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40,091 results for “recordings”
Beirut blast seismic records and Rinex data of CORS-TR DOY217 of 2020
<p>These are the seismic data and the Rinex observation data for the DOY 217 (August 4, 2020), the day of the explosion of Beirut Port. The data was used in the results and analysis of a manuscript entitled with "Investigation of the Lithosphere-Atmosphere-Ionosphere Coupling during Beirut Explosion, Lebanon, by Geodetic and Seismological data"</p>
Neogene–Quaternary uplift and landscape evolution in northern Greenland recorded by subglacial valley morphology: Datasets
<p>This dataset contains a csv file of subglacial valley morphology derived from radio-echo sounding datasets in northern Greenland, and an ESRI shapefile of the interpreted channel network. For further documentation of the data please view the README.txt file.</p> <p>RADAR-DERIVED VALLEY MORPHOLOGY</p> <ul> <li><strong>northern_Greenland_valley_morphology.csv</strong>: location and morphology of subglacial valleys in northern Greenland, as imaged by airborne radio-echo sounding datasets.</li> </ul> <p>SUBGLACIAL VALLEY NETWORK</p> <ul> <li><strong>northern_Greenland_valley_network.shp (and ancillary files: .cpg, .dbf, .prj, .qpj, .shx)</strong>: ESRI shapefile of the interpreted valley network in the northern Greenland subglacial drainage catchment.</li> </ul>
Database of Webcam Photos Recorded in Videoconference-like Settings
<p><br> <strong>Webcam photos of persons (senders) locking at targets on their computer screens</strong></p> <p>n: number of the sender<br> hp: head position<br> tp: target position</p> <p>Positioning of hp:</p> <pre><code>[7] [5] [10] [3] [8] [1][0][2] [11] [4] [9] [6] [12]</code></pre> <p>Positioning of tp:</p> <pre><code>[ 0][ 1][ 2][ 3][ 4][ 5][ 6] [ 7][ 8][ 9][10][11][12][13] [14][15][16][17][18][19][20]</code></pre> <p><br> File structure:<br> P1-8: A directory for each sender.<br> T1: Collection day 1, T2: Collection day 2.<br> Each combination of hp and tp was recorded once per collection day.</p> <p>Naming of the images:<br> n_hp_tp.png</p>
IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"
<p>IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"</p> <p>Includes SEM QEMSCAN® and <sup>40</sup>Ar/<sup>39</sup>Ar data for International Ocean Discovery Program (IODP) Expedition 382 Site U1538. Also includes a movie of a 3D-volume realization of an iceberg-rafted sedimentary layer from this site based on non-destructive X-ray microtomography imaging.</p> <p> </p> <p><strong>Data Set Captions:</strong></p> <p> </p> <p><strong>Data Set S1. </strong>Modal mineralogy data based on QEMSCAN® analyses, which infer minerals from chemistry. The mineral name assignations for each chemistry-based category stated in this table are aided by visual (microscope-based) inspection of the raw sieved samples.</p> <p><strong>Data Set S2. </strong>Mineral association data based on QEMSCAN® analyses. Please read data in columns, mineral against mineral (down then across left). These data define what touches what in the sample and is displayed as a percentage. Association refers to adjacency. Two minerals are “associated” if a pixel of one of the minerals occurs adjacent to a pixel of the other mineral. iExplorer software used scans the measured particles horizontally, from left to right, counting the associations that occur in the images (so the more pixels/closer the x-ray spacing the more accurate the data). Each column is independent. That is, it is split into a percentage of what touches what, so it is not expected that any two minerals’ data are reciprocal. The background category primarily reflects the free boundaries of ‘grains’ rather than liberated grains/particles. While it may provide an indicator of liberation, it does not represent liberation since it does not describe ‘particles’ which are made up of mineral grains. Inclusions and composite particles are therefore not described. Please consider the modal mineralogy (Tab. S1) when examining these mineral association data.</p> <p><strong>Data Set S3. </strong>Lithotyping data based on QEMSCAN® analyses. Particles have been digitally filtered using a set of lithotype rules (also displayed in this data set). These rules are based on the mineral grains in the particles themselves and use their area percent within each particle and their size in microns. The lithotype names stated here are largely assigned based on the dominant mineral grain in each category.</p> <p><strong>Data Set S4. </strong>40Ar/39Ar ages of individual sand-sized hornblende and mica. See main text for method used to generate these ages.</p> <p><strong>Data Set S5.</strong> Ties to place Hole U1538A NGR data on Dove Basin Stack (Reilly et al., 2021) depths.