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14,239 results for “STRUCTURE”
Database of measurements for damage detection of T-type timber structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two timber beams connected at an angle of 90⁰. Presented data related to seven different states of joints, five load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p>
Database of measurements for damage detection of panel-to-panel moment joints in timber structures by Coaxial Correlation Method
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned in two different ways on either side of the investigated panel-to-panel connection. Presented data related to ten different states of joints, two load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds with frequency range from 10 Hz to 2000 Hz). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the case of static load equal to 151.8 kg with sweep-type input signal, and T2 scheme of sensors placement is described in Kurtenoks, V.; Kurajevs, A.; Buka-Vaivade, K.; Serdjuks, D.; Lapkovskis, V.; Mironovs, V.; Podkoritovs, A.; Vilnitis, M. The Quality Assessment of Timber Structural Joints Using the Coaxial Correlation Method. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1929. https://doi.org/10.3390/buildings13081929</p>
Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
Copper mineralization at Carajás mineral province - Brazil: geological, structural, and geophysical data
<p>Gridded geological, structural, and geophysical data at the Carajás mineral province. A number of known Cu occurrences are provided. This dataset is suitable for experimenting with machine learning methods.</p>
Dataset of "From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation"
Liquid-jet photoelectron spectroscopy (LJ-PES) and electronic-structure theory were employed to investigate the chemical and structural properties of the amino acid L-proline in aqueous solution for its three ionized states (protonated, zwitterionic, deprotonated). This is the first PES study of this amino acid in its most biologically relevant environment. Proline's structure in the aqueous phase under neutral conditions is zwitterionic, distinctly different from the non-ionic neutral form in the gas phase. By analyzing the carbon 1s and nitrogen 1s core-levels as well as the valence spectra of aqueous-phase proline, we found that the electronic structure is dominated by the protonation state of each constituent molecular site (the carboxyl and amine) with small yet noticeable interference across the molecule. The site-specific nature of the core-level spectra enables probing of individual molecular constituents. The valence photoelectron spectra are more difficult to interpret because of overlapping signals of proline with the solvent and pH-adjusting agents (HCl and NaOH). Yet we are able to reveal subtle effects of specific (hydrogen-bonding) interaction with the solvent on the electronic structure. We also demonstrate that the relevant conformational space is much smaller for aqueous-phase proline than it is for its gas phase analogue. This study suggests that caution must be taken when comparing photoelectron spectra for gaseous and aqueous-phase molecules, particularly if those molecules are readily protonated / deprotonated in solution.
Dataset of "A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure"
<p>3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).</p>
Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
Non-perturbative phase structure of the bosonic BMN matrix model --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice calculations investigating the phase structure of the bosonic part of the Berenstein--Maldacena--Nastase matrix model. See the README for further information.</p>
Dataset of "Structural Development on Ru and RuO2 Electrodes during Oxygen Evolution – an operando soft X-ray Absorption Spectroscopy Approach"
<p>Time resolved in-situ X-ray absorption spectroscopy (XAS) in soft X-ray region was used to characterize polarized interphase on Ru and Ru oxide based electrodes under oxygen evolution reaction (OER) conditions. XAS spectra were used to align the type and population of oxygen-containing species formed at electrodes at anodic potentials with local electronic structure of the OER catalyst. The operando soft XAS data do not identify a single rate limiting process at potentials negative to 1.4 V vs Ag/AgCl. Individual intermediates of the oxygen evolution process coexist at the surface at potentials preceding the actual OER onset. The OER is accompanied with redistribution of the electron density resulting for a start of the catalytic cycle reflecting increased population of oxygen vacancies at the surface. The observed spectral behavior indicates a confinement of the OER to the coordination unsaturated sites (cus) at the surface. </p>
Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network
<p>Datasets acquired and generated for the manuscript "Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network". The datasets include test, training and time series datasets each containing the raw data and the predicted data where it applies. </p>
Dataset of "Sensitivity analysis in photodynamics: How the electronic structure controls cis-stilbene photodynamics?"
