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1,079 results for “source data”
Data from: The BumbleBox: An open-source platform for quantifying behavior in bumblebee colonies
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Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips
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Data for publication "Evaluation of equivalent black carbon (EBC) source apportionment using observations from Switzerland between 2008 and 2018"
<p>Data to accompany "<a href="https://www.atmos-meas-tech-discuss.net/amt-2019-351/"><em>Evaluation of equivalent black carbon (EBC) source apportionment using observations from Switzerland between 2008 and 2018</em></a>" publication in <em>Atmospheric Measurement Techniques</em>. This repository contains observational data and two helper tables containing monitoring site information and instrument location data. All files are .csv files, with UTF-8 encoding, and are self describing. The time zone used for analysis was UTC + 1, tz database/Olson string "Etc/GMT+1".</p>
Open source physiological data and physiological-based kinetic model code for the chicken (Gallus gallus domesticus)
<p>This excel file and mode code (DOI:10.5281/zenodo.3603114) provides:</p> <p>1. Physiological parameters and associated inter-individual variability (sample size, mean, coefficient of variation,) for chicken (<em>Gallus gallus domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020).</p> <p>2. An R code for the generic chicken physiologically based model as well as the “soboljansen” code to carry out sensitivity analysis using sobol plots. The code for the generic model allows to run:</p> <p>a. A deterministic PBK model which represents only a single animal.</p> <p>b. A probabilistic PBK model to simulate individual differences in physiological parameters within a population. Sensitivity analyses can be performed to identify which parameters have the most impact on the model’s outputs. Predictions can be compared with experimental data. The model can be used to assess the influence of physiological parameters on the kinetics of chemicals. For PBK modelling purposes, species and chemical specific kinetics (e.g clearance, absorption rate, etc…) should be provided by the user.</p> <p>The full data collection and implementation of the models using case studies are described in (Lautz et al., 2020).</p> <p><strong>The dataset providing the physiological parameters is available in Excel.<br> The R code is presented as meta data to be implemented in R.</strong></p>
Data for "Characterization of composition and sources of atmospheric submicron particles in Xi'an, China during summer using an aerosol chemical speciation monitor"
<p>The dataset used for the study of Li et al. (2020). </p>
Crowd and community sourcing to update authoritative LULC data in urban areas
<p>The French National Mapping Agency (Institut National de l'Information Géographique et Forestière - IGN) is responsible for producing and maintaining the spatial data sets for all of France. At the same time, they must satisfy the needs of different stakeholders who are responsible for decisions at multiple levels from local to national. IGN produces many different maps including detailed road networks and land cover/land use maps over time. The information contained in these maps is crucial for many of the decisions made about urban planning, resource management and landscape restoration as well as other environmental issues in France. Recently, IGN has started the process of creating a high-resolution land use land cover (LULC) maps, aimed at developing smart and accurate monitoring services of LULC over time. To help update and validate the French LULC database, citizens and interested stakeholders can contribute using the <a href="https://paysages.ign.fr/">Paysages</a> mobile and web applications. This approach presents an opportunity to evaluate the integration of citizens in the IGN process of updating and validating LULC data.</p> <p><strong>Dataset 1: Change detection validation 2019</strong></p> <p>This dataset contains web-based validations of changes detected by time series (2016 – 2019) analysis of Sentinel-2 satellite imagery. Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: <a href="https://paysages.ign.fr/">Paysages</a> web application and <a href="https://laco-wiki.net/">LACO-Wiki</a>. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes.</p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of the change detection: 2016-2019.</li> <li>Time period of data collection: February 2019-December 2019</li> <li>Total number of contributors: 105</li> <li>Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 1- Change validation locations.png, 1-Change validation 2019 – Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>, and <a href="https://www.geoville.com/">GeoVille</a>.