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3,118 results for “resources”
Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
Open the record for dataset details and reuse information.
GREEN-VARAN scores resources (CADD GRCh37)
<p>Processed CADD scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 version for CADD v.1.4.</p> <p>See: <a href="https://cadd.gs.washington.edu/">https://cadd.gs.washington.edu/</a></p> <p>If you use CADD score annotations with GREEN-VARAN don't forget to cite also the original CADD paper.</p>
GREEN-VARAN scores resources (FATHMM-XF GRCh38)
<p>Processed FATHMM-XF non-coding scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh38 version for FATHMM-XF v2.3 non-coding annotations.</p> <p>See: <a href="http://fathmm.biocompute.org.uk/">http://fathmm.biocompute.org.uk/</a></p> <p>If you use FATHMM-XF score annotations with GREEN-VARAN don't forget to cite also the original FATHMM-XF paper.</p>
GREEN-VARAN scores resources (EIGEN GRCh38)
<p>Processed EIGEN and EIGEN-PC scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh38 version for EIGEN v1.1 non-coding annotations, obtained by coordinates liftover.</p> <p>See: <a href="http://www.columbia.edu/~ii2135/eigen.html">http://www.columbia.edu/~ii2135/eigen.html</a></p> <p>If you use EIGEN score annotations with GREEN-VARAN don't forget to cite also the original EIGEN paper.</p>
List of capacity building resources for combating climate mis/disinformation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on combating climate change misinformation and disinformation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
List of capacity building resources for climate change adaptation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
Experimental data and software for: Defaults: a double-edged sword in governing common resources
<p>Experimental data and software for the paper: <strong>Defaults: a double-edged sword in governing common resources</strong></p> <p>The experiment consisted in three treatments of the Common Pool Resource Dilemma, where three default interventions were applied: pro-social, self-serving and no default. Plus, the participants had to complete an SVO task and a Risk assessment task.</p> <h4>Description of the data and file structure</h4> <p>In the file called <code>all_participants.csv</code> is the full dataset of all participants that took part of the experiment. This includes participants who will end up excluded and dropouts.</p> <p>The experimental data files come in two formats: wide and long. The wide version, called <code>data_wide_format.csv</code> contains one row per participant and a column for all the fields, including rounds from 1 to 10 of the CPR task. Also, this file includes all demographic information of the participants, times and payments. The ID shown is generated internally and has no relationship with the participants' Prolific ID.</p> <p>The long version, called <code>data_long_format.csv</code>, contains 10 rows per participant, and columns for the extraction and other variables necessary for analysis. This version contains the necessary data to reproduce all the figures and statistics detailed in the main manuscript.</p> <p>In both of the previous files, the participants taken into account were the ones who completed the whole experiment. Those who did not complete the comprehension test, dropped out or did not sign the Informed Consent Form were excluded from the experimental data used. More details in the Methods below.</p> <p>In the file <code>default_opinions.csv</code>, we manually classified the responses by participants to whether they were influenced by the default presented.</p> <p>The file "<code>Instructions of the experiment.pdf</code>" contains the instructions of the experiment as shown to participants, also screenshots of the platform.</p>
OpenCitations Meta RDF dataset of bibliographic resources metadata and its provenance information
<div> <p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>bibliographic resources </strong>(<a href="http://purl.org/spar/fabio/Expression" target="_blank" rel="noopener">http:///purl.org/spar/fabio/Expression</a>). It contains all the metadata and its provenance information, structured specifically around bibliographic resources, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /br/06250/10000/1000/1000.zip, while information about provenance in /br/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p> <p> </p> </div>
GREEN-VARAN additional regions resources