</p> <p><strong>Movie S1. </strong>3D-volume realization based on non-destructive X-ray microtomography imaging of a centimeter-scale iceberg-rafted debris-rich layer in Hole U1538A-36X-3W. 3D images were generated using a helical scanning trajectory that allows for long scan sequences and fast acquisition time. Based on the sample geometry, a voxel (pixel) resolution of ~14-μm was achieved. The 7000+ projection images were reconstructed to produce a 3D volume of image intensities (where higher values indicate greater x-ray attenuation). Avizo software was used for 3D segmentation and volume rendering to visualize gravel and sand to create this animation. The different colors assigned to each clast were chosen arbitrary.</p>
Dataset for 'Valley Networks and the Record of Glaciation on Ancient Mars'
<p>Supplementary information (in pdf format, ~800 kb) containing the model, setup, and parameter analysis supporting the manuscript 'Valley Networks and the Record of Glaciation on Ancient Mars'. </p> <p>Includes a detailed table of parameters with references. </p>
4 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file
<p>This is a 4 image lysozyme datasets derived from https://doi.org/10.5281/zenodo.3352357. The specific 4 images are able to be processed by the software package DIALS using commands in the linked dataset above. The images were rounded to integer and compressed to save file space using this script:</p> <pre><code class="language-python">import shutil, h5py import numpy as np shutil.copyfile('../lyso009a_0087.JF07T32V01_master.h5', 'lyso009a_0087.JF07T32V01_master_4img.h5') h5 = h5py.File('lyso009a_0087.JF07T32V01_master_4img.h5', 'r+') data = h5["entry/data/data"][()] del h5["entry/data/data"] h = h5["entry/data"] subset = data[5:9].astype(np.int32) h.create_dataset("data", subset.shape, subset.dtype, subset, compression="gzip", compression_opts=9) h5.close()</code></pre> <p>The .expt file was created by dials.import and is useful for regression testing in DIALS.</p>
A collection of fully-annotated soundscape recordings from the Western United States
<p>This collection contains 33 hour-long soundscape recordings, which have been annotated with 20,147 bounding box labels for 56 different bird species from the Western United States. The data were recorded in 2018 in the Sierra Nevada, California, USA. This collection has partially been featured as test data in the 2021 BirdCLEF competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>Measuring the effects of forest management activities in the Sierra Nevada, California, USA can reveal a potential correlation with avian population density and diversity. For this dataset, passive acoustic surveys were conducted in the Lassen and Plumas National Forests in May-August 2018. Survey grid cells (4 km<sup>2</sup>) were randomly selected from a 6,000-km<sup>2</sup> area, and SWIFT recording units were deployed at locations conducive to sound propagation (e.g., ridges rather than gullies) within those cells. The sensitivity of the used microphones was -44 (+/-3) dB re 1 V/Pa. The microphone's frequency response was not measured, but is assumed to be flat (+/- 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 38 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of -/+ 0.9 V. Recording units recorded continuously 17:00 - 23:59, 0:00 - 10:00, one-hour files were stored as uncompressed WAVE sampled at 32 kHz and later converted to FLAC. Parts of this dataset have previously been used in the 2021 BirdCLEF competition.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>We subsampled data for this collection by selecting locations that spanned the full elevational and latitudinal gradients of our study area (~840 – 1700 m asl and 39.41 – 40.71°N), and thus represent a broad range of plant communities. A single annotator boxed every bird call he could recognize, ignoring those that are too faint or unidentifiable. Raven Pro software was used to annotate the data. Provided labels contain full bird calls that are boxed in time and frequency. The annotator was allowed to combine multiple consecutive calls of one species into one bounding box label if pauses between calls were shorter than five seconds. We use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list).</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date and timestamp in PDT. As an example, the file “SNE_001_20180509_050002.flac” has sequential ID 001 and was recorded on May 9th 2018 at 05:00:02 PDT. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.</p> <p><strong>Acknowledgements </strong></p> <p>The collection and annotation of this dataset was funded by the U.S. Forest Service Region 5 and the California Department of Fish and Wildlife.</p>