</p> <p><strong>Dataset 2: Land use classification 2019</strong></p> <p>The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (<a href="https://geoservices.ign.fr/documentation/diffusion/telechargement-donnees-libres.html#ocs-ge">OCS-GE 2016</a> ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location. </p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of data collection: August 2019</li> <li>Types of contributors: Surveyors from the production department of IGN</li> <li>Total number of contributors: 5</li> <li>Total number of observations: 2711</li> <li><a href="https://geoservices.ign.fr/ressources_documentaires/Espace_documentaire/BASES_VECTORIELLES/OCS_GE/DC_OCS_GE_1-1.pdf">Data specifications of the OCS-GE</a> ©IGN</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 2- LU classification points.png, 2-LU classification 2019 – Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a> and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p><strong>Dataset 3: In-situ validation 2018</strong></p> <p>The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined: </p> <ul> <li>Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5).</li> <li>Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes.</li> </ul> <p>The dataset has the following characteristics </p> <ul> <li>Time period of data collection: June 2018 – October 2018</li> <li>Types of contributors: students from the School of Agricultural and Life Sciences and citizens</li> <li>Total number of contributors: 26</li> <li>Total number of observations: 281</li> <li>Total number of photos: 421</li> <li>Region of interest: Toulouse and surrounding areas</li> </ul> <p>Associated files: 3- Insitu locations.png, 3- Insitu validation 2018 – Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSON</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="http://www.ign.fr/">IGN-France</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
On Strong Scaling and Open Source Tools for Analyzing Atom Probe Tomography Data
<p>This repository contains supplemental results to the paper "On Strong Scaling and Open Source Tools<br> for Analyzing Atom Probe Tomography Data".</p> <p>Specifically, the input parameter and result files from all synthetic benchmark studies.<br> The results file for all synthetic specimen benchmark studies are available from the<br> authors upon serious request.</p> <p><strong>The repository content is as follows:</strong></p> <p><strong>Real world data:<br> Three triplets of *.tar.gz archive files document the settings and analysis files for the real world case studies</strong><br> 3013902 is the incipient specimen,<br> 2763207 the intermediate specimen, and<br> 3345501 the mature specimen.</p> <p><strong>Scripts.zip</strong><br> Contains all batch scripts we used for performing the analyses as a queue</p> <p><strong>Synthetic data:</strong></p> <p><strong>APTCrystallography.zip</strong><br> Contains all results from the benchmarking of the reimplemented Vicente Araullo-Peters et al. method</p> <p><strong>FullVolumeTessellation.zip</strong><br> Contains all results from the benchmarking of the Voro++ Voronoi volume tessellation method except for the HDF5/XDMF volume tessellation files of the 200 and 2000 million ion datasets</p> <p><strong>TwoPointStatistics.zip</strong><br> Contains all results from benchmarking the computing of 2-point spatial statistics</p> <p><strong>TipSurfaceDescrSpatialStatistics.zip</strong><br> Contains all results from benchmarking the alpha shape computation, descriptive spatial statistics, and clustering analyses</p> <p><strong>PARAPROBE.Results.2Mio.tar.gz<br> PARAPROBE.Results.20Mio.tar.gz</strong><br> <strong>PARAPROBE.Results.200Mio.tar.gz</strong><br> Contains all settings files and some results from benchmarking the hybrid implementation.</p> <p><strong>Unpack the individual repositories using tar through Linux console as follows:</strong><br> tar -xvf <archivename> <br> where <archivename> is a placeholder for the respective tar gz compressed archives within the ZIP file.<br> Alternatively WinRAR can be used on a Windows system.</p> <p><strong>We would kindly like to ask you to use the following repository<br> to access source code of PARAPROBE in the future:</strong></p> <p><strong>https://gitlab.mpcdf.mpg.de/mpie-aptfim-toolbox/paraprobe<br> https://paraprobe-toolbox.readthedocs.io/en/latest/</strong><br> <br> <strong>Only above repository will be updated in the future! </strong><br> <strong>Only there new code additions and bugfixes are posted!<br> The examples folder of this repository contains a folder examples/tasks/paper14 which shows how<br> to run a workflow of paraprobe tools from this above repository to run analyses akin as reported in this paper.</strong></p> <p>Recommendations and bug reports to M. Kühbach are appreciated! Happy APT analyzing!</p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------<br> <strong>The PARAPROBE source code version we used for analyzing the synthetic and real world APT<br> specimens in the paper used an earlier development version of the PARAPROBE tool, the source code</strong><br> <strong>is here:</strong></p> <p>PARAPROBE_20190117_VersionUsedForPaper.zip</p> <p>https://github.com/mkuehbach/PARAPROBE<br> https://paraprobe.readthedocs.io/en/latest/</p> <p> </p>
Temporal characteristics and potential sources of black carbon in megacity Shanghai, China-Data
<p>These data are associated with the research publication with the same title (Temporal characteristics and potential sources of black carbon in megacity Shanghai, China), published in the Journal of Geophysical Research: Atmospheres.</p> <p>The dataset includes hourly and daily black carbon concentrations observed in Shanghai, China and their contribution from liquid fuel, biomass, and coal. The dataset also includes the source data used to produce figures (Figure 2 to Figure 7) in the article. See the associated article for details.</p>
Tour de France data for the improvement of energy consumption in devices powered by limited energy sources