<p>Processed functional regions datasets to be used with GREEN-VARAN</p> <p>This repository contains the GRCh37 and GRCh38 files as indexed BED files. The GRCh38 version of UCNE and TAD were obtained by coordinate liftover.</p> <ul> <li>TFBS from ENCODE v3</li> <li>DNase hypersensitivity peaks from ENCODE v3</li> <li>UCNE (ultra-conserved non-coding elements) from https://ccg.epfl.ch/UCNEbase/</li> <li>TAD (topologically associating chromatin domains) from http://dna.cs.miami.edu/TADKB/</li> <li>Super enhancer from dbSuper at http://bioinfo.au.tsinghua.edu.cn/dbsuper/</li> </ul> <p>Please refer to the original datasets listed in related identifiers and references for eventual limits in use and distribution</p>
GREEN-VARAN scores resources (ncER)
<p>Processed ncER scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 and GRCh38 versions for ncER v2 single-base resolution annotations. GRCh38 file is obtained by coordinates liftover.</p> <p>Original scores obtained from: https://github.com/TelentiLab/ncER_datasets </p> <p>Original publication: https://www.nature.com/articles/s41467-019-13212-3</p> <p>If you use ncER score annotations with GREEN-VARAN don't forget to cite also the original ncER paper.</p>
GDPRtEXT - GDPR as a Linked Data Resource
<p>The General Data Protection Regulation (GDPR) is the new European data protection law whose compliance affects organisations in several aspects related to the use of consent and personal data. With emerging research and innovation in data management solutions claiming assistance with various provisions of the GDPR, the task of comparing the degree and scope of such solutions is a challenge without a way to consolidate them. With GDPR as a linked data resource, it is possible to link together information and approaches addressing specific articles and thereby compare them. Organisations can take advantage of this by linking queries and results directly to the relevant text, thereby making it possible to record and measure their solutions for compliance towards specific obligations. GDPR text extensions (GDPRtEXT) uses the European Legislation Identifier (ELI) ontology published by the European Publications Office for exposing the GDPR as linked data. The dataset is published using DCAT and includes an online webpage with HTML id attributes for each article and its subpoints. A SKOS vocabulary is provided that links concepts with the relevant text in GDPR.</p>
DS_Wave_Mutriku: Wave resource at Mutriku (Spain)
<p>Data obtained from the RBR virtuoso pressure sensor deployed in Mutriku: i) winter 2016-2017; ii) Spring 2018.</p> <p>Sensor is located at 10 m water depth, 200 m off the shoreline plant (43º18'52"N, 2º22'34").</p> <p> </p>
Datasets for paper 'Cabello, V., Renner, A., Giampietro, M. 2019. Relational analysis of the resource nexus in arid land crop production. Advances in Water Resources 130:258-629'
<p>Datasets produced for the paper Cabello, V., Renner, A., Giampietro, M. 2019.<em> </em>Relational analysis of the resource nexus in arid land crop production. <em>Advances in Water Resources </em>130:258-269</p>
S43 | NEUROTOXINS | Neurotoxicants Collection from Public Resources
<p>This is the collection associated with list S43 NEUROTOXINS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S43</p> <p>NEUROTOXINS</p> <p><strong>Neurotoxicants Collection from Public Resources</strong></p> <p>NEUROTOXINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/NEUROTOXINS_14022019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/NEUROTOXINS_14022019.csv">CSV</a> (14/02/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/neurotoxins">NEUROTOXINS List</a></p> <p>NEUROTOXINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/NEUROTOXINS_InChIKeys_14022019.txt">InChIKeys</a> (14/02/2019)</p> <p>A list of neurotoxicants compiled from public resources, details on CompTox and Schymanski <em>et al. </em>(submitted). </p>
S44 | STATINS | Statins Collection from Public Resources
<p>This is the collection associated with list S44 STATINS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S44</p> <p>STATINS</p> <p><strong>S</strong><strong>tatins Collection from Public Resources</strong></p> <p>STATINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/STATINS_14022019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/STATINS_14022019.csv">CSV</a> (14/02/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/statins">STATINS List</a></p> <p>STATINS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/STATINS_InChIKeys_14022019.txt">InChIKeys</a> (14/02/2019)</p> <p>A list of statins (lipid-lowering medications) compiled from public resources, details on CompTox. </p>
