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European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2020/2021
<p>All tables produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2021:</p> <p>- <em>Campylobacter</em></p> <p><em>- E. coli</em></p> <p>- MRSA</p> <p>- <em>Salmonella</em></p> <p>- ESBL</p> <p>Annexes A to F are also included.</p>
Indicators and metrics in local climate adaptation plans
<p>This dataset gathers information related to indicators and metrics collected from 11 local climate adaptation plans in worldwide cities - Athens (Greece), Auckland (USA), Barcelona (Spain), Glasgow (UK), Istanbul (Turkey), Lima (Peru), Los Angeles (USA), Montreal (Canada), Nagoya (Japan), New York City (USA), Portland (USA), Tokyo (Japan) and Vancouver (Canada). The dataset describes the use and characteristics of adaptation indicators and metrics across climate adaptation-related planning documents. Although the sample is relatively small, it is a reflection of the global embryonic stage of adaptation metrics practice.</p> <p>The results of the analysis of this database have been published in:</p> <p>Goonesekera, S. M., & Olazabal, M. (2022). Climate adaptation indicators and metrics: State of local policy practice. <em>Ecological Indicators</em>, <em>145</em>, 109657. <a href="https://doi.org/10.1016/j.ecolind.2022.109657">https://doi.org/10.1016/j.ecolind.2022.109657</a> (OPEN ACCESS)</p>
ReCon Soil Project: Dataset for Identification of Soil Health Indicators in Construction
<p>This dataset represents experimental results from Work Package 4 of the ReCon Soil Project: https://www.plymouth.ac.uk/research/institutes/sustainable-earth/the-recon-soil-project </p> <p>Soil health indicators for maintenance in a construction context, are less about soil fertility and more about maintaining those indicators identified, to minimise loss of carbon (C) and nitrogen (N), maintain soil microbial activity and to ensure stockpiled soils are maintained to a point where they can be reused effectively in the future. To this end, an experiment was designed in order to identify and monitor key soil health indicators for construction, and to investigate methods to mitigate against carbon losses in stockpiled soils.</p> <p>The main questions that were addressed were:</p> <p>1. Will sowing grass and other plant species in stockpiled soils reduce losses of C and N from the soil? </p> <p>2. What effect does soil stockpiling have on microbial communities? </p> <p>3. What effect does wood chip application on stockpiled soils have on C and N stocks and microbial communities? </p> <p>4. How do rapid in-field methods for carbon and microbial analysis compare with established lab methods?</p> <p>The experiment was established at the end of August 2022. Any vegetation was cleared from the study site by using a digger to scrape away the top 5cm of soil. Diggers were used to take soil from an area adjacent to the study site which had been undisturbed for 3-4 years, vegetation was cleared from this area and the topsoil was formed into 12 stockpiles (6m length x 4m width x 2m height). The stockpiles were then randomly assigned one of three treatments; amenity grass seed mix, herbal ley seed mix or control. For the seeded treatments, each stockpile was sown by hand to an application rate of 70g/m<sup>2</sup>. After seeds were sown, wood chip was applied by hand to half of each stockpile (randomly assigned), to a depth of covering of approximately 2cm. This resulted in 4 replicates of each combination producing 24 sample plots.</p> <p>Soil samples were collected using a soil sampling auger to collect samples at 0-30 cm and 90-100cm. One sample was taken from each replicate at both depths, these were taken at baseline (T<sub>0</sub>), and intervals of 1 week (T<sub>1</sub>), 2 weeks (T<sub>2</sub>), 4 weeks (T<sub>4</sub>) and every 4 weeks after that. The soil cores were sealed in sterile plastic bags and transported to the laboratory for processing.</p> <p>At each time point, soil was analysed in the laboratory at Eden Project Learning for the following parameters: non-purgeable organic carbon (NPOC), total nitrogen (TN), soil organic matter (SOM), gravimetric water content, pH, and microbial activity using a fluorescein diacetate assay. All remaining bagged soil samples were then transferred to a freezer and stored at -18°C. These were later defrosted and analysed for total organic carbon (TOC), available iron, ammonia, nitrate and nitrite at a subset of time points. Using in-field equipment, soils were analysed for CO<sub>2</sub> emissions at every time point, fungi:bacteria ratio and for soil carbon every 4 weeks. Samples for soil eDNA were taken from each replicate at T<sub>0</sub>, T<sub>8</sub> and T<sub>20</sub>.</p>
