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Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </p>
Data set of 1) plant abundance in the herb layer and 2) Shrub and tree composition and structure following forest management along a chronosequence
<p>In each site (1200 m²), three circular plots (400 m²) were established (total of 198 plots). Data presented here for 1) plant of the herb layer and 2) shrub and tree are grouped by site (addition of three plots). </p> <p>In the herb layer, total plant species identity and abundance (percentage cover) were sampled in each plot using eight circular micro-plots of 4 m². Data were collected in 2016 and 2017, between June and August. To minimize seasonal variability and allow detection of early spring species, plants (identity and abundance) in the herb layer were measured twice (once in June to early-July, and once in late-July to August).</p> <p>For tree, in each plot, species identity, diameter at breast height (DBH, 1.3 m) and locations of each tree > 9.1 cm DBH were determined. In each plot, species identity and DBH of shrubs and small trees (DBH range: 1.1 to 9.1 cm) were measured in three circular micro-plots of 25 m². Data that were related to the shrub-canopy layer included all trees and shrubs with DBH > 1.1 cm. Here, in each site, forest composition and structure is represented by different combinations of DBH classes (1.1-4 cm, 4-9.1 cm, 9.1-20 cm, 20-35 cm, >35 cm) and species.</p> <p>Plant community composition and abundance were assessed in unmanaged forests (sites of old-growth forest > 100 years, with dominant and co-dominant trees older than 200 years, and no obvious sign of past harvesting), and in even-aged and uneven-aged managed forests along a chronosequence (< 5 years, 15 years, 30 years after forest harvesting).</p>
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules
<div> </div> <h3>Data</h3> <p> This folder contains the raw data used during this work. `out_seq_total.txt` contains information on the sequences used (mutations, number of mutations, etc.). `output_energies_total.txt` contains the response variables, which include: a) unrelaxed EvoEF energies (peptides); b) relaxed EvoEF energies (peptides, `*_repaired.txt`); and c) solvation energies (molecules). 3D structures are provided in `.xyz` format in the subfolder `XYZ` (molecules).</p> <div> <div><strong>GB1 dataset</strong></div> <br> <div>The GB1 dataset was <strong>not</strong> generated by us (https://doi.org/10.48550/arXiv.2405.05167). If you use the GB1 dataset, please cite the original paper:</div> <br> <div>Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., & Sun, R. (2016). <em>Adaptation in protein fitness landscapes is facilitated by indirect paths. </em><strong>eLife</strong>, 5:e16965. doi:10.7554/eLife.16965</div> </div> <h3>Results</h3> <p>This folder contains the results (outputs) of the ML models trained using the provided scripts (see github repository). Such results are incuded in the form of `.npy` files. To load the files please include the option `allow_pickle=True`.</p> <p> Each `.npy` file contains the following keys:<br> * `initial_parameters`: script inputs.<br> * `d_encoder`: encoder used (not always included).<br> * `ns_train`: number of training points used for the LCs (rounded, integers).<br> * `ns_train_float`: number of training points used for the LCs (not rounded, float).<br> * `ns_train_norm`: number of training points used for the LCs (normalized, float).<br> * `res`: test MAEs.<br> * `res_tot`: (train,validation,test) MAEs.<br> * `res_tot_mut`: (train,validation,test) MAEs sorted by mutation number.<br> * `l_opt`: optimal kernel length used during the test.<br> * `ls`: kernel lengths used for grid search.<br> * `idx_seeds`: indices used to reshuffle the data. If one want to rebild the initial order use `np.argsort(idx_seeds)`.<br> * `alpha_opt`: optimal regression parameters used to calculate the test error. To sort the data use `alpha_opt[i][ii][np.argsort(idx_seeds[ii,0:arg_train_max].astype(int)[:ns_train[i]]`. Where `i` is the replicate number (0-99) and ii is the idex in the LC.<br> * `valid_errs`: validation error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.<br> * `test_errs`: test error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.</p>
HySN Data set