Polarity Shifter Resources
<p>This repository was created as part of Marc Schulder's doctoral thesis <a href="https://dx.doi.org/10.22028/D291-28454"><em>Sentiment Polarity Shifters: Creating Lexical Resources through Manual Annotation and Bootstrapped Machine Learning</em></a></p> <p>The collection of polarity shifter resources presented herein is also connected to a number of publications:</p> <ul> <li><strong><a href="https://doi.org/10.5281/zenodo.3365609">Schulder et al. (IJCNLP 2017)</a>:</strong> Lexicon of English Verbal Shifters (bootstrapped, lemma-level) and sentiment verb phrase dataset. <a href="https://doi.org/10.5281/zenodo.3364812"><em>doi: 10.5281/zenodo.3364812</em></a></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3365683">Schulder et al. (LREC 2018)</a>:</strong> Lexicon of English Verbal Shifters (manual, sense-level). <em><a href="https://doi.org/10.5281/zenodo.3365288">doi: 10.5281/zenodo.3365288</a></em></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3365694">Schulder et al. (COLING 2018)</a>:</strong> Lexicon of German Verbal Shifters (bootstrapped, lemma-level). <em><a href="https://doi.org/10.5281/zenodo.3365370">doi: 10.5281/zenodo.3365370</a></em></li> <li><strong><a href="https://www.aclweb.org/anthology/2020.lrec-1.616/">Schulder et al. (LREC 2020)</a>:</strong> Lexicon of Polarity Shifting Directions (supervised classification, lemma-level). <em><a href="https://doi.org/10.5281/zenodo.3545947">doi: 10.5281/zenodo.3545947</a></em></li> <li><strong><a href="https://doi.org/10.1017/S135132492000039X">Schulder et al. (JNLE 2020)</a>:</strong> General Lexicon of English Shifters (bootstrapped, lemma-level). <em><a href="https://doi.org/10.5281/zenodo.3365601">doi: 10.5281/zenodo.3365601</a></em></li> </ul> <p><strong>Data</strong></p> <p>The repository contains the following resources:</p> <ol> <li>A general lexicon of English polarity shifters, covering verbs, adjectives and nouns. Provides lemma labels for shifters and for which polarities they can affect.</li> <li>A lexicon of English verbal shifters. Provides word sense labels for shifters and their shifting scopes.</li> <li>A lexicon of German verbal shifters. Provides lemma labels for shifters.</li> <li>A set of verb phrases annotated for shifting polarities.</li> </ol> <p><strong>1. English Shifter Lexicon (Lemma)</strong></p> <p>A lexicon of 9145 English words, annotated for whether they are polarity shifters and which polarities they affect. The lexicon is based on the vocabulary of WordNet v3.1 (Miller et al., 1990). It contains 2631 shifters and 6514 non-shifters.</p> <ul> <li>File: <code>shifters.english.all.lemma.txt</code></li> <li>The lexicon is a comma-separated value (CSV) table.</li> <li>Each line follows the format <code>POS,LEMMA,SHIFTER_LABEL,DIRECTION_LABEL,SOURCE</code>. <ul> <li><code>POS</code>: The part of speech of the word (<code>verb</code>, <code>noun</code>, <code>adj</code>)</li> <li><code>LEMMA</code>: The lemma representation of the word in question. Multiword expressions are separated by an underscore (<code>WORD_WORD</code>).</li> <li><code>SHIFTER_LABEL</code>: Whether the word is a polarity shifter (<code>SHIFTER</code>) or a non-shifter (<code>NONSHIFTER</code>).</li> <li><code>DIRECTION_LABEL</code>: Whether the shifter affects only positive polarities (<code>AFFECTS_POSITIVE</code>), only negative polarities. (<code>AFFECTS_NEGATIVE</code>) or can shift in both directions (<code>AFFECTS_BOTH</code>). Non-shifters are all labeled (<code>NONE</code>).</li> <li><code>SOURCE</code>: Whether the word was part of the gold standard. (<code>GOLD_STANDARD</code>) or was bootstrapped (<code>BOOTSTRAPPED</code>). Note that while bootstrapped shifter labels are verified by a human annotator, their direction label is automatically classified without verification.</li> </ul> </li> </ul> <p><strong>2. English Verbal Shifter Lexicon (Word Sense)</strong></p> <p>A lexicon of word senses of English verbs, annotated for whether they are polarity shifters and their shifting scope. The lexicon covers all verbs of WordNet v3.1 (Miller et al., 1990) that are single word or particle verbs. Polarity shifter and scope labels are given for each lemma-synset pair (i.e. each word sense of a lemma).</p> <p>The data is presented in the following forms:</p> <ol> <li>A complete lexicon of all verbal shifters and their shifting scopes.</li> <li>Two auxiliary lists containing simplified information: <ol> <li>A list of all lemmas with shifter labels</li> <li>A list of all word senses with shifter labels</li> </ol> </li> </ol> <p>All files are in CSV (comma-separated value) format.