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1,053 results for “MD”
GRIME AI Water Segmentation Model for the USGS Lake Serene at Edgewood Camera Monitoring Site, MD, 2022-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks). All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/MD_Lake_Serene_at_Edgewood for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project was conducted in 2023-2025 by collaborators at the University of Nebraska-Lincoln, Uni
Small Mammal Trapping Data for Assateague Island, MD 1978
This is raw trapping data from a survey of small mammals along 21 transects on the Maryland portion of Assateague Island during the summer of 1978.
A2aR Oligomeric assemblies identified from MD simulations using in-vivo mimetic biomembranes
<p>GPCR oligomerisation is known to play an important role in the receptor signalling. However, due to the technical challenges, the structural information of GPCR oligomerisation is still very limited, which hinders our understanding of GPCR signalling in a fuller picture. In this deposit, we provide the structural coordinates of various oligomeric assemblies of Adenosine A2a receptor that were sampled from unbiased MD simulations.</p> <p>For more information regarding the MD simulation setup and definitions of the various calculated values, please check out our paper on <a href="https://www.biorxiv.org/content/10.1101/2020.06.24.168260v2">BioRxiv</a> (doi: https://doi.org/10.1101/2020.06.24.168260)</p> <p>** Simulation setup **<br> 9 copies of A2aR were randomly inserted into an <em>in-vivo </em>mimetic biomembrane (of size of 45nm x 45nm) to build the initial configuration of the simulations. 10 such systems were set up for A2aR in the inactive state, 10 for the active state and 10 for the active in complex with the mini Gs state. These systems were represented by MARTINI 2 coarse-grained models and were simulated for 50 micro-seconds. The use of MARTINI coarse-grained force field would freeze the receptor conformation in the initial configuration, thus decoupled the oligomerization from such process as ligand-induced conformational change. The more efficient sampling of coarse-grained force field therefore allowed us to explore fully the protein-protein associations in the oligomerisation process. Protein-protein associations were identified when any atoms from two protomers were getting closer than 0.75 nm. The oligomerisation process was monitored and the sampled various oligomeric assemblies were identified for calculation of oligomer residence time. </p> <p><br> ** Coordinate file explained **<br> These pdb files contain the A2aR oligomer structures in atomistic models. The coarse-grained oligomeric structures were converted back to atomistic models using CHARMM 36 force field. The identified oligomeric structures from each oligomeric order were clustered. 10 structures were randomly taken from each cluster and stored as individual models in the pdb files with a naming format <strong><em>{Conf. State}_OS{Oligomeric Order}_cl{Cluster id}.pdb</em></strong>. The pdb files can be viewed by such visualization tools as PyMol, Chimera or JMol etc.</p> <p><br> ** Spreadsheet file explained **<br> The calculated properties, including residence time and geometry, of each identified oligomer were stored in the Excel shreadsheet (Oligomeric_Assembly_Distribution.xlsx). Each oligomeric order, i.e. oligomer order = 2,3,4,5, opens an individual spreadsheet page where the calculated data were grouped by oligomers' conformational states (i.e. Inactive, Active and Act + mini Gs) and then ranked by oligomers' residence time. The measurements for describing the oligomer geometry were shown in columns after "Cluster ID" and before "Count". For definitions of these measurements, please refer to our paper. Pictures of the oligomers viewed from the extracellular side and intracellular side were also provided in the the spreadsheet to assist visualisation.</p>
MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