<p>HySN, a high resolution HYbrid SeNorge data set of daily near surface humidity and incident shortwave and longwave radiation, and surface pressure created by merging reanalysis data (Era-interim) with a national 1-by-1 km gridded temperature data set, SeNorgev2 (SeNorge v2.1 between 1979 and 2015, and SeNorge v2.0 from 2016). The assumptions and methods used to downscale humidity and longwave radiation are similar to those used in WATCH/PGMFD/NLDAS-1. The software used to compile the data is available at <a href="https://doi.org/10.5281/zenodo.1435555">GitHub</a>. The data is described, and compared to surface observations and other data sets here: <a href="https://doi.org/10.5194/essd-11-797-2019">Erlandsen, H. B., Tallaksen, L. M., and Kristiansen, J.: Merits of novel high-resolution estimates and existing long-term estimates of humidity and incident radiation in a complex domain, Earth Syst. Sci. Data, 11, 797-821, https://doi.org/10.5194/essd-11-797-2019, 2019</a>, and in its supplement: <a href="https://doi.org/10.5194/essd-11-797-2019-supplement">Supplement of Earth Syst. Sci. Data, 11, 797–821, 2019 https://doi.org/10.5194/essd-11-797-2019-supplement</a> .</p> <p>The data has the same projection as SeNorge, UTM33, and is stored for precipitation days, i.e. the datestamp is day-month-year 06 UTC and the variable represents average value of the preceding 24 hours. Monthly means of the data can be found here: <a href="https://doi.org/10.5281/zenodo.1993870">https://doi.org/10.5281/zenodo.1993870</a> .</p> <p>Note that HySN5, a high resolution HYbrid SeNorge data set of daily near surface humidity and surface incident shortwave and longwave radiation, and surface pressure created by merging reanalysis data (Era5) with a national 1-by-1 km gridded temperature data set, SeNorge2018, is available from 1979-2000 (<a href="http://10.5281/zenodo.3351430">10.5281/zenodo.3351430</a>) and 2001-2017 (<a href="http://10.5281/zenodo.3516560">10.5281/zenodo.3516560</a>). </p>
Monthly means of the HySN Data set
<p>Annual monthly means, and monthly means of the full time-series of HySN, a high resolution HYbrid SeNorge data set of daily near surface humidity and incident shortwave and longwave radiation, and surface pressure created by merging reanalysis data (Era-interim) with a national 1-by-1 km gridded temperature data set, SeNorge v2 (SeNorge v2.1 between 1979 and 2015, and SeNorge v2.0 from 2016). The assumptions and methods used to downscale humidity and longwave radiation are similar to those used in WATCH/PGMFD/NLDAS-1. The software used to compile the data is available at <a href="https://doi.org/10.5281/zenodo.1435555">GitHub</a>. The daily data is at <a href="https://doi.org/10.5281/zenodo.1970170">doi.org/10.5281/zenodo.1970170</a>.</p> <p>The data has the same projection as SeNorge, UTM33, and is stored for precipitation days, i.e. the datestamp is day-month-year 06 UTC and the variable represents average value of the preceding 24 hours.</p>
Turbulence in Natural Mangrove Pneumatophore Canopies - Data Set
<p>Raw and processed data files (format: zipped .mat files) for plotting Figures 3 - 15 in our paper entitled "Turbulence in Natural Mangrove Pneumatophore Canopies" published in the Journal of Geophysical Research: Oceans in 2018. </p>
Use of Augmented Reality in the preservation of architectural heritage: case of aqueduct Kuru Kopru _Data set
<p>Architectural preservation embeds all the activities dealing with the physical sustainability of the built heritage<strong>,</strong> its diffusion and comprehension by a wide scope public. Representation and diffusion of heritage take a core place in that process. Nowadays, Augmented Reality (AR) is one of the most used digital tools in the diffusion of architectural heritage. This dataset embeds data used for the modeling of reconstruction models of the Roman aqueduct Kuru Kopru from the Roman-Byzantıne period to the year 2017 and those used for the development of two AR applications for the diffusion of the aforementioned aqueduct.</p>
1-hour meteorological data set
<p>1-hour meteorological data set</p>
Improving soil aquifer treatment efficiency using air injection into the subsurface - Data set
<p>Data set for the research article "Improving soil aquifer treatment efficiency using air injection into the subsurface"</p>
Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3. Blaimschein et al.: Data sets
<p>Data sets used in the manuscript 'Substrate-Binding Guides Individual Melibiose Permeases MelB to Structurally Soften and to Destabilize Cytoplasmic Middle-Loop C3' by Blaimschein et al.</p> <p>The analysis scripts can be found at https://doi.org/10.5281/zenodo.7268838</p>
Large Landing Trajectory Data Set for Go-Around Analysis