</p> <p><strong>2.1. Complete Lexicon</strong></p> <p>The main lexicon lists all verbal shifters and their shifting scopes. Verbal shifters are modeled as lemma-sense pairs with one or more shifting scopes.</p> <p>The lexicon lists all lemma-sense pairs that are verbal shifters. Any lemma-sense pair not listed is not a verbal shifter. When a lemma-sense pair has more than one possible scope, a separate entry is made for each scope.</p> <ul> <li>File name: <code>shifters.english.verb.sense.csv</code></li> <li>Each line contains a single lemma-sense-scope triple, using the format <code>LEMMA,SYNSET,SCOPE</code>. <ul> <li><code>LEMMA</code>: The lemma representation of the verb in question. Multiword expressions are separated by an underscore (<code>WORD_WORD</code>).</li> <li><code>SYNSET</code>: The numeric identifier of the synset, commonly referred to as <em>offset</em> or <em>database location</em>. It consists of 8 digits, including leading zeroes (e.g. <code>00334568</code>).</li> <li><code>SCOPE</code>: The scope of the shifting: <ul> <li><code>subj</code>: The verbal shifter affects its subject.</li> <li><code>dobj</code>: The verbal shifter affects its direct object.</li> <li><code>pobj_*</code>: The verbal shifter affects objects within a prepositional phrase. The preposition in question is included in the annotation. For example a <em>from</em>-preposition scope receives the label <code>pobj_from</code> and a a <em>for</em>-preposition receives <code>pobj_for</code>.</li> <li><code>comp</code>: The verbal shifter affects a clausal complement, such as infinitive clauses or gerunds.</li> </ul> </li> </ul> </li> </ul> <p><strong>2.2. List of Lemmas</strong></p> <p>List of all verb lemmas and whether they are shifters in at least one of their word senses. Does not provide shifter scope information.</p> <p>Many verbal shifter lemmas only cause shifting in some of their word senses. This list is therefore considerably more coarse-grained than the main lexicon. It is intended as a convenience measure for quick experimentation.</p> <ul> <li>File name: <code>shifters.english.verb.sense.lemmas_only.csv</code></li> <li>Each line follows the format <code>LEMMA,SHIFTER_LABEL</code>. <ul> <li><code>LEMMA</code>: The lemma representation of the verb in question. Multiword expressions are separated by an underscore (<code>WORD_WORD</code>).</li> <li><code>SHIFTER_LABEL</code>: Whether the verb is a polarity shifter (<code>SHIFTER</code>) or a non-shifter (<code>NONSHIFTER</code>).</li> </ul> </li> </ul> <p><strong>2.3. List of Synsets</strong></p> <p>List of all synsets and whether their lemmas are shifters in this specific word sense. Does not provide shifter scope information.</p> <p>Shifting is shared among lemmas of the same word sense. This list, therefore, provides (almost) the same granularity for the shifter label as the main lexicon. However, in a few exceptions, synsets contained words with subtly different senses that did not all cause shifting. These senses are considered shifters in this list, analogous to the generalization in the list of lemmas.</p> <ul> <li>File name: <code>shifters.english.verb.sense.synsets_only.csv</code></li> <li>Each line follows the format <code>SYNSET,SHIFTER_LABEL</code>. <ul> <li><code>SYNSET</code>: The numeric identifier of the synset, commonly referred to as <em>offset</em> or <em>database location</em>. It consists of 8 digits, including leading zeroes (e.g. <code>00334568</code>).</li> <li><code>SHIFTER_LABEL</code>: Whether the verb is a polarity shifter (<code>SHIFTER</code>) or a non-shifter (<code>NONSHIFTER</code>).</li> </ul> </li> </ul> <p><strong>3. German Verbal Shifter Lexicon (Lemma)</strong></p> <p>A lexicon of 2595 German verbs, annotated for whether they are polarity shifters and which polarities they affect. The lexicon is based on the vocabulary of GermaNet (Hamp and Feldweg, 1997). It contains 677 shifters and 1918 non-shifters.</p> <ul> <li>File: <code>shifters.german.verb.lemma.txt</code></li> <li>The lexicon is a comma-separated value (CSV) table.</li> <li>Each line follows the format <code>LEMMA,SHIFTER_LABEL,SOURCE</code>. <ul> <li><code>LEMMA</code>: The lemma representation of the verb in question. Multiword expressions are separated by an underscore (<code>WORD_WORD</code>).</li> <li><code>SHIFTER_LABEL</code>: Whether the verb is a polarity shifter (<code>SHIFTER</code>) or a non-shifter (<code>NONSHIFTER</code>).