Infrared Transmission Spectra and Complex Refractive Indices of Surface Soils from Global Dust Entrainment Regions
<p>Spectral data for the paper “Variations in Infrared Complex Refractive Index Spectra of Surface Soils from Global Dust Entrainment Regions” submitted to the journal of <em>Atmosphere</em> Manuscript ID: atmosphere-2291559.</p> <p>Longwave infrared continuum removed transmission spectra, real (<em>n</em>) and imaginary (<em>k</em>) indices of refraction as well as subtractive Kramers-Kronig (SKK) MATLAB code presented in that paper are included in this Zenodo repository.</p> <p>The continuum removed transmission spectral data are stored in an Excel file format in columns and the first column is the wavelengths (µm). </p> <p>Real (<em>n</em>) and imaginary (<em>k</em>) indices of refraction for each sample are stored in a separate file where the first the column is the wavelengths (µm), and the second and third are <em>k</em> and <em>n</em> respectively.</p> <p> </p> <p>LWIR transmission: LWIR transmission was measured in the wavelength range between ~ 2.5 and 25 µm using a benchtop Nicolet 380 Fourier Transform Infrared (FTIR) spectrometer. First, we made soil-KBr pellets using ~ 0.5 mg of soil and ~ 200 mg of KBr blended using a clean mortar and pestle for 2 to 3 minutes to ensure uniform dispersion of mineral particles in the matrix. Pellet production was performed with the means of an evacuable KBr Die Kit and a CrushIR Digital Hydraulic Press from PIKE Technologies (Madison, WI, USA). The mixture was first transferred to the Die Kit which was then pressed under vacuum for about 4 to 5 minutes at a pressure of ~ 10 t cm<sup>-2</sup>, forming a hard disk 13 mm in diameter. Due to the hygroscopic nature of KBr, we pulled a vacuum on the pellet die for approximately 3 to 4 minutes prior to compression, and we immediately placed the pellets in a desiccant box, and then measured transmission within one or two hours of pellet creation.</p> <p>The pellet was attached to a self-adhesive sampling card and placed into the transmission holder. The resulting transmission spectrum for the sample is recorded with the dust-KBr mixture measurement being ratioed to that for an empty chamber, or blank reference. To avoid contamination of transmission spectra with ambient gases, the instrument was initially purged with dry air for at least 5 minutes, prior to each transmission collection. To increase the quality of the spectra and improve the SNR, the numbers of scans averaged were 200 for the blank reference and 100 for the samples.</p> <p>We ratioed transmission spectra to a blank KBr spectrum and then removed the continuum. The continuum removal (CR) transmission spectrum was employed to derive imaginary index of refraction (<em>k</em>) using Beer-Bouguer-Lambert law and <em>k</em> = <em>β<sub>a</sub></em>λ/4π. Real index of refraction (<em>n</em>) spectrum was consequently derived from the <em>k</em> spectrum with a subtractive Kramers-Kronig (SKK) technique. </p> <p>The spectral range of 4–25 μm was chosen to present the transmission spectra as well as real (<em>n</em>) and imaginary (<em>k</em>) indices of refraction since absorption in the region from 2.5 to 3.5 μm is mainly due to water and is not necessarily diagnostic.</p> <p>Here, we define any acronyms that may be present in the names of uploaded Excel files or their contents.</p> <p>*LWIR = Longwave infrared</p> <p>*T = Transmission</p> <p>*CR = Continuum Removed</p>
Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)
<p><strong>1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong></p> <p>CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f,</p> <p>CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f,</p> <p>CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude:</strong></p> <p>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"):</strong></p> <p>Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt</p> <p>GRDC_number_WaterGAP_ArcID_220_stations.txt</p> <p><strong>4) SDHIs for four GRDC gauging stations:</strong></p> <p>time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt</p> <p><strong>5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p> </p>
Data for: Ornaments indicate parasite load only if they are dynamic, or parasites are contagious
