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3,118 results for “resources”
Dirt cheap: An experimental test of controls on resource exchange in an ectomycorrhizal symbiosis
<p>1. To distinguish among hypotheses on the importance of resource-exchange ratios in outcomes of mutualisms, we measured resource (carbon (C), nitrogen (N), and phosphorus (P)) transfers, and their ratios, between Pinus taeda seedlings and two ectomycorrhizal (EM) fungal species, Rhizopogon roseolus and Pisolithus arhizus in a laboratory experiment.</p> <p>2. We evaluated how ambient light affected those resource fluxes and ratios over 3 time periods (10, 20, and 30 weeks), and the consequences for plant and fungal biomass accrual, in environmental chambers.</p> <p>3. Our results suggest that light availability is an important factor driving absolute fluxes of N, P, and C, but not exchange ratios, although its effects vary among EM fungal species. Declines in N:C and P:C exchange ratios over time, as soil nutrient availability likely declined, were consistent with predictions of biological market models. Absolute transfer of P was an important predictor of both plant and fungal biomass, consistent with the excess resource exchange hypothesis, and N transfer to plants was positively associated with fungal biomass.</p> <p>4. Altogether, light effects on resource fluxes indicated mixed support for various theoretical frameworks, while results on biomass accrual better supported the excess resource exchange hypothesis, although among-species variability is in need of further characterization.</p>
Resources for publication "Meteorologically normalised long-term trends of atmospheric ammonia (NH3) in Switzerland/Liechtenstein and the explanatory role of gas-aerosol partitioning"
<p>Resources for publication "Meteorologically normalised long-term trends of atmospheric ammonia (NH3) in Switzerland/Liechtenstein and the explanatory role of gas-aerosol partitioning".</p>
Rapid resource depletion on coral reefs disrupts competitor recognition processes among butterflyfish species
<p>Avoiding costly fights can help conserve energy needed to survive rapid environmental change. Competitor recognition processes help resolve contests without escalating to attack, yet we have limited understanding of how they are affected by resource depletion and potential effects on species coexistence. Using a mass coral mortality event as a natural experiment and 3,770 field observations of butterflyfish encounters, we test how rapid resource depletion could disrupt recognition processes in butterflyfishes. Following resource loss, heterospecifics approached each other more closely before initiating aggression, fewer contests were resolved by signalling, and the energy invested in attacks was greater. In contrast, behaviour towards conspecifics did not change. As predicted by theory, conspecifics approached one another more closely and were more consistent in attack intensity yet, contrary to expectations, resolution of contests via signalling was more common among heterospecifics. Phylogenetic relatedness or body size did not predict these outcomes. Our results suggest that competitor recognition processes for heterospecifics became less accurate after mass coral mortality, which we hypothesise is due to altered resource overlaps following dietary shifts. Our work implies that competitor recognition is common among heterospecifics, and disruption of this system could lead to suboptimal decision-making, exacerbating sublethal impacts of food scarcity.</p>
Data resources for: "How career hubs shape the global corporate elite"
<p>Here you find data resources for the article: "How career hubs shape the global corporate elite" published in Global Networks.</p> <p>The resources contain a merge list of IDs for firms in Boardex and links to WikiData. Data was collected in 2019. Furthermore there is R code for the variant of career networks used in the article.</p>
Lack of pollinators selects for increased selfing, restricted gene flow and resource allocation in the rare Mediterranean sage Salvia brachyodon
<p>Salvia brachyodon (Lamiaceae): raw data on flower morphometry, nectar concentration and volume and seed weight according various pollination treatments.</p>
Data set for paper "Spatial Mobility Capital: A Valuable Resource for the Social Mobility of Border-Crossing Migrant Entrepreneurs?"