<p>The techniques of computational photodynamics are increasingly employed to unravel reaction mechanisms and interpret experiments. However, inaccuracies in nonadiabatic dynamics can lead to misinterpretations, particularly when calculated observables exhibit low sensitivity to the underlying dynamics. This issue is exemplified in the photochemistry of cis-stilbene, where similar experimental outcomes have been differently interpreted based on the electronic structures supporting nonadiabatic dynamics. This study examines the predictions of cis-stilbene photochemistry using trajectory surface hopping methods coupled with various electronic structures (OM3-MRCISD, SA2-CASSCF, XMS-SA2-CASPT2, and XMS-SA3-CASPT2) and assesses their ability to interpret experimental observations. Although the excited-state lifetimes show consistency, ranging from 360 fs to 295 fs, the reaction quantum yields vary significantly. The quantum yield for cyclization ranges from nearly zero to 35% while the photoisomerization channel can either exceed 50% or be entirely suppressed completely in the second case. Intriguingly, the calculated photoelectron signal is not strikingly different for different reaction scenarios, making the methods seemingly reliable when treated separately Furthermore, analyzing stationary points on the potential energy surface does not reliably predict simulation outcomes, nor does it aid in selecting a specific method before simulations. Therefore, we advocate for incorporating sensitivity analyses in the simulation protocol. While employing an ensemble of methods is impractical, nonadiabatic simulations with external bias present a resource-efficient approach to achieve this goal.</p>
NMR screen reveals the diverse structural landscape of a G- quadruplex library
<p>This is the NMR dataset for the manuscript '<span>NMR screen reveals the diverse structural landscape of a G-</span><br><span>quadruplex library</span>'</p> <p>Abstract</p> <p><span>G-quadruplexes are noncanonical nucleic acid structures</span><br><span>formed by stacked guanosine tetrads. Despite their functional and</span><br><span>structural diversity, a single consensus model is typically used to</span><br><span>describe</span><span> </span><span>sequences</span><span> </span><span>with</span><span> </span><span>the</span><span> </span><span>potential</span><span> </span><span>to</span><span> </span><span>form</span><span> </span><span>G-quadruplex</span><br><span>structures. We are interested in developing more specific sequence</span><br><span>models</span><span> </span><span>for</span><span> </span><span>G-quadruplexes.</span><span> </span><span>In</span><span> </span><span>previous</span><span> </span><span>work,</span><span> </span><span>we</span><span> </span><span>functionally</span><br><span>characterized each sequence in a 496-member library of variants of a</span><br><span>monomeric</span><span> </span><span>reference</span><span> </span><span>G-quadruplex</span><span> </span><span>for</span><span> </span><span>the</span><span> </span><span>ability</span><span> </span><span>to</span><span> </span><span>bind</span><span> </span><span>GTP,</span><br><span>promote a model peroxidase reaction, generate intrinsic fluorescence,</span><br><span>and to form multimers. Here we used NMR to obtain a broad overview</span><br><span>of the structural features of this library. After determining the</span><span> </span><span>1</span><span>H NMR</span><br><span>spectrum of each of these 496 sequences, spectra were sorted into</span><br><span>multiple classes, most</span><span> </span><span>of</span><span> </span><span>which could be rationalized based on</span><br><span>mutational patterns in the primary sequence. A more detailed screen</span><br><span>using representative sequences provided additional information about</span><br><span>spectral classes, and confirmed that the classes determined based on</span><br><span>analysis of</span><span> </span><span>1</span><span>H NMR spectra are correlated with functional categories</span><br><span>identified in previous studies. These results provide new insights into</span><br><span>the surprising structural diversity of this library. They also show how</span><br><span>NMR can be used to identify classes of sequences with distinct</span><br><span>mutational signatures and functions.</span></p> <p><span>Link to journal article: <a href="https://doi.org/10.1002/chem.202401437"><span>https://doi.org/10.1002/chem.202401437</span></a></span></p>
Structures of S-protein in complex with ligands deposited in the PDB between the 1st January 2021 and the 13th May 2021
<p>All 174 structures of SARS-CoV-2 S-protein in complex with a ligand released on the PDB between the 1<sup>st</sup> January 2021 and the 13<sup>th</sup> May 2021 were categorised by ligand type: hACE2, antibody Fab fragments, VHH antibody fragments or <em>de novo</em> designed peptide scaffolds. Information concerning the method by which the structures were determined and their resolution were retrieved from the PDB. The categorisation of ligands by S-protein binding site were achieved by visual analysis of all the structures using molecular visualisation software PyMOL, in which no new binding sites were found beyond those already categorised for the structures released on the PDB until the 1<sup>st</sup> January 2021 (10.5281/zenodo.5503855).</p> <p>The Pure project is funded by the European Union’s Horizon 2020 program under grant agreement No. 899732.</p> <p> </p>
List of the structures of S-protein in complex with ligands deposited in the Protein Data Bank until the 1st January 2021.