<p><span>Choosing to mate with an infected partner has several potential fitness costs, including disease transmission and infection-induced reductions in fecundity and parental care. By instead choosing a mate with no, or few, parasites, animals avoid these costs and may also obtain resistance genes for offspring. Within a population, then, the quality of sexually selected ornaments on which mate choice is based should correlate negatively with the number of parasites with which a host is infected ('parasite load'). However, the hundreds of tests of this prediction yield positive, negative, or no correlation between parasite load and ornament quality. Here, we use phylogenetically controlled meta-analysis of 424 correlations from 142 studies on a wide range of host and parasite taxa to evaluate explanations for this ambiguity. We found that ornament quality is weakly negatively correlated with parasite load overall, but the relationship is more strongly negative among ornaments that can dynamically change in quality, such as behavioral displays and skin pigmentation, and thus can accurately reflect current parasite load. The relationship was also more strongly negative among parasites that can transmit during sex. Thus, the direct benefit of avoiding parasite transmission may be a key driver of parasite-mediated sexual selection. No other moderators, including methodological details and whether males exhibit parental care, explained the substantial heterogeneity in our dataset. We hope to stimulate research that more inclusively considers the many and varied ways in which parasites, sexual selection, and epidemiology intersect.</span></p>
Indice de Dépollution Atmosphérique (IDA) cartographié dans 10 zones urbaines de la région Grand Est, France
<p>Un indice global de décontamination appelé Indice de Dépollution Atmosphérique (IDA), qui comprend plusieurs paramètres de pollution atmosphérique (NO<sub>2</sub>, HCHO, SO<sub>2</sub>, O<sub>3</sub>, CO et Indice d'Aérosol), a été développé et la diminution des niveaux de pollution atmosphérique dans dix zones urbaines de la région Grand Est, dans le nord-est de la France, a été analysée quantitativement. L'IDA est calculé à partir de la standardisation du changement relatif des polluants introduits dans le modèle. La standardisation permet d'obtenir un score (<em>z</em>), calculé comme le score de changement brut (<em>Δx</em>) moins la moyenne arithmétique des rasters (<em>m</em>), divisé par l'écart-type des rasters (<em>s</em>) et multiplié par un facteur d'étirement défini (<em>d</em>). De cette manière, l'IDA peut être défini comme la somme des différences de rasters standardisées.</p>
Figure 4 A-E in LIFE CYCLE OF NATURAL POPULATIONS OF METRIOCNEMUS (INERMIPUPA) CARMENCITABERTARUM LANGTON & COBO 1997 (DIPTERA: CHIRONOMIDAE) IN THE NETHERLANDS: INDICATIONS FOR A SOUTHERN ORIGIN? Abstract
Figure 4 A-E. Numbers of adults, egg strings and pupal exuviae of Metriocnemus (Inermipupa) carmencitabetarum recorded between April and December in 2012 and 2013 at Appingdam and Nijmegen and of 4th instar larvae at Nijmegen in 2013: 4A number of adults and egg strings at Appingedam, 2012; 4B number of adults and pupal exuviae at Appingedam 2013; 4C number of adults and egg strings at Nijmegen 2012; 4D number of adults and pupal exuviae at Nijmegen 2013; 4E numbers of 4th instar larvae recorded in the upper 50 cm of the water butt at Nijmegen, 2013. Horizontal brackets indicate periods of adult activity of one generation. Periods with no observations are shaded.
Figure 3 in LIFE CYCLE OF NATURAL POPULATIONS OF METRIOCNEMUS (INERMIPUPA) CARMENCITABERTARUM LANGTON & COBO 1997 (DIPTERA: CHIRONOMIDAE) IN THE NETHERLANDS: INDICATIONS FOR A SOUTHERN ORIGIN? Abstract
Figure 3. Emergence pattern of the wintering generation from the Appingedam vase in spring 2014 (black dots and solid line) and hypothetical emergence pattern when larval development would have been interrupted by a diapause (dotted line). Number of pupal exuviae are added up per week.
Figure 5 in LIFE CYCLE OF NATURAL POPULATIONS OF METRIOCNEMUS (INERMIPUPA) CARMENCITABERTARUM LANGTON & COBO 1997 (DIPTERA: CHIRONOMIDAE) IN THE NETHERLANDS: INDICATIONS FOR A SOUTHERN ORIGIN? Abstract
Figure 5. Seasonal length differences in male and female pupal exuviae in Nijmegen in 2013, with a binomial fit (left y-axis). The two wavy lines represent mean weekly minimum and maximum temperatures from April to October (right y-axis).
Figure 1 in LIFE CYCLE OF NATURAL POPULATIONS OF METRIOCNEMUS (INERMIPUPA) CARMENCITABERTARUM LANGTON & COBO 1997 (DIPTERA: CHIRONOMIDAE) IN THE NETHERLANDS: INDICATIONS FOR A SOUTHERN ORIGIN? Abstract
Figure 1. Distribution of Metriocnemus (I.) carmencitabertarum in the Netherlands and the location of the two research sites Appingedam and Nijmegen.