<p><strong>Background</strong></p> <p>Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. </p> <p><strong>Contents</strong></p> <p>There are three tarball (<strong>.tar.gz</strong>) files containing the <strong>core simulation data</strong>: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains:</p> <p>1. a source PDB (<strong>.pdb</strong>) file</p> <p>2. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.</p> <p>There is an additional tarball containing the <strong>control files</strong> <strong>and scripts</strong> used for running the MD simulations:</p> <p>1. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model</p> <p>2. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps</p> <p>3. control file <strong>prod.ctl</strong> used for the production run </p> <p>4. Executable <strong>run_prod</strong> that was used to perform the production run</p> <p>5. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs</p> <p>6. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs.</p>
MD simulations of phosphorylated peptides (GGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of six repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXGGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXGGGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites: pseudo-experimental data
<p>readme.txt</p> <p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>This contribution introduces an unconventional procedure to characterize spatial profiles of elastic and inelastic properties inside polymer interphases around nanoparticles. Interphases denote those regions in the polymer matrix whose mechanical properties are influenced by the filler surfaces and thus deviate from the bulk properties. They are of particular relevance in case of nano-sized filler particles with a comparatively large surface-to-volume ratio and hence can explain the frequent observation that the overall properties of polymer nanocomposites cannot be determined by classical mixing rules, which only consider the behavior of the individual constituents.<br> <br> Interphase characterization for nanocomposites poses hardly solvable challengesto the experimenter and is still an unsolved problem in many cases. Instead of real experiments, we perform pseudo experiments using our recently developed Capriccio method, which is an MD-FE domain-decomposition tool specifically designed for amorphous polymers. These pseudo-experimental data then serve as input for a typical inverse parameter identification. With this procedure, spatially varying mechanical properties inside the polymer are, for the first time, translated into intuitively understandable profiles of continuum mechanical parameters.</p> <p><br> As a model material, we employ silica-enforced polystyrene, for which our procedure reveals exponential saturation profiles for Young’s modulus and the yield stress inside the interphase, where the former takes about seven times the bulk value at the particle surface and the latter roughly triples. Interestingly, hardening coefficient and Poisson’s ratio of the polymer remain nearly constant inside the interphase. Besides gaining insight into the constitutive influence of filler particles, these unexpected and intriguing results also offer interesting explanatory options for the failure behavior of polymer nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universiät Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:<br> [1] Ries, M.; Possart, G.; Steinmann, P. & Pfaller, S., "A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites," <em>International Journal of Mechanical Sciences, </em><em>Elsevier, </em><strong>2021</strong>, 106564.</p> <p>This dataset contains the results of a multiscale study on polystyrene-silica nanocomposites using an atomistic-continuum coupling approach. 120 polystyrene samples, each containing 2 nano-sized silica particles are subjected to uniaxial tension. Here we use coarse-grained molecular dynamics (MD) domain embedded into a larger finite element (FE) region. These two resolutions are coupled in a concurrent multiscale fashion using the so-called Capriccio method. We observe the deformation state of the MD and FE domain, as well as the relative displacement of the two nanoparticles with respect to each other. Based on this pseudo-experimental data, we derive the material properties (Young's modulus, Poisson's ratio, yield stress, hardening) of the interphase forming in the proximity of the nanoparticles in [1].</p> <p>A more detailed description of the used methods can be found in Ries et al. [1].