strong>v1.1.0</strong> <ul> <li>LSTM benchmark results are now based on a <strong>LSTM with 10 ensemble members</strong>, changing the median NSE in the testing period from 0.83 to 0.85 <div> <ul> <li>The columns <em>discharge_spec_sim_lstm</em> and <em>discharge_vol_sim_lstm</em> in <em>timeseries_simulated</em> are now based on the median values of the 10 ensemble members.</li> <li>The column <em>NSE_lstm</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> is now calculated from the median simulations of the 10 ensemble members</li> <li><em>model_parameters/LSTM/CAMELS_DE_epochs_training_lstm.zip</em> now contains the epochs of the 10 ensemble members</li> </ul> </div> </li> <li>The columns <em>NSE_lstm</em>, <em>NSE_hbv</em> and <em>training_perc_complete</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> were calculated from 2001 - 2020, now corrected to 2000 - 2020</li> <li>Fixed a bug in the calculation of <em>high_prec_dur</em> and <em>low_prec_dur</em> in <em>CAMELS_DE_climatic_attributes.csv</em> calculation (thank you to Bastian Klein from BfG for reporting this issue)</li> <li>Bayern: removed blank space after gauge and water body name and removed water body name from some gauge names, where it was included as "[gauge_name]_[water_body_name]", e.g. "Würzburg_Main" -> "Würzburg", water body name is now only included in the `water_body_name` column in<code> </code><em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Nordrhein-Westfalen: corrected some wrong river names in <em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Sachsen: added `gauge_elevation_metadata` information to <em>CAMELS_DE_topographic_attributes.csv</em></li> </ul> </li> </ul> <ul> <li><strong>v1.0.0</strong> <ul> <li>CAMELS-DE v1.0.0 is the version of the dataset that is described by the <a href="https://doi.org/10.5194/essd-2024-318">CAMELS-DE data description paper</a>.</li> <li>Addition of the federal state of Saarland, resulting in 27 additional catchments and coverage of all federal states except the city states of Berlin, Bremen and Hamburg. This also leads to a change in the title of the dataset from 1555 catchments to 1582 catchments.</li> <li>Addition of HBV model parameters and LSTM model training period epochs.</li> <li>Catchment DE911970: Removal of erroneous zero discharge values at the beginning of the measurement period.</li> <li>Minor fixes such as the elimination of discrepancies between the variable names in the dataset and in the data description.</li> <li>We were able to identify and fix some of these problems based on the feedback from the community, thank you very much!</li> </ul> </li> </ul>
Europe-wide public preferences for plankton-based ecosystem services and marine biodiversity from a series of Deliberative Monetary Valuation workshops
<p>The data were collected as part of the Horizon Europe project BIOcean5D – Marine Biodiversity Assessment and Prediction Across Spatial, Temporal and Human Scales. The aim of the study was to elicit public preferences for marine biodiversity and ecosystem services, with a particular focus on plankton. This dataset comprises responses from a series of Deliberative Monetary Valuation workshops held across Europe, along with the corresponding supplementary material. The responses encompass a range of data, including socio-demographic information, personal characteristics, beliefs, prior knowledge, preferences derived from a Discrete Choice Experiment, and the associated motivations of each respondent. In total, 15 workshops were conducted between October 2023 and February 2024 in the following locations: Poland (Poznan and Sopot), Italy (Padova and Chioggia), the Basque Country (Vitoria-Gasteiz and Bilbao), Germany (Bremerhaven and Hannover), and France (Rennes and Brest).</p>
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
<p>We present a <strong>multi-temporal</strong>, <strong>multi-modal</strong> remote-sensing dataset for predicting <strong>how active wildfires will spread</strong> at a resolution of 24 hours. The dataset consists of <strong>13.607 images</strong> across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a <strong>full time series of daily observations</strong>, containing detected active fires and variables related to <strong>fuel, topography and weather conditions</strong>.</p><h2>Documentation</h2><p><i><strong>WildfireSpreadTS_Documentation.pdf</strong></i> includes further details about the dataset, following Gebru et al.'s <strong>"Datasheets for Datasets"</strong> framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. </p><h2>Code: Getting started</h2><p>Get started working with the dataset at <a href="https://github.com/SebastianGer/WildfireSpreadTS">https://github.com/SebastianGer/WildfireSpreadTS</a>. </p><p>The code includes a <strong>PyTorch Dataset</strong> and <strong>Lightning DataModule </strong>to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository.</p><p> </p><p>This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.</p>
Soil study results at Vallon de Nant : Soil moisture and soil temperature time series and granulometry results