Figure 2 in LIFE CYCLE OF NATURAL POPULATIONS OF METRIOCNEMUS (INERMIPUPA) CARMENCITABERTARUM LANGTON & COBO 1997 (DIPTERA: CHIRONOMIDAE) IN THE NETHERLANDS: INDICATIONS FOR A SOUTHERN ORIGIN? Abstract
Figure 2. Head width and head length of wintering larvae collected in the Appingedam vase in December 2013 and February 2014. Results are projected in a HW-HL graph of the four M. (I.) carmencitabertarum instars obtained from Kuper (2015) revealing wintering in 3rd and 4th larval stage in the Appingedam vase.
Figure 6 in LIFE CYCLE OF NATURAL POPULATIONS OF METRIOCNEMUS (INERMIPUPA) CARMENCITABERTARUM LANGTON & COBO 1997 (DIPTERA: CHIRONOMIDAE) IN THE NETHERLANDS: INDICATIONS FOR A SOUTHERN ORIGIN? Abstract
Figure 6. Correlation between mean ambient temperature during development and skin size for males and females from the Nijmegen water butt in 2013.
Landsat-derived rangeland condition indicators in East Africa from 2000 to 2022
<p>Tracking environmental change is important to ensure efficient and sustainable natural resources management. East Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most of the human population. Despite several mapping efforts, East Africa lacks accurate and reliable high-resolution rangeland health maps necessary for management, policy, and research purposes. Earth Observations offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches that rely on in-situ methods, while complimenting their certainty. Using machine learning-based classification and linear unmixing, this paper produced Landsat-based time series at 30 m spatial resolution for mapping of land cover classes (LCC) and vegetation fractional cover (VFC, including photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare ground BG), two major data assets to derive metrics for rangeland health in East Africa. Due to scarcity of in-situ measurements in a large, remote and highly heterogeneous landscape, an algorithm was developed to combine very high-resolution WorldView-2 and -3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region. The LCC analysis yielded an overall accuracy of 0.856 using our validation dataset, with Kappa of 0.832; VFC, yielded R<sup>2</sup> = 0.801, <em>p</em> < 2.2e-16, normalized root mean squared error (nRMSE) = 0.123. Our products represent the first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia and Somalia, covering a total area of 745,840 km<sup>2</sup>, dominated by arid and semi-arid extensive rangeland systems. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 and Google Earth Engine (GEE; details in data availability section).</p>
Relation entre l'Indice d'Influence Humaine (IIH) et la variation relative du NO2 troposphérique (ΔNO2) pendant avril 2020 dans les départements du Grand Est (Fig. 4a)
<p>L'IIH est une mesure territoriale de l'impact humain sur les terres, estimée à partir de plusieurs indicateurs anthropiques globaux dont : la pression démographique (densité de population), l'utilisation des terres et les infrastructures (zones bâties, éclairage nocturne, utilisation des terres/couverture des terres) et l'accessibilité (littoral, routes, voies ferrées, rivières navigables). Un modèle de régression a indiqué la forte dépendance de la baisse du NO<sub>2</sub> par rapport à l'IIH lors des mesures de confinement en avril 2020 (R<sup>2</sup> = 0,91) dans les départements du Grand Est.</p>
Dynamics-based characterisation and classification of biodiversity indicators
<p>Various biodiversity indicators, such as species richness, total abundance, and species diversity indices, have been developed to capture the state of ecological communities over space and time. As biodiversity is a multifaceted concept, it is important to understand the dimension of biodiversity reflected by each indicator for successful conservation and management. Here we utilised the responsiveness of biodiversity indicators' dynamics to environmental changes (i.e. environmental responsiveness) as a signature of the dimension of biodiversity. We present a method for characterising and classifying biodiversity indicators according to environmental responsiveness and apply the methodology to monitoring data for a marine fish community under intermittent anthropogenic warm water discharge. Our analysis showed that ten biodiversity indicators can be classified into three super-groups based on the dimension of biodiversity that is reflected. Group I (species richness and community mean of centre of distribution latitude (cCOD)) showed the greatest robustness to temperature changes; Group II (species diversity and total abundance) showed an abrupt change in the middle of the monitoring period, presumably due to a change in temperature; Group III (species evenness) exhibited the highest sensitivity to environmental changes, including temperature. These results had several ecological implications. First, the responsiveness of species diversity and species evenness to temperature changes might be related to changes in the species abundance distribution. Second, the similar environmental responsiveness of species richness and cCOD implies that fish migration from lower latitudes is a major driver of species compositional changes. The study methodology may be useful in selecting appropriate indicators for efficient biodiversity monitoring.</p>