</p> <p> </p> <p><strong>Content:</strong></p> <p>The attached text file contains the following quantities (columns) for all samples (rows):</p> <ul> <li>sample: [initial nanoparticle distance]-ID</li> <li>d0_NP: initial distance of nanoparticles in nm</li> <li>rot_x: rotation of nanoparticles with respect to x-axis in degree</li> <li>d_NP: distance of nanoparticles in nm (after equilibration)</li> <li>Elements: number of finite elements</li> <li>Element_warnings: number of element warnings by Abaqus</li> <li>LS: loadstep 1-6</li> <li>eps_NP(LS): tensile strain of nanoparticles in loadstep LS in %</li> <li>eps_MD(LS): tensile strain of MD domain in loadstep LS in %</li> <li>eps_NP_MD(LS): tensile strain of nanoparticles normalized to eps_MD(LS) in loadstep LS</li> <li>eps_FE(LS): tensile strain of FE domain in loadstep LS in %</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>u_max(LS): maximum displacement of FE nodes in load step LS in nm</li> <li>F_ext(LS): external force in load step LS in E-11 N</li> </ul> <p> </p>
CH3CH2OCH3 molecule 200 ps MD trajectory with energies and forces
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p> </p> <p>SOAP params:</p> <p>species=["H", "C", "O"],</p> <p>periodic=False,</p> <p>rcut=5.0,</p> <p>sigma=0.5,</p> <p>nmax=5,</p> <p>lmax=5,</p> <p>average="outer" / "inner",</p> <p>crossover=True,</p> <p>dtype="float64",</p> <p>------------------------------------------------</p> <p>SOAP invariants were calculated with DScribe library (https://pypi.org/project/dscribe/1.2.1/)</p> <p> </p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
TRPC3 interaction with cholesterol as explored through MD (raw data)
<p>Transient receptor potential canonical 3 (TRPC3) channel belongs to the superfamily of transient receptor potential (TRP) channels which mediate Ca<sup>2+</sup> influx into the cell. These channels constitute essential elements of cellular signalling. TRPC3 is primarily gated by lipids, and its surface expression has been shown to be dependent on cholesterol, yet a comprehensive exploration of its interaction with this lipid has thus far not emerged. Here, through 80 µs of coarse-grained molecular dynamics simulations, we show that cholesterol interacts with multiple elements of the transmembrane machinery of TRPC3. Through our approach, we identify an annular binding site for cholesterol on the pre-S1 helix, and a non-annular site at the interface between the voltage-sensor like domain and pore domains. Here cholesterol interacts with exposed polar residues, and possibly acts to stabilise the domain interface.</p> <p> </p> <p><br> p { margin-bottom: 0.08in; color: #000000; line-height: 0.24in; text-align: justify; orphans: 2; widows: 2; background: transparent }p.western { font-family: "Palatino Linotype", serif; font-size: 12pt }p.cjk { font-family: "Palatino Linotype", serif; font-size: 12pt; so-language: de-DE }p.ctl { font-family: "Palatino Linotype", serif }a:visited { color: #954f72; text-decoration: underline }a:link { color: #0000ff; text-decoration: underline }</p> <p> </p>
Dataset - CORE-MD Post-Market Surveillance Tool
<p>WP3 of CORE-MD investigated how to aggregate and extract maximal value for post-market surveillance from medical device registries, big data, clinical practices and experience, and the internet. This data collection was created by the Task 3.2 of the CORE-MD project, as the result of the proposed methodological framework to transform unstructured and dispersed publicly available safety information (Field Safety Notices, recalls, alerts) into a standardized and harmonized database. The databases includes 137,720 historical safety notices (updated to February 2024) safety notices published by different competent national authorities (16 EU Member States and 5 extra EU jurisdictions).</p>
Cellulose test MD dataset for analysis with Galaxy and BRIDGE
<p>This coordinate and trajectory dataset is that of cellulase and octaose substrate in water. It has been derived from the <a href="https://www.rcsb.org/structure/7cel">7CEL PDB</a> structure of a fungal cellobiohydrolase. </p> <p>The original enzyme has been modified to revert the mutation at position 217 and to include disulfide bonds. The octaose substrate is an oligosaccharide consisting of 8 beta 1-4 linked glucose monomers. The enzyme and substrate have been placed in a cubic TIP3P water box containing 0.15 M ions (NaCl).</p> <p>The files includes are: </p> <ul> <li><strong>cbh1test.pdb</strong>: the coordinates of the entire system (water, protein and substrate) in PDB format. </li> <li><strong>cbh1test.dcd</strong>: a short MD trajectory (16 frames) in CHARMM DCD format.</li> </ul>
Example dataset for openPMD-conform molecular dynamics data (MD domain extension)