<p>Dataset:</p><ul><li><a href="https://zenodo.org/api/records/10136586/draft/files/Particule_size_distribution.csv/content">Particule_size_distribution.csv : </a><br>Particle size distribution obtained for 34 samples in Vallon de Nant. Please refer to the pdf report for technical information.<br>Columns description : <br>point,depth= identification of the point. Please refer to the pdf notice<br>size (in micrometers) : Particle size ranging from 0.003mum to 2 mm<br>value (in %) : Fraction of the material volume corresponding to the size<br>USDAclass : Soil class according to the USDA classification for each sample<br> </li><li><a href="https://zenodo.org/api/records/10136586/draft/files/measure_T_HU_5TM_3stations.csv/content">measure_T_HU_5TM_3stations.csv :</a><br>Hourly soil moisture and soil temperature measurements at 3 points and different depths in the catchment. Please refer to the pdf report for technical information.<br>Columns description : <br>Time,Hour : recording time stamp<br>portX_HU : Soil moisture recorded at the X slot. Please refer to the notice for sensor depth.<br>portX_T : Soil temperature recorded at the X slot.<br>Station : Name of the measurement point (Auberge, Chalet or LaChaux). Auberge and LaChaux are at the exact same location than the corresponding weather stations. Chalet point is on the left bank on the river, near little bridge. Please refer to the pdf notice.</li></ul>
PV-gradient (PVG) tropopause: Time series 1980--2017 in four reanalyses
<h1>PV-gradient tropopause time series</h1> <h2>General description</h2> <p>These datasets contain time series of the PV-gradient tropopause (PVG tropopause) introduced by A. Kunz (2011, <a href="https://doi.org/10.1029/2010JD014343">doi:10.1029/2010JD014343</a>) and calculated by K. Turhal (2024, paper " Variability and Trends in the PVG Tropopause", preprint in EGUsphere: https://doi.org/10.5194/egusphere-2024-471).</p> <h2>Data and methods</h2> <p>The PVG tropopause has been computed by means of the Eddy Tracking Toolkit (developed by J. Clemens and K. Turhal, to be published):</p> <ul> <li>from four reanalyses: ERA5, ERA-Interim, MERRA-2 and JRA-55</li> <li>for the time range 1980/01/01 -- 2017/12/31 in time steps of the according reanalyses, i.e. four times daily at 00h, 06h, 12h and 18h</li> <li>on each isentropic level, with potential temperatures (theta) ranging from 320 K to 380 K, in steps of 5 K for ERA5 and 10 K for the other reanalyses.</li> </ul> <h2>Contents</h2> <p>Datasets are provided for each year and isentropic level in NetCDF4 format, every file consisting of two groups for the northern and southern hemisphere. Each group contains the following variables, with time as dimension:</p> <ul> <li>time in seconds since 2000/01/01 00:00 UTC</li> <li>u_lim: Zonal wind speed at the PVG tropopause</li> <li>vh_lim: Horizontal wind speed at the PVG tropopause</li> <li>q_lim: Maximum of Q = vh * Grad PV</li> <li>eqlat_lim: Location of the PVG tropopause in equivalent latitudes</li> <li>latmean_lim: Location of the PVG tropopause in latitudes</li> <li>pv_lim: PV value at the PVG tropopause</li> </ul> <p>In this upload, the PVG tropopause time series are included as *.zip files:</p> <ul> <li>ERA5 dataset: "pvg-tp_era5_ts.zip"</li> <li>ERA-Interim dataset: "pvg-tp_eraint_ts.zip"</li> <li>MERRA-2 dataset: "pvg-tp_merra2_ts.zip"</li> <li>JRA-55 dataset: "pvg-tp_jra55_ts.zip"</li> <li>Plots of time series for each reanalysis of the variables eqlat_lim, latmean_lim and pv_lim: "pvg_tropopause_timeseries_plots.zip".</li> </ul> <h2>How to use</h2> <p>The variables in these netCDF files are grouped by hemisphere. To read in the data, specify the group first ("NorthernHemisphere" or "SouthernHemisphere") and then the variable name (see list above). In Python, this can be done as follows:</p> <pre><code>import netCDF4 as nc file="<insert file path here>" d = nc.Dataset(file) # read in a variable. Syntax: d["group name"]["variable name"][:]. For example: latmean_lim = d["NorthernHemisphere"]["latmean_lim"][:] # test print print(f"First value of latmean_lim in NH: {latmean_lim[0]}")</code></pre> <p>If you would like to read in all variables in both hemispheres, you can loop e.g. as follows:</p> <pre><code>import netCDF4 as nc file = "<insert file path here>" d = nc.Dataset(file) # iterate through both hemispheres for hem in ["NorthernHemisphere", "SouthernHemisphere"]: # select the group to each hemisphere in the netCDF file g = d.groups[hem] # iterate through variables in each hemisphere. "v" is the name of each variable in the group. for v in g.variables: # read in the data for variable 'v' in hemisphere 'hem' as an array var = g[v][:] # just a test print, optional print(f"First value of {v} in {hem.replace('Hem', ' Hem')} is {var[0]}")</code></pre> <h2>Funding</h2> <p>This project has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742, TPChange: The Tropopause Region in a Changing Atmosphere (<a href="https://tpchange.de/">https://tpchange.de/</a>).</p>
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 1: Introduction and Methodology
<p>This document is Part 1 of the EnergyPROSPECTS Factsheet Series summarising the methodology used for collecting and presenting the data as well as introducing the database.</p> <p>We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</p>
PhasAGE Ask The Expert Series | Salvador Ventura
<p>A collection of captivating interviews with senior researchers from the PhasAGE consortium. Meet each core member while they foster new scientific horizons and cultivate collaborations, through clear, engaging dialogue that connects science with society. This initiative was organized by the early-stage researchers together with the Research and Communication Manager.</p> <p><span>This interview is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952334 – PhasAGE.</span></p>
PhasAGE Ask The Expert Series | Peter Tompa
<p>A collection of captivating interviews with senior researchers from the PhasAGE consortium. Meet each core member while they foster new scientific horizons and cultivate collaborations, through clear, engaging dialogue that connects science with society. This initiative was organized by the early-stage researchers together with the Research and Communication Manager.</p> <p>This interview is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952334 – PhasAGE. </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.