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Data for: Functional trait variability supports the use of mean trait values and identifies tradeoffs for marine macroalgae
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Dataset from: In-situ bidirectional human-robot value alignment
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A parallel accumulator model accounts for decision randomness when deciding on risky prospects with different expected value
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Milkweed trait values associated with aridity gradients and drought-induced changes in hostplant quality
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Five Points Plant Isotopic Values for Carbon and Nitrogen
To quantify foraging strategies in the small mammal community of the Sevilleta National Wildlife Refuge, plant samples were collected at green-up (generally March for forbs and July for grasses), and when going to seed (generally May for forbs and October for grasses). Plants were analyzed for C:N ratio, delta15N and delta13C isotopic values and for carbon and nitrogen percent content by mass in both leaf and seed. These isotopic values can be compared to mouse plasma values to determine forage width (Carbon) and forage breadth (Nitrogen). Please see Sevilleta Dataset 313 on small mammal plasma to quantify resource preferences within and between small mammal communities.
Five Points Small Mammal Blood Plasma Isotopic Values for Carbon, Nitrogen and Hydrogen
There has been little comprehensive research undertaken to quantify resource use by small mammal communities in a nutrient limited, highly stochastic ecosystem. The most abundant small mammals in this ecosystem are Heteromyids, food-caching granivores, and Cricetids, omnivores that must utilize on board fat stores as energy reserves. Heteromyid populations co-vary with primary production whereas the cricetids can forage at multiple trophic levels reducing their dependence on primary productivity. Using isotopic values for Carbon, delta13C (a ratio of 13C to 12C), of primary producers consistent within a photosynthetic pathway, C3 = -26.6 +/- 1.8Permille and C4, = -14.4 +/- 0.8Permille we can track mouse dietary assimilation of forage by plant functional type. Nitrogen isotopic values 15N (a ratio of 15N to 14N) fluctuate constantly, reflecting the landscape of primary production. Therefore, tracking nitrogen values in small mammal plasma provides a landscape level tool for studying diet by trophic level and nutritional value.
Spatial and temporal variation in the value of solar power across United States electricity markets
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States Electricity Markets". Renewable & Sustainable Energy Reviews 2019. <a href="https://doi.org/10.1016/j.rser.2019.109594">https://doi.org/10.1016/j.rser.2019.109594</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Many of the scripts, data, and descriptive text in this repository are shared with the following publication:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles". Applied Energy 2019, 256, 113734. <a href="https://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm/*.py : Python module with functions for modeling PV generation and calculating PV energy revenue, capacity value, and emissions offset.</li> <li>notebooks/*.ipynb : Jupyter notebooks, including: <ul> <li>pvvm-vos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, assign nodes to capacity zones, download NSRDB input data, and reproduce some figures in [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the use of the PV generation model and a sensitivity analysis of PV generator assumptions</li> <li>pvvm-example-plots.ipynb: Example scripts demonstrating different plotting functions</li> <li>validate-pv-monthly-eia.ipynb: Scripts and plots for comparing modeled PV generation with monthly generation reported in EIA forms 860 and 923, as discussed in SI Note 3 of [1]</li> <li>validate-pv-hourly-pvdaq.ipynb: Scripts and plots for comparing modeled PV generation with hourly generation reported in NREL PVDAQ database, as discussed in SI Note 3 of [1]</li> <li>pvvm-energyvalue.ipynb: Scripts for calculating the wholesale energy market revenues of PV and reproducing some figures in [1]</li> <li>pvvm-capacityvalue.ipynb: Scripts for calculating the capacity credit and capacity revenues of PV and reproducing some figures in [1]</li> <li>pvvm-emissionsvalue.ipynb: Scripts for calculating the emissions offset of PV and reproducing some figures in [1]</li> <li>pvvm-breakeven.ipynb: Scripts for calculating the breakeven upfront cost and carbon price for PV and reproducing some figures in [1]</li> </ul> </li> <li>html/*.html : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data/lmp/*.gz : Day-ahead nodal locational marginal prices (LMPs) and marginal costs of energy (MCE), congestion (MCC), and losses (MCL) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will be linked here with the complete dataset.</li> </ul> </li> <li>results/*.csv.gz : Simulation results associated with [1], including modeled energy revenue, capacity credit and revenue, emissions offsets, and breakeven costs for PV systems at all LMP nodes</li> </ul> <p><strong>Data notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="https://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes on LMP data: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-vos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Usage notes</strong></p> <ul> <li>Code is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file and at the top of each *.py file. </li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_vos">https://github.com/patrickbrown4/pvvm_vos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, you will need to modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing (latitude, longitude) floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, you will need to request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul> <p> </p>