A collection of fully-annotated soundscape recordings from the Island of Hawai'i
<p>This collection contains 635 soundscape recordings with a total duration of almost 51 hours, which have been annotated by expert ornithologists who provided 59,583 bounding box labels for 27 different bird species from the Hawaiian Islands, including 6 threatened or endangered native birds. The data were recorded between 2016 and 2022 at four sites across Hawai‘i Island. This collection has partially been featured as test data in the 2022 BirdCLEF competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>Soundscapes for this collection were recorded for various research projects by the Listening Observatory for Hawaiian Ecosystems (LOHE) at the University of Hawai‘i at Hilo. The recordings were collected using Wildlife Acoustics Inc. Song Meters (models 2, 4, or Mini), as 16-bit wav files at a sampling rate of 44.1 kHz, using the default gain settings of each model. Further specifics for each recording, such as recording location and habitat type, can be found in the metadata provided. Soundscapes in this collection vary in length, ranging from just under a minute to 9 minutes in duration. All audio was unified, converted to FLAC, and resampled to 32 kHz for this collection. Parts of this dataset have previously been used in the 2022 BirdCLEF competition.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>This collection is a subset of the files recorded over the course of the LOHE lab’s respective studies. The data were subsampled for annotation by aurally scanning the recordings and visually scanning spectrograms generated using Raven Pro software for target species of interest to the individual research project for which each recording was collected. Recordings that did not contain vocalizations of the species of interest were excluded from full annotation and thus this collection. </p> <p>Using Raven Pro, annotators were asked to create a selection box around every bird call they could recognize, ignoring those that were too faint or unidentifiable at a spectrogram window size of 700 points. Provided labels contain full bird calls that are boxed in time and frequency. Annotators were allowed to combine multiple consecutive calls of the same species into one bounding box label if pauses between calls were shorter than 0.5 seconds. We converted labels to eBird species codes, following the 2021 eBird taxonomy (Clements list).</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, site ID, recording date, and timestamp in HST. As an example, the file “UHH_001_S01_20161121_150000.flac” has sequential ID 001 and was recorded at site S01 on Nov 21st, 2016 at 15:00:00 HST. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz, and an eBird species code. These species codes can be assigned to the scientific and common name of a species with the “species.csv” file. The approximate recording location with Universal Transverse Mercator (UTM) coordinates and other metadata can be found in the “recording_location.csv” file.</p> <p><strong>Acknowledgements </strong></p> <p>Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection. Specifically, we want to thank Charlotte Forbes-Perry with the Pacific Cooperative Studies Unit, University of Hawai'i at Hawai‘i Volcanoes National Park as well as the following current and past members of the LOHE lab (in alphabetical order): Keith Burnett, Saxony Charlot, Noah Hunt, Caleb Kow, Elizabeth Lough, and Bret Mossman.</p> <p>Access and permits to record soundscapes were provided by (in alphabetical order): Hakalau Forest National Wildlife Refuge, the State of Hawai‘i Department of Land and Natural Resources Division of Forestry and Wildlife, and the U.S. Fish and Wildlife Service.</p> <p>We would also like to acknowledge our funding sources (in alphabetical order): The National Park Service Inventory and Monitoring Division, the National Science Foundation, and the U.S. Army Engineer Research and Development Center.</p>
A collection of fully-annotated soundscape recordings from the Northeastern United States
<p>This collection contains 285 hour-long soundscape recordings, which have been annotated by expert ornithologists who provided 50,760 bounding box labels for 81 different bird species from the Northeastern USA. The data were recorded in 2017 in the Sapsucker Woods bird sanctuary in Ithaca, NY, USA. This collection has (partially) been featured as test data in the 2019, 2020 and 2021 BirdCLEF competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>As part of the Sapsucker Woods Acoustic Monitoring Project (SWAMP), the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology deployed 30 first-generation SWIFT recorders in the surrounding bird sanctuary area in Ithaca, NY, USA. The sensitivity of the used microphones was -44 (+/-3) dB re 1 V/Pa. The microphone's frequency response was not measured, but is assumed to be flat (+/- 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 33 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of -/+ 0.9 V. This ongoing study aims to investigate the vocal activity patterns and seasonally changing diversity of local bird species. The data are also used to assess the impact of noise pollution on the behavior of birds. Recordings were recorded 24 h/day in 1-hour uncompressed WAVE files at 48 kHz, converted to FLAC and resampled to 32 kHz for this collection. Parts of this dataset have previously been used in the 2019, 2020 and 2021 BirdCLEF competition.