<p>Data set for paper "Spatial Mobility Capital: A Valuable Resource for the Social Mobility of Border-Crossing Migrant Entrepreneurs?"</p>
WaivOps HH-LFBB: Open Audio Resources for Machine Learning in Music
<p><strong>WaivOps HH-LFBB Dataset</strong></p> <p>HH-LFBB is an open audio dataset composed of a series of drum recordings in the style of lofi hip-hop music. The dataset contains 3332 audio loops recorded in uncompressed stereo WAV format, produced with custom drum samples and MIDI-programmed rhythms at various tempo rates.</p> <p><strong>Dataset</strong></p> <p>The primary objective of this dataset is to provide accessible content for machine learning applications in music and audio research. Some potential use cases for this dataset include tempo detection and classification, drum rhythm analysis, audio-to-MIDI conversion, source separation, automated mixing, music information retrieval, AI music generation, sound design, and signal processing.</p> <p>Specifications</p> <ul> <li>3332 audio loops (19.3 hours)</li> <li>24-bit WAV format</li> <li>BPM labeled</li> <li>Tempo range: 60-96bpm</li> <li>Expressive drum swings</li> <li>Lofi and boom bap style rhythms</li> </ul> <p><strong>License</strong></p> <p>This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.</p> <p>The HH-LFBB dataset is licensed under Creative Commons Attribution 4.0 International <a href="https://creativecommons.org/licenses/by/4.0/">(CC BY 4.0)</a>.</p> <p><strong>Additional Info</strong></p> <p>For audio examples or more information about this dataset, please refer to the <a href="https://github.com/patchbanks/WaivOps-HH-LFBB">GitHub repository</a>.</p>
Dataset for "Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources"
<p>A qualitative dataset for the article: Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources</p> <p>We collected this data in Phase 2 of the work described in the article, to inform development of educational resources (<em>Be Smart About Your Health</em>) to support teaching critical thinking about health claims and making informed health choices for use in secondary schools, based on a set of Informed Health Choices Key Concepts. </p> <p>Data collection methods: individual and group interviews, observation of classroom pilots, in Kenya, Rwanda, and Uganda, and via email from an international advisory group. Timeframe for data collection and analysis: 2020-2022</p> <p>This dataset is a part of the research project: <em>Enabling sustainable public engagement in improving health and health equity, </em>2019-2024. Funded by GLOBVAC programme, Research Council of Norway. </p> <p> </p>
Resource availability and capacity to implement multi-stranded cholera interventions in the north-east region of Nigeria
<p>Limited healthcare facility (HCF) resources and capacity to implement multi-stranded cholera interventions ('water, sanitation, and hygiene (WASH)’, ‘surveillance’, ‘case management’, and ‘community engagement’) can hinder the actualisation of the global strategic roadmap goals for cholera control, especially in settings made fragile by armed conflicts, such as the north-east region of Nigeria. Therefore, we aimed to assess HCF resource availability and capacity to implement these cholera interventions in Adamawa and Bauchi States in Nigeria, as well as assess their coordination in both states and Abuja, where national coordination of cholera is based.<br> We conducted a cross-sectional survey using a face-to-face structured questionnaire to collect data on multi-stranded cholera interventions and their respective indicators in HCFs. We generated scores to describe the resource availability of each cholera intervention and categorised them as: 0-50 (low), 51-70 (moderate), 71-90 (high), and over 90 (excellent). Further, we defined an HCF with a high capacity to implement a cholera intervention as one with a score equal to or above the average intervention score. </p>
Data for: Herbivores disrupt the flow of food resources to termites in dryland ecosystems
<p><span>Irruption of herbivore populations due to the extirpation of predators has led to dramatic changes in ecosystem functioning worldwide. Herbivores compete with other species for their primary source of nutrition, plant biomass. Such competition is typically considered to occur between species in closely related clades and functional groups but could also occur with detritivores that consume senescent plant biomass. Here, we test predictions that herbivores' indirect impacts on dead vegetation increase with primary productivity and extend to termites which feed on senescent vegetation. We compared dead vegetation cover and termite activity in herbivore exclosures and associated grazed plots at 3 locations situated along a rainfall gradient in arid Australia where kangaroo populations have irrupted. Dead vegetation cover and termite activity increased with rainfall in ungrazed plots but showed a negligible response to rainfall in grazed plots. Our results suggest that grazing can disrupt the flow of energy to detritivores and decouple the relationship between termite activity and primary productivity. Such disruption could have far-reaching impacts on arid ecosystems because many organisms sit within "brown food webs" that are sustained by energy derived from decomposition of senescent plant-tissues. </span></p>
U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)