<p>Large go-around, also referred to as missed approach, data set. The data set is in support of <a href="https://www.mdpi.com/2673-4591/28/1/2">the paper</a> presented at the OpenSky Symposium on November the 10th.</p> <p>If you use this data for a scientific publication, please consider citing <a href="https://doi.org/10.3390/engproc2022028002">our paper</a>.</p> <p>The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from <a href="https://opensky-network.org/data/impala">OpenSky Network's historical data base</a> for the year 2019. The published data set contains multiple files:</p> <p><strong>go_arounds_minimal.csv.gz</strong></p> <p>Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> </tbody> </table> <p>The last two columns, <em>n_approaches</em> and <em>n_rwy_approached</em>, are useful to filter out training and calibration flight. These have usually a large number of <em>n_approaches</em>, so an easy way to exclude them is to filter by <em>n_approaches > 2.</em></p> <p><strong>go_arounds_augmented.csv.gz</strong></p> <p>Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>time</td> <td>date time</td> <td>UTC time of landing or first GA attempt</td> </tr> <tr> <td>icao24</td> <td>string</td> <td>Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned</td> </tr> <tr> <td>callsign</td> <td>string</td> <td>Aircraft identifier in air-ground communications</td> </tr> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>has_ga</td> <td>string</td> <td>"True" if at least one GA was performed, otherwise "False"</td> </tr> <tr> <td>n_approaches</td> <td>integer</td> <td>Number of approaches identified for this flight</td> </tr> <tr> <td>n_rwy_approached</td> <td>integer</td> <td>Number of unique runways approached by this flight</td> </tr> <tr> <td>registration</td> <td>string</td> <td>Aircraft registration</td> </tr> <tr> <td>typecode</td> <td>string</td> <td>Aircraft ICAO typecode</td> </tr> <tr> <td>icaoaircrafttype</td> <td>string</td> <td>ICAO aircraft type</td> </tr> <tr> <td>wtc</td> <td>string</td> <td>ICAO wake turbulence category</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td> <p>string</p> </td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometre</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>operator_country</td> <td>string</td> <td>ISO Alpha-3 country code of the operator</td> </tr> <tr> <td>operator_region</td> <td>string</td> <td>Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> <tr> <td>wind_speed_knts</td> <td>integer</td> <td>METAR, surface wind speed in knots</td> </tr> <tr> <td>wind_dir_deg</td> <td>integer</td> <td>METAR, surface wind direction in degrees</td> </tr> <tr> <td>wind_gust_knts</td> <td>integer</td> <td>METAR, surface wind gust speed in knots</td> </tr> <tr> <td>visibility_m</td> <td>float</td> <td>METAR, visibility in m</td> </tr> <tr> <td>temperature_deg</td> <td>integer</td> <td>METAR, temperature in degrees Celsius</td> </tr> <tr> <td>press_sea_level_p</td> <td>float</td> <td>METAR, sea level pressure in hPa</td> </tr> <tr> <td>press_p</td> <td>float</td> <td>METAR, QNH in hPA</td> </tr> <tr> <td>weather_intensity</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - intensity</td> </tr> <tr> <td>weather_precipitation</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - precipitation</td> </tr> <tr> <td>weather_desc</td> <td>list</td> <td>METAR, list of present weather codes: qualifier - descriptor</td> </tr> <tr> <td>weather_obscuration</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - obscuration</td> </tr> <tr> <td>weather_other</td> <td>list</td> <td>METAR, list of present weather codes: weather phenomena - other</td> </tr> </tbody> </table> <p>This data set is augmented with data from various public data sources. Aircraft related data is mostly from the <a href="https://opensky-network.org/aircraft-database">OpenSky Network's aircraft data base</a>, the METAR information is from the <a href="https://mesonet.agron.iastate.edu/info/datasets/metar.html">Iowa State University</a>, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the <a href="https://library.wmo.int/doc_num.php?explnum_id=11220">WMO's Aerodrom Reports and Forecasts handbook</a>.</p> <p><strong>go_arounds_agg.csv.gz</strong></p> <p>Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:</p> <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>airport</td> <td>string</td> <td>ICAO airport code where the aircraft is landing</td> </tr> <tr> <td>runway</td> <td>string</td> <td>Runway designator on which the aircraft landed</td> </tr> <tr> <td>n_landings</td> <td>integer</td> <td>Total number of landings observed on this runway in 2019</td> </tr> <tr> <td>ga_rate</td> <td>float</td> <td>Go-around rate, per 1000 landings</td> </tr> <tr> <td>glide_slope_angle</td> <td>float</td> <td>Angle of the ILS glide slope in degrees</td> </tr> <tr> <td>has_intersection</td> <td>string</td> <td>Boolean that is true if the runway has an other runway intersecting it, otherwise false</td> </tr> <tr> <td>rwy_length</td> <td>float</td> <td>Length of the runway in kilometres</td> </tr> <tr> <td>airport_country</td> <td>string</td> <td>ISO Alpha-3 country code of the airport</td> </tr> <tr> <td>airport_region</td> <td>string</td> <td>Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)</td> </tr> </tbody> </table> <p>This aggregated data set is used in the paper for the generalized linear regression model.