<p>We propose a set of data that were collected as part of a "tour de France" with electrical wheelchair.</p> <p>Part of these data are allowed to propose a mathematical model based on an experimental methodology on the energy consumed in smartphones.</p> <p>The objective is to make accessible the data related to the publications in several fields of research (computer science, telecommunication, meteorological science, artificial intelligence, statistics ...)</p>
Improving early estimates of large earthquake's final fault lengths and magnitudes leveraging source fault structural maturity information - supplementary data
<p>Supplementary dataset for <em>Improving early estimates of large ea­­rthquake’s final fault lengths and magnitudes leveraging source fault structural maturity information. </em>This contains individual performance test results for the algorithm discussed in this publication for each earthquake included in the study</p>
The WWU DUNEuro reference data set for combined EEG/MEG source analysis
<p>The provided dataset consists of two high-quality realistic head models and combined EEG/MEG data which can be used for state-of-the-art methods in brain research, such as modern finite element methods (FEM) to compute the EEG/MEG forward problems using the software toolbox DUNEuro (http://duneuro.org).</p> <p>A combined EEG/MEG dataset from a somatosensory experiment is provided (<strong>sep_sef.zip</strong>): Somatosensory evoked potentials (SEP) and fields (SEF) were elicited by stimulating the median nerve at the wrist of the right arm with monophasic square-wave electrical pulses with 0.5 ms duration. A random stimulus onset asynchrony between 350 and 450 ms was used and the strength was adjusted to invoke a clear movement of the thumb. The duration of the experiment was 10 minutes for a measurement of 1200 trials and data was acquired with a sampling rate of 1200 Hz and online low pass filtered at 300 Hz. An artifact reduction was achieved by reversing the polarity of the stimulation during the second half of the measurement. A 74-channel EEG (EASYCAP GmbH, Herrsching, Germany), for which the electrode positions were digitized using a Polhemus device (FASTRAK, Polhemus Incorporated, Colchester, Vermont, U.S.A.), and a whole-head MEG with 275 axial gradiometers and 29 reference coils (OMEGA2005, VSM MedTech Ltd., Canada) were used in the measurement.</p> <p>Ethics Statement: One healthy subject (49 years, male) participated in this study. The subject had no history of psychiatric or neurological disorders and had given written informed consent before the experiment. All procedures had been approved by the ethics committee of the University of Erlangen, Faculty of Medicine on 10.05.2011 (Ref. No. 4453).</p> <p>Additionally, two different advanced realistic head models are supplied, which both use a six-compartment segmentation from T1/T2-MRI of the test subject. They differentiate between scalp, skull compacta, skull spongiosa, cerebrospinal fluid (CSF) and gray and white matter tissue. One head model is a tetrahedral volumetric mesh (<strong>realistic_tet_mesh_6c.msh</strong>), while the other provides the geometric information by level-sets for each tissue boundary (<strong>realistic_levelsets_6c.zip</strong>). </p> <p>A detailed description of the construction of the tetrahedral mesh can be found <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25272">here</a> (subsection 2.3), the main steps are presented in the following. First, the MR images were co-registered and resampled so that the voxels of the anatomical data are cubic. Furthermore, the images were cut sufficiently below the skull of the participant. Subsequently, the segmentation of the T1w and T2w was performed in order to create six volumetric masks representing the six tissue compartments. The brain compartment was segmented via the <a href="http://surfer.nmr.mgh.harvard.edu">FreeSurfer</a> software. The remaining preprocessing and creation of the volumetric masks was entirely performed via routines available in <a href="https://www.fieldtriptoolbox.org/">FieldTrip</a>. In particular, the scalp and skull segmentations were done via the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">spm12</a> software, embedded in FieldTrip. Once the masks were assembled, a volumetric tetrahedral mesh was created using the <a href="https://doc.cgal.org/Manual/3.5/doc_html/cgal_manual/Mesh_3/Chapter_main.html">CGAL</a> software embedded in <a href="http://iso2mesh.sourceforge.net/cgi-bin/index.cgi">iso2mesh</a>, resulting in 885,214 nodes and 5,335,615 tetrahedrons. The mesh is provided in <a href="https://gmsh.info">gmsh</a> format, including information about the node positions, elements defined by their node indices, and labels for each element indicating the tissue compartment.</p> <p>For the construction of the unfitted head model, a six-compartment voxel segmentation was constructed based on the T1- and T2-weighted MR images, distinguishing between skin, skull compacta and spongiosa, CSF, gray and white matter using <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">SPM12</a> via <a href="https://www.fieldtriptoolbox.org/">Fieldtrip</a>, <a href="https://fsl.fmrib.ox.ac.uk/fsl">FSL</a> and internal MATLAB routines. Surfaces were extracted from this voxel segmentation to distinguish between the different tissue compartments. In order to smooth the surfaces while sustaining the available information from the segmentation, we applied an anti-aliasing algorithm created for binary voxel images presented in (<a href="https://doi.org/10.1145/353888.353893">Whitaker, 2000</a>). The resulting smoothed surfaces are represented as discrete level-set functions, i.e., by <span class="math-tex">\(N^3\)</span>-dimensional arrays (<span class="math-tex">\(N\)</span>=257), the value on each node indicates the signed distance to the respective surface.</p>