ASR values of ferritic stainless steels
<p>Additional data to the article "CORROSION BEHAVIOUR OF NITRIDED FERRITIC STAINLESS STEELS FOR USE IN SOLID OXIDE FUEL CELL DEVICES" comparing the results of nitrided and non nitrided steel substrate.</p>
Supplementary materials for the paper "Benefits of Stabilization versus Rollback in Self-Stabilizing Graph-Based Applications on Eventually Consistent Key-Value Stores"
<p>Supplementary materials for the paper "Benefits of Stabilization versus Rollback in Self-Stabilizing Graph-Based Applications on Eventually Consistent Key-Value Stores"</p> <p>The materials include experimental results and source code.</p>
2018 CODATA values converted to the machine-readable D-SI XML format
<p>This data set provides 2018 CODATA values for fundamental physical constants using the XML scheme based on the Digital-SI (D-SI) data model that enables a machine-readable transmission of metrological data in digital applications.</p>
Signing up to Open Science: Open Peer Review and Aligning Core Values
<p><strong>Episode Summary:</strong></p> <p>In this episode we are talking to Dr Guillaume Filion from the Center for Genomic Regulation (CRG), Barcelona about his decision to put his name on all his peer reviews, why he feels this makes him more accountable, and what choices researchers need to make about whether their values align with those who they will work with. </p> <p><strong>Resources and Links:</strong></p> <ul> <li><a href="https://www.orion-openscience.eu/node/201">Factsheets</a></li> <li><a href="https://www.crg.eu/en/programmes-groups/filion-lab">Dr Guillaume Filion</a> <ul> <li><a href="https://twitter.com/thegrandlocus">Twitter Page</a></li> </ul> </li> <li><a href="http://blog.thegrandlocus.com/2018/09/on-open-peer-review">Original Blog Post by Dr Guillaume Filion</a></li> <li><a href="https://www.fosteropenscience.eu/learning/open-peer-review/#/id/5a17e150c2af651d1e3b1bce">FOSTER+ Information on Open Peer Review </a></li> </ul> <p><strong>Episode Quotes:</strong></p> <p>“I had the feeling I would do a better job as a reviewer, if I put my name on it, if I put my name on the line then it would force me not to do a bad job”</p>
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Social Value items validation through stakeholder consultation
<p>Stakeholder validation and integration of the list of social impact categories, subcategories and indicators identified in the D6.1</p> <p>This dataset has been used for these two publications:</p> <p>Imbert, E., & Falcone, P. M. (2020). Social Assessment. Transition Towards a Sustainable Biobased Economy, 64, 166</p> <p>Falcone, P. M., González García, S., Imbert, E., Lijó, L., Moreira, M. T., Tani, A., ... & Morone, P. (2019). Transitioning towards the bio‐economy: Assessing the social dimension through a stakeholder lens. Corporate Social Responsibility and Environmental Management, 26(5), 1135-1153.</p>
Comparison table of betweenness centrality and electrical grid centrality values for Ural main power lines
<p>Comparison table of betweenness centrality and electrical grid centrality values for Ural united power system (lines with voltage 220-500 kV), calculated with ArcGIS and Networkx tools.</p>
3D spatial distribution of the seismic Gutenberg-Richter parameters (a and b values) in Long Valley Caldera
<p>3D distribution of the <em>a</em> and <em>b</em> parameters of the Gutenberg-Richter (GR) law. The analysis is presented in Gola G., Barone A., Castaldo R., Chiodini G., D'Auria L., Garcia-Hernandez R., Pepe S., Solaro G. & Tizzani P. "A novel multidisciplinary approach for the thermo-rheological study of volcanic areas: The case study of Long Valley Caldera", which has been submitted for possible publication in Journal of Geophysical Research - Solid Earth. For this purpose, we used a dataset consisting of more than 220,000 earthquakes from the NCEDC catalogue (NCEDC, 2014), spanning the interval 1978-2017 and located in the surroundings of Long Valley Caldera. We mapped the <em>a</em> and <em>b</em> values for a volume of 50×50×30 km<sup>3</sup>, over a grid of points having a regular spacing of 0.5 km.</p> <p>Coordinates: North America NAD27 UTM Zone 11N<br> Easting: 310000 - 350000 m.; Northing: 4150000 - 4185000 m</p> <p> </p>
Data from: Study of regional differences in GC content values in chromosomes of the guppy and related fish species
<p><span><span><span><span><span><span><span><span><span><span><span>Genetic and physical mapping of the guppy (<i>P. reticulata</i>) have shown that recombination patterns differ greatly between males and females. Crossover events occur evenly across the chromosomes in females, but in male meiosis they are restricted to the tip furthest from the centromere of each chromosome, creating very high recombination rates per megabase, similar to the high rates in of pseudo-autosomal regions (PARs) of mammalian sex chromosomes. We here used the intronic GC content to indirectly infer the recombination patterns on guppy chromosomes. This is based on evidence that recombination is associated with GC-biased gene conversion, so that genome regions with high recombination rates should be detectable by high GC content. We used intron sequences and 3<sup>rd</sup> positions of codons, in order to make comparisons