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>We subsampled data for this collection by randomly selecting one 1-hour file from one of the 30 different recording units for each hour of one day per week between Feb and Aug 2017. For this collection, we excluded recordings that were shorter than one hour or did not contain a bird vocalization. Annotators were asked to box every bird call they could recognize, ignoring those that are too faint. Raven Pro software was used to annotate the data. Provided labels contain full bird calls that are boxed in time and frequency. Annotators were allowed to combine multiple consecutive calls of one species into one bounding box label if pauses between calls were shorter than five seconds. We use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list).</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date, and timestamp in UTC. As an example, the file “SSW_001_20170225_010000Z.flac” has sequential ID 001 and was recorded on Feb 25th, 2017 at 01:00:00 UTC. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz, and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. Unidentifiable calls have been marked with “????” and are included in the ground truth annotations. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.</p> <p><strong>Acknowledgements </strong></p> <p>Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection (individual contributors in alphabetic order): Jessie Barry, Sarah Dzielski, Cullen Hanks, W. Alexander Hopping, Robert Koch, Jim Lowe, Jay McGowan, Ashik Rahaman, Yu Shiu, Laurel Symes, and Matt Young. </p> <p><strong>Version history</strong></p> <p>Version 2: Unidentifiable calls have been marked with “????” and added as bounding box labels to the ground truth annotations.<br> Version 1: Initial release.</p>
Ablation stake length record at Glaciar Perito Moreno, Patagonia
<p>These are records of ablation stake length observed at Glaciar Perito Moreno in southern Patagonia. </p>
The dataset of Hong Kong Housing transaction records (1997-2018, after an incoming data quality processing)
<p>These detailed housing transaction records of over 2 million property rights entries in Hong Kong's property markets over the past 23 years (1997 - 2018).</p>
Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
<p>EEG signals recorded from seven healthy subjects. On average, Seventy-three minutes of EEG data were recorded from 31 electrodes placed according to the extended 10-20 system. Signals are used in the paradigm-agnostic post-hoc labeled dataset generation framework for benchmarking of oscillatory neural decoding methods.</p>
Behaviour, Welfare and Performance Records from Duroc Pigs
<p>This is a database generated within Feed-a-Gene (H2020) and GENEF (Spanish INIA) projects. It comprise feeding, growth and body performance of Duroc pigs. It also includes feeding behavoiur data as recorded by automatic feeding stations and also a number of behaviour and welfare traits assessed in a reduced subset of animals.</p> <p>This database comprise Deliverable D2.1 from Feed-a-Gene, a document describing the data structura can be get in (link to EU publication of the Report).</p> <p> </p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Granular Material Candidates
<p>A database of 14 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Stressful crystal histories recorded around melt inclusions in volcanic quartz
<p>Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals preserve similar and relatively small magnitudes of elastic residual stress (mean 53-135 MPa, median 46-116 MPa) in comparison to the strength of quartz (~10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption. The data collected using Laue microdiffraction at Lawrence Berkeley National Laboratory Advanced Light Source beamline 12.3.2 are included below as .xlsx files. Data was processed and analyzed using XMAS (Tamura, 2014) and XtalCAMP (Li et al., 2020).</p>
Modelled urban climate island during the record-breaking 2022 heatwave in London