Data for "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations"
<p>This file consists of the time series of the measured trace gases, meteorological parameters, and the concentrations of isoprene and monoterpenes in the Landes forest and at the SMEAR Ⅱ station, which have been analyzed in the manuscript "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations". For more details, please contact the author (haiyan.li@helsinki.fi).</p>
Data from: De novo gene birth, horizontal gene transfer and gene duplication as sources of new gene families associated with the origin of a symbiosis in Amanita
<p>By introducing novel capacities and functions, new genes and gene families may play a crucial role in ecological transitions. Mechanisms generating new gene families include <i>de novo</i> gene birth, horizontal gene transfer and neofunctionalization following a duplication event. The ectomycorrhizal (ECM) symbiosis is a ubiquitous mutualism and the association has evolved repeatedly and independently many times among the fungi, but the molecular dynamics enabling its emergence remain elusive. We developed a phylogenetic workflow to first understand if gene families unique to ECM <i>Amanita</i> fungi and absent from closely related asymbiotic species are functionally relevant to the symbiosis, and then to systematically infer their origins. We identified 109 gene families unique to ECM <i>Amanita </i>species. Genes belonging to unique gene families are under strong purifying selection and are upregulated during symbiosis, compared to genes of conserved or orphan gene families. The origins of seven of the unique gene families are strongly supported as either <i>de novo</i> gene birth (two gene families), horizontal gene transfer (four), and gene duplication (one). An additional 34 families appear new because of their selective retention within symbiotic species. Among the 109 unique gene families, the most upregulated gene in symbiotic cultures encodes an ACC deaminase, an enzyme capable of downregulating the synthesis of the plant hormone ethylene. Ethylene is a common negative regulator of plant-microbial mutualisms.</p>
Source data for: Antagonistic effects of intraspecific cooperation and interspecific competition on thermal performance
<p>Understanding how climate-mediated biotic interactions shape thermal niche width is critical in an era of global change. Yet, most previous work on thermal niches has ignored detailed mechanistic information about the relationship between temperature and organismal performance, which can be described by a thermal performance curve. Here, we develop a model that predicts the width of thermal performance curves will be narrower in the presence of interspecific competitors, causing a species' optimal breeding temperature to diverge from that of its competitor. We test this prediction in the Asian burying beetle <i>Nicrophorus nepalensis</i>, confirming that the divergence in actual and optimal breeding temperatures is the result of competition with their primary competitor, blowflies. However, we further show that intraspecific cooperation enables beetles to outcompete blowflies by recovering their optimal breeding temperature. Ultimately, linking abiotic factors and biotic interactions on niche width will be critical for understanding species-specific responses to climate change.</p>
Data for: Birth order as a source of within-genotype diversification in the clonal duckweed, Spirodela polyrhiza (Araceae: Lemnoideae)
<p>Organismal persistence attests to adaptive response to environmental variation. Diversification bet hedging, in which risk is reduced at the cost of expected fitness, is increasingly recognized as an adaptive response, yet mechanisms by which a single genotype generates diversification remain obscure. The clonal greater duckweed, <i>Spirodela polyrhiza </i>(L.), facultatively expresses a seed-like but vegetative form, the "turion", that allows survival through otherwise lethal conditions. Turion reactivation phenology is a key fitness component, yet little is known about turion reactivation phenology in the field, or sources of variation. Here, using floating traps deployed in the field, we find a remarkable extent of variation in natural reactivation phenology that cannot be explained solely by spring cues, occurring over a period of at least 200 days. Under controlled laboratory conditions, we find support for the hypothesis that turion phenology is influenced jointly by phenotypic plasticity to temperature and diversification within clones. Turion "birth order" consistently accounted for a difference in reactivation time of 46 days at temperatures between 10° and 18°C, with early birth-order turions reactivating more rapidly than late birth-order turions. These results should motivate future work to formally evaluate turion phenology variance as a bet-hedging trait.</p>
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
Data from: Montane meadows: A soil carbon sink or source?