between sequences that are matched, as far as possible, with respect to selective constraints. Both these types of sites are likely to be under weak selection. Almost all guppy chromosomes, including the sex chromosome (LG12), prove to have very high GC values near their assembly ends, suggesting high recombination rates due to strong crossover localisation in male meiosis. Our test does not suggest that the guppy XY pair has stronger crossover localisation than the autosomes, or than the homologous chromosome in a closely related fish, the platyfish (<i>Xiphophorus maculatus</i>). We therefore conclude that the guppy XY pair has not recently undergone an evolutionary change to a different recombination pattern, or reduced its crossover rate, but that the guppy evolved Y-linkage due to acquiring a male-determining factor that also conferred the male crossover pattern. The results also identify the centromere ends of guppy chromosomes, which were not determined in the guppy genome assembly. </span></span></span></span></span></span></span></span></span></span></span></p>
The asymptotic behavior of bootstrap support values in molecular phylogenetics
<p>The phylogenetic bootstrap is the most commonly used method for assessing statistical confidence in estimated phylogenies by non-Bayesian methods such as maximum parsimony and maximum likelihood (ML). It is observed that bootstrap support tends to be high in large genomic datasets whether or not the inferred trees and clades are correct. Here we study the asymptotic behavior of bootstrap support for the ML tree in large datasets when the competing phylogenetic trees are equally right or equally wrong. We consider phylogenetic reconstruction as a problem of statistical model selection when the compared models are nonnested and misspecified. The bootstrap is found to have qualitatively different dynamics from Bayesian inference, and does not exhibit the polarized behavior of posterior model probabilities, consistent with the empirical observation that the bootstrap is more conservative than Bayesian probabilities. Nevertheless bootstrap support similarly shows fluctuations among large datasets, with no convergence to a point value, when the compared models are equally right or equally wrong. Thus in large datasets strong support for wrong trees or models is likely to occur. Our analysis provides a partial explanation for the high bootstrap support values for incorrect clades observed in empirical data analysis.</p>
Comparative Study of the Restorative effects of Forest and Urban Videos during Covid-19 Lockdown: Intrinsic and Benchmark Value
<p>Article: "Comparative Study of the Restorative effects of Forest and Urban Videos during Covid-19 Lockdown: Intrinsic and Benchmark Value"</p> <p>Video S1: forest environment </p> <p>Video S2: urban environment</p>
Finland COVID-19 R-value estimate March - August
<p>This dataset contains the time-dependent effective reproduction number R<sub>t</sub> of the COVID-19 epidemic in Finland for the period Feb 27th 2020 - August 14th 2020. The reproduction number was estimated using the daily new coronavirus cases in <a href="https://thl.fi/en/web/thlfi-en/statistics/statistical-databases/open-data/confirmed-corona-cases-in-finland-covid-19-">the infectious diseases registry of Finland</a> with an unscented RTS smoother and the SEIR epidemic model.</p> <p>The SEIR model was defined as<br> <span class="math-tex">\(S(t+1) = S(t) - \frac{R_t(t)}{T_i} I(t) \frac{S(t)}{N} + \omega_{S}\\ E(t+1) = E(t) + \frac{R_t(t)}{T_i} I(t) \frac{S(t)}{N} - \frac{E(t)}{T_e} + \omega_{E}\\ I(t+1) = I(t) + \frac{E(t)}{T_e}-\frac{I(t)}{T_i} + \omega_{I} \\ R_t(t+1) = R_t(t) + \omega_{R_t}\)</span></p> <p>with S, E, and I being the susceptible, exposed, and infective populations, R<sub>t</sub> the time-dependent effective reproduction number, and ωs are normally distributed random numbers with zero mean and small (1e-8) variance except for ω<sub>Rt</sub> whose variance was set to 0.00025. The exposed state duration was set to T<sub>e</sub>=3 days and infective state duration T<sub>i</sub>=5 days.</p> <p>The measurement model (from the SEIR state to observed daily new cases) was defined as<br> <span class="math-tex">\(P_\text{new cases}(k) \propto \mathcal{N}\left[ \alpha \frac{R_t(t-5 days)}{T_i}I(t-5\text{days}) \frac{S(t-5\text{days})}{N}, \sigma_\text{new cases}^2(t) \right]\)</span>, i.e., via a time-delayed gaussian process whose variance was computed by the observed daily new cases with a 7-day rolling mean. Here the detection rate α is set to 10%, but has no significant affect on the computed R-values.</p> <p> </p> <p>The data is a CSV-format file with the columns</p> <ul> <li>date = ISO 8601 date string</li> <li>Rt = maximum a posteriori estimate of R<sub>t</sub></li> <li>Rt_lower50 = lower limit of the 50% credible interval for R<sub>t</sub></li> <li>Rt_upper50 = upper limit of the 50% credible interval for R<sub>t</sub></li> <li>Rt_lower90 = lower limit of the 90% credible interval for R<sub>t</sub></li> <li>Rt_upper90 = upper limit of the 90% credible interval for R<sub>t</sub></li> </ul>
Data for: High-resolution land value maps reveal underestimation of conservation costs in the United States
<p>The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I develop the first high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimated conservation costs, especially at the expensive tail of the distribution. This might have led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges, and can assist with the targeting of public ecosystem service investments.</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.