<p><strong>* New in Version 2.1 *</strong></p> <ul> <li> <p>All residential measure savings shapes data (<strong>Latest_Res_Shapes.zip</strong> and residential measures in <strong>Latest_BM_Shapes.zip</strong>) were updated to correct post-processing errors present in version 2.</p> </li> <li> <p>The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files <strong>Latest_Res_Baselines.zip</strong> and <strong>Latest_Com_Baselines.zip</strong>).</p> </li> <li> <p>Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure).</p> </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> <p><strong>* New in Version 2 *</strong></p> <p>All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements:</p> <ul> <li> <p>Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0.</p> </li> <li> <p>Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>.</p> </li> <li> <p>Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes).</p> </li> </ul> <p>Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests:</p> <p><strong>Latest_BM_Shapes.zip</strong> includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>.</p> <p><strong>Latest_Res_Shapes.zip</strong> includes all residential savings shape CSVs.</p> <p><strong>Latest_Com_Shapes.zip</strong> includes all commercial savings shape CSVs.</p> <p>Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data).</p> <p><br> <strong>Summary of Original Data Files</strong></p> <p>These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020.<strong> </strong>Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.</p> <p>The four ZIP files that make up this data record are interpreted as follows:</p> <p><strong>Measure_Data.zip: </strong>Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>.</p> <p><strong>Results_Data.zip: </strong>Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s".</p> <p><strong>Source_Code.zip: </strong>Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs.</p> <p><strong>Supporting_Data.zip: </strong>Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json". </p>
Data from: Work-Life Conflict Among Higher Education Institution Workers' During COVID-19: A Demands-Resources Approach
<p>Dataset from: Work-Life Conflict Among Higher Education Institution Workers' During COVID-19: A Demands-Resources Approach</p>
Maverick Variant Pathogenicity Data Resources
<p>MAVERICK is a Mendelian Approach to Variant Effect pRedICtion built in Keras. It classifies protein-altering variants as either dominant disease-causing, recessive disease-causing, or benign. Here, we provide the pre-computed scores for all missense and nonsense SNVs in Gencode Basic V33 on GRCh37 and lifted over to GRCh38 as well as the datasets on which MAVERICK was trained and primarily evaluated. </p>
Data from: Landscape variation in defense traits along gradients of multiple resources in a tropical savanna plant
<p><span>Many plant species are widely distributed and consequently are exposed to multiple abiotic factors and diverse herbivores, each of which may distinctly affect the magnitude of different defense traits.</span><span> </span><span>Alternative theories for optimal allocation to plant defense traits predict both positive and negative associations between magnitude of defense and resource availability. These predictions may apply even within species. </span><span>This </span><span>suggests potential for a single species' patterns of association of defense traits and resources to vary with both the type of defense and identity of resource, but relatively few studies have explored intraspecific variation in multiple defense traits along several resource gradients simultaneously. </span><span>In order to address this gap, especially in an ecosystem dominated by large mammalian herbivores, we assessed relationships between multiple resources (rainfall, soil N, and soil P) and plant defense traits (prickle density, phenolics, and lignin content) using a widely distributed tropical savanna herb, <em>Solanum</em> <em>incanum</em>, growing in naturally occurring resource gradients within the Serengeti National Park. </span>We found substantial intraspecific variation in all three defense traits across sites (n =43). Variation in prickle density was positively associated with rainfall and soil P, but not soil N. In contrast to prickle density, phenolics and lignin were uncorrelated with all three resource gradients. This independent association of soil P with a carbon-based defense, prickle density, suggests potential for resources that are not components of defenses to influence allocation to defense traits. Such influence may reflect association between resource and herbivore abundance and/or preference. These varied patterns in resource-defense associations further emphasize the tremendous variation in anti-herbivore traits which may be influenced by different plant resources and highlight the need to consider multiple resource gradients in understanding evolution of plant traits.</p>
The Ecology-Culture Dataset: a resource for investigating cultural variation (Abbreviated)
<p>Scholars interested in cultural diversity have long suggested that similarities and differences across human populations might be understood, at least in part, as stemming from differences in the social and physical ecologies individuals inhabit. Here, we describe the EcoCultural Dataset (ECD), the most comprehensive compilation to date of country-level ecological and cultural variables around the globe. ECD covers 220 countries, 9 ecological variables operationalized by 11 statistical metrics (including measures of variability and predictability), and 72 cultural variables (including values, personality traits, fundamental social motives, subjective well-being, tightness-looseness, indices of corruption, social capital, and gender inequality). This rich dataset can be used to identify novel relationships between ecological and cultural variables, to assess the overall relationship between ecology and culture, to explore the consequences of interactions between different ecological variables, and to construct new indices of cultural distance.</p> <p>Note: The full dataset with 72 cultural variables is available on OSF (<a href="https://osf.io/45am7/">https://osf.io/45am7/</a>). This is the abbreviated version (66 cultural variables). </p>
Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales
<p class="MsoNormal"><span>Resource quantity controls biodiversity across spatial scales, however the importance of resource quality to cross-scale patterns in species richness has seldom been explored. We evaluated the relationship between stream basal resource quantity (periphyton chlorophyll-<em>a</em>) and invertebrate richness and compared this to the relationship of resource quality (periphyton stoichiometry) and richness at local and regional scales across 27 North American streams. At the local scale, invertebrate richness peaked at intermediate levels of chlorophyll-<em>a</em>, but had a shallow negative relationship with periphyton C:P and N:P. However, at the regional scale richness had a strong negative relationship with both chlorophyll-<em>a</em> and periphyton C:P and N:P. The divergent effects of periphyton chl-<em>a</em> and stoichiometry on invertebrate richness suggest that basal resource quantity limits diversity more than resource quality, consistent with patterns of eutrophication. Collectively, we demonstrate that resource quantity and quality play important, yet differing roles in shaping freshwater biodiversity across spatial scale.</span></p>
The use of lexicographic resources in Croatian primary and secondary education - Survey Data
<p>The dataset contains the data collected in the survey on the use of dictionaries and other lexicographic resources in Croatian primary and secondary education, which was conducted from 1 February to 17 February 2023.</p>
Data from: How do resource distribution and taxonomy affect the use of dual foraging in seabirds?: A review
<p>In many seabird species, parents feeding young switch between short and long foraging excursions in a strategy known as "dual foraging". To investigate whether habitat quality near breeding colonies drives the use of dual foraging, we conducted a systematic review of the seabird literature, compiling the results of 103 studies which identified dual-foraging in 50 species across nine families from all six seabird orders. We estimated the mean distance from the colony of each species' short and long foraging trips and obtained remote-sensed data on chlorophyll concentrations within the radius of both short and long trips around each colony. We then assessed, for each seabird family, the relationship between the use of dual foraging strategies and the difference in the quality of foraging locations between short- and long-distance foraging trips. We found that the probability of dual foraging grew with increasing difference in the quality of foraging locations available during short- and long-distance trips. We also found that when controlling for differences in habitat quality, albatrosses and penguins were less likely to use dual foraging than Procellariidae, which in turn were less likely to use dual foraging than Sulids. This study helps clarify how environmental conditions and taxon-specific characteristics influence seabird foraging behaviour. Keywords: seabirds, dual foraging, habitat quality, central-place foraging, interspecific differences.</p>
Resources for the article "Investigating the role of educational robotics in formal mathematics education"
<p>This repository contains the material required to reproduce the study looking to investigate the role of educational robotics in formal mathematics education for 15 year old students in the French speaking region of Switzerland. This includes :</p> <ul> <li> <p>Pedagogical content in the form of both teacher and student resources</p> </li> <li> <p>Data collection ressources (surveys and tests)</p> </li> </ul> <p>If you use any of the resources provided in this repository, please cite the following</p> <p>• The Zenodo repository, DOI: 10.5281/zenodo.4649842</p> <p>• The corresponding article : Brender, J., El-Hamamsy, L., Bruno, B., Chessel-Lazzarotto, F., Zufferey, J.D., Mondada, F. (2021). Investigating the Role of Educational Robotics in Formal Mathematics Education: The Case of Geometry for 15-Year-Old Students. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds) Technology-Enhanced Learning for a Free, Safe, and Sustainable World. EC-TEL 2021. Lecture Notes in Computer Science(), vol 12884. Springer, Cham. https://doi.org/10.1007/978-3-030-86436-1_6</p> <p>• Licence : CC-BY</p>
Griots Interviews, Bambara Language WAV, 30 hours, Recorded 2022, Cultural and ASR Training Resource
<p><strong>Source material to this project:</strong></p> <ul> <li> <p>Addition to 200,000 lines Bambara-French clean synchronized corpus</p> </li> <li> <p>Co-project with Google, recorded 30 hours video interviews with Griots</p> </li> <li> <p>30 hours manually transcribed and translated to French</p> </li> <li> <p>10 hours used in training ASR system and MT transformer</p> </li> <li> <p>100% Open Sourced</p> </li> <li> <p>Cultural/Technical Exhibition to be hosted online and in the National Museum of Mali</p> </li> <li> <p>Record, preserve, and share Malian culture with the world</p> </li> <li> <p>Contribute to the science of low-resource language NLP</p> </li> <li> <p>Reinforce the development of written Bambara</p> </li> <li> <p>Enable Bambara to reach status as a “first-class internet language”</p> </li> </ul> <p>Corresponding transcribed data can be found at the following <a href="https://github.com/robotsmali-ai/jeli-asr">Github repository</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.