</p> <p><strong>Downloading the trajectories</strong></p> <p>Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the <a href="https://github.com/xoolive/traffic">Traffic</a> library for easy access to the database:</p> <pre><code class="language-python">import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic # load minimum data set df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"]) # select London City Airport, go-arounds, and 2019-01-04 airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc ) df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)") # iterate over flights and pull the data from OpenSky Network flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time # fetch the data from OpenSky Network flights.append( opensky.history( start=start_time.strftime("%Y-%m-%d %H:%M:%S"), stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"), callsign=row["callsign"], return_flight=True, ) ) # The flights can be converted into a Traffic object Traffic.from_flights(flights)</code></pre> <p><strong>Additional files</strong></p> <p>Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:</p> <ul> <li><strong>validation_table.xlsx</strong>: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.</li> <li><strong>validation_sample.zip</strong>: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.</li> </ul>
GPS data set used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations"
<p>Data set of continuous GPS observations at CVAN site in Gran Canaria (Canary Islands, Spain). The period of observation spans from July 3, 2013 to November 30, 2015. Data was acquired during the execution of the research project GCL2011-25494 of the Spanish Research Agency. </p> <p>This data set belongs to the Research Group ‘Geodesia’ of the University Complutense of Madrid, Spain, and has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.</p>
FIGURE. DnaSP DNA polymorphism analysis - Nucleotide variability (Pi) comparison between Strobilanthes lupulina and S. glandulata. The window length and step size were set to 600bp and 200bp respectively. The most varying regions and the commonly used barcoding regions are listed against the base-pair differences found. in Strobilanthes glandulata (Acanthaceae), a new species from Sri Lanka based on the morphological and molecular evidences
FIGURE. DnaSP DNA polymorphism analysis - Nucleotide variability (Pi) comparison between Strobilanthes lupulina and S. glandulata. The window length and step size were set to 600bp and 200bp respectively. The most varying regions and the commonly used barcoding regions are listed against the base-pair differences found.
Data Sets for: Complete Description of the LaCl3-NaCl Melt Structure and the Concept of a Spacer Salt that Causes Structural Heterogeneity
<p>Data Sets for published figures and sample input files for PIM and AIMD simulations in paper <a href="https://doi.org/10.1021/jacs.2c09987">https://doi.org/10.1021/jacs.2c09987</a></p> <p> </p> <p>Complete Description of the LaCl3–NaCl Melt Structure and the Concept of a Spacer Salt That Causes Structural Heterogeneity</p> <p>Matthew S. Emerson, Shobha Sharma, Santanu Roy*, Vyacheslav S. Bryantsev*, Alexander S. Ivanov*, Ruchi Gakhar*, Michael E. Woods, Leighanne C. Gallington, Sheng Dai, Dmitry S. Maltsev, and Claudio J. Margulis*</p> <p> </p>
Public Profile Settings (TRIPLE Video Tutorial Series)
<p>In this tutorial series, researcher Agnieszka Szulińska and Research Infrastructure Coordinator Edward Gray discuss gotriple.eu. This episode explores the opportunities GoTriple offers for the creation of a public profile.</p> <p>Video recorded and edited by Fabian Fess.</p>
Data set for: "Model atmospheric aerosols convert to vesicles upon entry into aqueous solution"
<p>This document compiles raw data used in the aerosol to vesicle transformation study carried out by <strong>Serge Nader <em>et al.</em></strong><br> For detailed information and context, refer to the main article and its supplementary material published in ACS Earth and Space Chemistry.</p> <p>The Excel file contains data relevant to each figure in the main article and supporting information. The additional compressed file contains raw Transmission Electron Microscopy (TEM) photographs.</p>
Data set for EMPIR project MeDDII Paper bmt-2022-0040
<p>Data set for EMPIR project MeDDII Paper</p> <p>https://doi.org/10.1515/bmt-2022-0040</p>
Data set for EMPIR project MeDDII Report A2.3.4 D4
<p>Data set for EMPIR project MeDDII Report A2.3.4 D4</p> <p>Report on the use of a calibrated microfluidic multi-parameter chip for the in-line measurement of pressure, viscosity and temperature</p>
Data set for EMPIR project MeDDII Report A2.1.4
<p>Data set for EMPIR project MeDDII Report A2.1.4</p> <p>Validation of the pipe viscometers (primary standards) for the in-line measurement of the dynamic viscosity of Newtonian liquids</p>
Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90
<p>Data set for EMPIR project MeDDII Paper FLOMEKO 2022 ID90</p> <p>Dynamic vs constant liquid flow calibrations down to 20 nL/min</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.