<p>This record is created as a data supplement for the manuscript "Estimated mortality attributable to the urban heat island during the record-breaking 2022 heatwave in London".</p> <p>These data were produced using the Weather Research Forecasting model with BEP-BEM. The model setup is described in Brousse et al (2023) <a href="doi.org/10.1175/JAMC-D-22-0142.1">10.1175/JAMC-D-22-0142.1</a>. These data cover the period 2022-07-10 to 2022-07-25, during which temperatures exceding<strong> </strong>40 °C were recorded in London for the first time.</p> <p>The data comprise two NetCDF files. One is labelled "Urb" one "Nourb". In the "Nourb" file, the urban tile is removed from the model and the land surface replaced by the nearest natural tile. This can be used to estimate the influence of the urban tile on the local climate.</p> <p>Variables included in the file are T2 (temperature at 2 m elevation in Kelvin), V10 and U10 (winds at 10 m elevation in metres per second), PSFC (surface level pressure in Pascal), RAINNC (rain in mm), TH2 (potential temperature at 2m elevation in Kelvin), and Q2 (specific humidity at 2 m elevation, which is dimensionless). All variables are provided at hourly timestep.</p> <p>Queries about this dataset can be directed to o.brousse@ucl.ac.uk</p>
Elbrus Ice Core, Caucasus record of ammonia (NH4+)
<p><span>A deep ice core was drilled to bedrock (182.6 m) in 2009 on the western plateau of <span>Mount Elbrus </span>(ELB, 43°N, 42°E; 5115 m above sea level, asl) in the Caucasus (Russia).</span><span> </span><span>The upper 168.6 m (131.5 meters</span><span> </span><span>water equivalent, mwe) depth of the ice core were first dated by annual layer counting using pronounced seasonal variations in ammonium and succinate concentrations, both exhibiting well-marked winter minima (Mikhalenko et al., 2015; Preunkert et al., 2019). <span>Chemical measurements were done with a Dionex ICS-1000 chromatograph equipped with a CS12 separator column for cations </span>(Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, and NH<sub>4</sub><sup>+</sup>), a Dionex 600 equipped with an AS11 separator column for anions (Cl<sup>-</sup>, NO<sub>3</sub><sup>-</sup>, and SO<sub>4</sub><sup>2-</sup>) and light carboxylates. Detailed working conditions are given in Legrand et al. (2013). <span>Using the winter ammonium/succinate minima we determined half-year summer and winter means of of ammonia (NH4+) from 1748 to 2009.</span></span></p>
Sample Records: Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>The project contains several underlying datasets essential for replicating the study's findings. The dataset <strong>01.1_PRIMERPRISMA_IDENTIFICATION.xlsx</strong> includes the initial selection of 6 general terms related to environment and sustainability and 11 specific terms related to disinformation, summarizing the selected keywords, generated Boolean operators, and initial search results, yielding 783 records. The <strong>01.2_PRIMER PRIMA-SCREENING.xlsx</strong> file details the screening process, eliminating duplicates and non-English documents, resulting in 271 retained records. The <strong>01.3_PRIMER PRISMA_INCLUDED.xlsx</strong> file contains results after further screening, retaining 82 documents with expanded bibliometric details. The <strong>02.1_SEGUNDOPRISMA_IDENTIFICATION.xlsx</strong> file documents the second phase of identification using new terms related to climate and disinformation, retrieving 174 records. The <strong>02.2_SEGUNDOPRISMA_SCREENING.xlsx</strong> file includes the screening process for the second phase, reducing records to 75, with an abstract review retaining 2 documents. The <strong>02.3_SEGUNDOPRISMA_INCLUDED.xlsx</strong> file integrates documents from both search phases and other sources, culminating in a final review of 86 documents. The <strong>3.1_Other sources.xlsx</strong> file includes additional relevant sources identified during the review process. Finally, the <strong>4-Final included.xlsx</strong> file contains the final set of 75 publications subjected to the DESLOCIS analysis model.</p>
Sample Records (Analytical procedure): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes t<span>he online form and the results from the quantitative phase of the study: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon</span></p> <p>To duplicate the form you can use: https://forms.office.com/Pages/ShareFormPage.aspx?id=6sSEXw03nkuDDHVvi_G1Hw0s3dVrMb1NsO12gDNTB9BUREo4WENRMFFDN1lOSlRSU0xJNkVHWURWUS4u&sharetoken=rg4Qfg19O4UgYzUB084C </p>
Sample Records (PRISMA Checklist and Flow diagram): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes: the PRISMA Checklist and the <span>PRISMA Flow diagram of the study titled: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon.</span></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.