<p>As the largest biogeochemically active terrestrial reserve of carbon (C), soils have the potential to either mitigate or amplify rates of climate change. Ecosystems with large C stocks and high rates of soil C sequestration, in particular, may have outsized impacts on regional and global C cycles. Montane meadows have large soil C stocks relative to surrounding ecosystems. However, anthropogenic disturbances in many meadows may have altered the balance of C inputs and outputs, potentially converting these soils from net C sinks to net sources of C to the atmosphere. Here, we quantified ecosystem-level C inputs and outputs to estimate the annual net soil C flux from 13 montane meadows spanning a range of conditions throughout the California Sierra Nevada. Our results suggest that meadow soils can be either large net C sinks (577.6 ± 250.5 g C m−2 y−1) or sources of C to the atmosphere (− 391.6 ± 154.2 g C m−2 y−1). Variation in the direction and magnitude of net soil C flux appears to be driven by belowground C inputs. Vegetation species and functional group composition were not associated with the direction of net C flux, but climate and watershed characteristics were. Our results demonstrate that, per unit area, montane meadows hold a greater potential for C sequestration than the surrounding forest. However, legacies of disturbance have converted some meadows to strong net C sources. Accurate quantification of ecosystem-level C fluxes is critical for the development of regional C budgets and achieving global emissions goals.</p>
Source data for: Presynaptic NMDARs cooperate with local spikes toward GABA release from the reciprocal olfactory bulb granule cell spine
<p><span>In the rodent olfactory bulb the smooth dendrites of the principal glutamatergic mitral cells (MCs) form reciprocal dendrodendritic synapses with large spines on GABAergic granule cells (GC), where unitary release of glutamate can trigger postsynaptic local activation of voltage-gated Na<sup>+</sup>-channels (Na<sub>v</sub>s), i.e. a spine spike. Can such single MC input evoke reciprocal release? We find that unitary-like activation via two-photon uncaging of glutamate causes GC spines to release GABA both synchronously and asynchronously onto MC dendrites. This release indeed requires activation of Na<sub>v</sub>s and high-voltage-activated Ca<sup>2+</sup>-channels (HVACCs), but also of NMDA receptors (NMDAR). Simulations show temporally overlapping HVACC- and NMDAR-mediated Ca<sup>2+</sup>-currents during the spine spike, and ultrastructural data prove NMDAR presence within the GABAergic presynapse. The cooperative action of presynaptic NMDARs allows to implement synapse-specific, activity-dependent lateral inhibition and thus could provide an efficient solution to combinatorial percept synthesis in a sensory system with many receptor channels. </span></p>
Amory et al. (2021), Geoscientific Model Development : data, model outputs and source code
<p><strong>Data and model outputs for the replication of the analysis made in:</strong><br> (see the published version of this article in Geoscientific Model Development, 2021 - please cite this version if you use these data)<br> C. Amory, C. Kittel, L. Le Toumelin, C. Agosta, A. Delhasse, V. Favier, and X. Fettweis: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adelie Land, East Antarctica, Geoscientific Model Development, accepted, 2021. </p> <p>See README.txt for a full description of the dataset content</p> <p>Please contact me at amory.charles@live.fr if you need other half-hourly outputs or for more details on the dataset</p>
Diamond Light Source data mm21716
<p>Diamond Light Source data mm21716 linked to Quadrupolar X-ray Magnetic Circular Dichroism using Superchiral X-rays</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.