Data and supplementary materials in support of "Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator"
<p>Data and supplementary materials in support of "Evgenios Vlachos, Regine Ejstrup, Thea Marie Drachen, Bertil Fabricius Dorch (2023) Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator, Frontiers in Research Metrics and Analytics, doi: 10.3389/frma.2023.1218213" </p> <p>It includes the anonymized dataset with the OADO values for all researchers from 10 departments (2 per faculty), the R code to perform the analysis and the graphs, the Scopus search string for the background search on Open Access and metrics, and the documentation for pulling data out from Pure.</p>
Dataset of Bibliometric and Wikipedia Indicators of Universities
<p>This dataset is related to the paper: "From academic to media capital: To what extent does the scientific reputation of universities translate into Wikipedia attention?" It contains for each university different bibliometric and Wikipedia indicators.</p> <table align="center"> <thead> <tr> <th scope="col">General</th> <th scope="col">Bibliometrics</th> <th scope="col">Wikipedia</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>impact_P</td> <td>langlinks</td> </tr> <tr> <td>grid_id</td> <td>impact_P_Bio_Health</td> <td>total_views</td> </tr> <tr> <td>short_name</td> <td>impact_P_Life_Earth</td> <td>local_views</td> </tr> <tr> <td>full_name</td> <td>impact_P_Math_Comp</td> <td>int_views</td> </tr> <tr> <td>Country</td> <td>impact_P_Phy_Eng</td> <td>rest_views</td> </tr> <tr> <td>established_year</td> <td>impact_P_Soc_Hum</td> <td>editors</td> </tr> <tr> <td>age</td> <td>P_top10</td> <td>revisions</td> </tr> <tr> <td>Continent</td> <td>collab_P</td> <td>words</td> </tr> <tr> <td> </td> <td>P_int_collab</td> <td>unique_references</td> </tr> <tr> <td> </td> <td>P_OA</td> <td>characters</td> </tr> <tr> <td> </td> <td>TCS</td> <td>sections</td> </tr> <tr> <td> </td> <td>TNCS</td> <td> </td> </tr> <tr> <td> </td> <td>P_industry_collab</td> <td> </td> </tr> <tr> <td> </td> <td>PA_F_MF</td> <td> </td> </tr> <tr> <td> </td> <td>dimensions_wos</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation_bio</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation_eng</td> <td> </td> </tr> <tr> <td> </td> <td>gini_production</td> <td> </td> </tr> </tbody> </table> <p> </p>
Figure 5 in Multiple mitogenomes indicate Things Fall Apart with Out of Africa or Asia hypotheses for the phylogeographic evolution
Figure 5. Molecular clades of A. mellifera mapped onto the first two PCA axes of morphometric ACMO space, redrawn afer Fig. 10.8 in Ref.8. Individual bees are identified to subspecies by the numeric color codes, and re-grouped to genetic clades as in Fig. 3, with additional color variants for Sub-Saharan taxa. Ruttner's four groups [African (A), European (C [Continental]), Mellifera (M), and Asian (O [Oriental]] correspond roughly to the four quadrants, clockwise from the upper lef as A, M, C, and O. Ŀe Levantine/Nilotic/Arabian clade overlays the Sub-Saharan clade, including local variants. Ŀe Asia Minor A. m. caucasia clade is distributed along the M → O axis, and is bisected by its Southeast European sister clade A. m. ligustica. Ŀe Afro-European Mediterranean clade overlies all three, as well as the basal A. m. mellifera clade.
Figure 2 in Multiple mitogenomes indicate Things Fall Apart with Out of Africa or Asia hypotheses for the phylogeographic evolution
Figure 2. Schematic maximum parsimony analysis of phylogenetic relationships among mtDNA genome sequences of 66 individual A. mellifera honey bees from 22 subspecies. Rooting as indicated by Fig. 1. Numbers above branches are inferred numbers of nucleotide substitutions; numbers in bold below branches are percent support in 3000 bootstrap replicates, with SPR branching swapping. Ŀe tree shown is one of nine minimum length trees that differ only by rearrangements at unresolved nodes. Sequences in the Sub-Saharan clade that make that subspecies paraphyletic are tagged in Roman font, as are two sequences referred to A. m. scutellata that are outside that clade. An additional 12 sequences from the Arabian series that are identical to the four shown are not included. Sequences curated as A. m. mellifera in GenBank15 are re-assigned their proper names in parentheses (J. M. Fuller, pers. comm). Subspecies represented by single sequences are indicated by (*). Named phylogeographic clades discussed in the text are indicated in color. Ŀe complete MP tree with GenBank accession numbers is given in Supplementary Fig. S1, along with those for Maximum Likelihood and Neighbor Joining methods (Supplementary Figs. S2 and S3, respectively).
ScienceDex guides
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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.