<p>This dataset results from the molecular dynamics (MD) simulation of the photon-sample interaction. The photons are propagated through the SASE1 beamline and the SPB-SFX instrument at European XFEL, with an initial energy of 5 keV. The sample is the two-nitrogenase iron protein (2nip) with 4348 atoms. The simulation is performed with a demo version of XMDYN. The datasets were rewritten from the original XMDYN output into an hdf5 format that complies with the openPMD metadata standard for particle and mesh data and the proposed domain extension of this standard for MD data. The dataset "pure_2nip_pmi_out.opmd.h5" conforms the openPMD metadata MD domain extension strictly, while the dataset "pure_2nip_pmi_out.opmd.ff.h5" stores form factor results additionally for SingFEL diffraction simulation.</p> <p>This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.<br> </p>
MD simulations of SARS-CoV-2 Spike Protein under static electric fields
<p>This dataset contains trajectories corresponding to all-atom MD simulations of segments of the SARS-CoV-2 Spike Protein, and in-silico mutations, under the influence of moderate external electric fields. The final structures of some of the simulations were used to perform in-silico docking with ACE2 receptor to evaluate the effect of comformational changes (docking was perform with PyDOCK).</p> <p>The file trajectories_6vsb_dt1ns.zip contains trajectories of simulations that were performed on a segment of the Protein Data Bank ID 6VSB comprising RBD, SD1 and SD2. The file trajectories_6m0j_dt1ns.zip correspond to the RBD in Protein Data Bank ID 6M0J. The file trajectories_in-silico_mutations_dt1ns.zip correspond to simulations performed on in-silico generated mutations following the mutations corresponding to WHO Variants of Concern UK, South Africa and Brazil. In all cases, simulations were performed at different electric field intensities ranging between 10<sup>4</sup> V/m and 10<sup>7</sup> V/m, with an extra short simulation under very high intensity (10<sup>9</sup> V/m). The file docked_structures_6m0j.zip contains the 100 best scored docked structures for each case as the output of PyDOCK.</p> <p>Trajectories are stored in GROMACS compressed trajectory file format (.xtc), downsampled to a 1ns timestep. Individual trajectories length are between 300 nanoseconds and 1 microsecond. In-silico docked structures are in PDB format. See linked preprint for more details.</p>
EA-MD-QD: Large Euro Area and Euro Member Countries Datasets for Macroeconomic Research
<p>EA-MD-QD is a collection of large monthly and quarterly EA and EA member countries datasets for macroeconomic analysis.<br>The EA member countries covered are: AT, BE, DE, EL, ES, FR, IE, IT, NL, PT.</p> <p>The formal reference to this dataset is: </p> <p><strong>Barigozzi, M. and Lissona, C. (2024) "EA-MD-QD: Large Euro Area and Euro Member Countries Datasets for Macroeconomic Research". Zenodo.</strong></p> <p>Please refer to it when using the data.</p> <p>Each zip file contains:<br><br>- Excel files for the EA and the countries covered, each containing an unbalanced panel of raw de-seasonalized data.<br><br>- A Matlab code taking as input the raw data and allowing to perform various operations such as:<br>choose the frequency, fill-in missing values, transform data to stationarity, and control for covid outliers.<br><br>- A pdf file with all informations about the series names, sources, and transformation codes.</p> <p><strong>This version (10.2025):</strong></p> <p>Updated data as of 31-October-2025. </p>
Test_MD_traj_for_MD2NMR
<p>This is a testing data set for MD2NMR:</p> <p>https://pypi.org/project/MD2NMR/</p> <p>https://github.com/DerienFe/MD2NMR</p>
An integrated approach including docking, MD simulations, and network analysis highlights the action mechanism of the cardiac hERG activator RPR260243
<p>500 ns MD trajectories of the hERG bound states (protein and ligand) used for the analyses discussed in the paper. There are three replica for each system. The trajectories can be visualized using molecular visualization programs such as Pymol or VMD uploading the PDB and the DCD file.</p> <p>The PDB and the topology files of the hERG bound state (protein, membrane, ions and ligand) are also included.</p> <p>An example of input file used for the production step of the dynamics has been provided (production_1.conf). </p>
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</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.