</li> <li><code>SOURCE</code>: Whether the word was part of the gold standard. (<code>GOLD_STANDARD</code>) or was bootstrapped (<code>BOOTSTRAPPED</code>). In either case the verbs were verified by a human annotator.</li> </ul> </li> </ul> <p><strong>4. Sentiment Verb Phrases</strong></p> <p>A set of verb phrases, annotated for the polarity of the verb phrase and the polarity of a polar noun that it contains. Can be used to evaluate whether a polarity classifier correctly recognizes polarity shifting. The file starts with 400 phrases containing shifter verbs, followed by 2231 phrases containing non-shifter verbs.</p> <ul> <li>File: <code>sentiment_phrases.txt</code></li> <li>Every item consists of: <ul> <li>The sentence from which the VP and the polar noun were extracted.</li> <li>The VP, polar noun and the verb heading the VP.</li> <li>Constituency parse for the VP.</li> <li>Gold labels for VP and polar noun by a human annotator.</li> <li>Predicted labels for VP and polar noun by RNTN tagger (Socher et al., 2013) and <code>LEX_gold</code> approach.</li> <li>Items are separated by a line of asterisks (*)</li> </ul> </li> </ul>
Dataset: Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment
<p>This dataset and these scripts supports the article 'Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment' as published in Cleaner Waste Systems. https://doi.org/10.1016/j.clwas.2024.100154</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we focused on exploring the potential of large-scale composting of rose waste in Kenyan rose cultivation. The objective of this study was to examine the potential of composting rose waste in this large-scale commercial setting with low operational costs, exploring its benefits and challenges.</p> <p>In piles of 4000 kg green waste the evolution of three mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the pesticide residue levels of mature rose waste were assessed. </p>
Graphic Illustration of Kendra Phelp's Talk: A harmonized taxonomic resource is critical for accurately interpreting host-pathogen interactions
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Kendra Phelps at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
SEEtheSkills resources database from the Interregional research on the status of energy skills
<p>This dataset includes a list of resources identified during the interregional research on the status of energy skills, done in the frame of SEEtheSkills project. The comprehensive overview of the information created in the area of Energy Efficiency (EE) and Renewable Energy Systems (RES), goes both wide, by trying to identify as many different examples as possible, and deep, by digging into the examples themselves. The key areas the research focused on: skills defined in national roadmaps; skills developed as part of previous BUS projects; developed training schemes; the number of trained workers and professionals; companies that design and produce EE materials; status of Recognition of Previous Learning (RPL); status of demand for energy skills; level of awareness of energy skills; available certification schemes; legal obligations promoting the use of energy skills and their timelines, predictions for future development of energy skills. The survey covers mainly the five countries participating in the project Slovenia, Spain, Netherlands, Slovakia and North Macedonia, but also beyond their geographical coverage.</p>
Data sources for the groundwater depletion manuscript in Water Resources Research
<p>Here, you can access the source files for the figures (and tables) of the publication (see reference).</p> <p>Basically, you find the model output (WaterGAP 2.2a) for global scaled groundwater storage, total water storage, baseflow, groundwater recharge (diffuse and below surface water bodies) and a table where location of grid cell and belonging continental area (e.g. to convert values into km³) is given. In addition, an Excel-File for the diagram of HPA (Figure 2) is accessible.</p> <p>First part of the file name represents the model variant (IRR100, IRR100_S, IRR70_S, NOUSE_S, for details see the manuscript), then the variable name and unit is given (Total Water Storages [mm], groundwater storage [mm], Qb (baseflow) [mm], Rg (diffuse groundwater recharge) [mm], Rg_swb (groundwater recharge below surface water bodies) [mm]). File format is a zipped netCDF. The table "lat_lon_cont_area.txt" contains the ArcID (internal grid cell number), coordinates and the continental area which is used for WaterGAP calculations.</p> <p>Original data description: https://www.uni-frankfurt.de/49903932/6__GW_depletion</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.