<p>All 131 structures of SARS-CoV-2 S-protein in complex with a ligand released on the PDB until the 1<sup>st</sup> January 2021 were categorised by ligand type: hACE2, antibody Fab fragments, VHH antibody fragments or <em>de novo</em> designed peptide scaffolds. The ligands’ amino acid sequences, the method by which the structures were determined and their resolution were retrieved from the PDB. Information regarding the ligands' production method, dissociation constants (K<sub>D</sub>), S-protein segment against which the K<sub>D</sub> were measured and the determination methods were retrieved from the respective references. The categorisation of ligands by S-protein binding site and listing of S-protein conformation in each structure were achieved by visual analysis of all the structures using molecular visualisation software PyMOL.</p>
Supplemental Information to Climate-driven habitat shifts of high-ranked prey species structure Late Upper Paleolithic hunting
<p>The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal <em>Scientific Reports</em>. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x.</p> <p><strong>Below is the legend for the Supplementary Information</strong>, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in <em>Scientific Reports</em>.</p> <p><strong>Supplementary 1</strong> – <em>UpperPaleoDietV4.html</em> – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions.</p> <p><strong>Supplementary 2</strong> – <em>Support Information 2.docx</em> – Word document containing supplementary tables 2 and 3.</p> <p><strong>Supplementary 3</strong> – <em>ArchaeoloigcalDataset_v8.csv</em> – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 4</strong> – <em>EuroUpperPaleoFaunas_v6.csv</em> – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 5 </strong>– <em>Lupo2016.csv</em> – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 6</strong> – <em>PushkinaRaia_FaunaWeights.csv</em> – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1.</p> <p><strong>Supplementary 7</strong> – <em>environmental_BG.csv</em> – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1.</p> <p>For more information on the data, methods, and results, please see the main paper. </p> <p> </p> <p> </p>
Chemical structures, Cell Painting and transcriptional profiles for compound bioactivity prediction.
<p>This is the related data, both input and produced for the paper <a href="https://doi.org/10.1101/2020.12.15.422887">"Predicting compound activity from phenotypic profiles and chemical structures"</a>.</p> <p>This data can be merged with <a href="https://github.com/CaicedoLab/2023_Moshkov_NatComm">paper's GitHub repository</a> for reproduction.</p> <p>Folders and files and are described below:</p> <pre><code>├── assay_data ├── assay_matrix_discrete_270_assays.csv Assay matrix with hits for assays (270) and compounds (16170). Note that this is the final file that we used to produce splits. ├── assay_metadata.csv Assay metadata ├── broad_ids.txt List of broad ids used in this study. That is an unfiltered list of compounds required by some analysis scripts. ├── smiles.txt Same as broad_ids.txt, but SMILES strings. ├── feature_data (for 16978 compounds, can be masked with ./misc/compounds16978to16170.npy) ├── cp.npz Classical chemical features ├── ge.npz Gene expression features ├── ge_scale.npz Gene expression scaled features ├── mo.npz Morphology features (not batch corrected) ├── mobc.npz Morphology features (batch corrected) ├── misc ├── compound_analysis.npz Compounds in the dataset identified as PAINS ├── compounds16978to16170.npy Used to filter features from the bigger set of compounds to the final one ├── fingerprints.npz Calculated fingerprints of compounds, those were then used to calculate similarity ├── similarity_fingerprints.npz Similarity matrix for compounds (16978) ├── population_normalized.csv.gz Well-level morphological profiles that were used for batch-correction ├── Table for PUMA Excel file with additional data and plots ├── predictions ├── scaffold_median(mean)_AUC.csv Aggregated median(mean) AUC scores over scaffold-based cross-validation splits. In the paper, median results were reported. ├── scaffold_median(mean)_EF.csv Aggregated median(mean) enrichment factor (EF) over scaffold-based cross-validation splits. In the paper, median results were reported. ├── toprank_chemical_cv{}_hitsnorm.csv Those files are needed to create enrichment plots and contain hit rate and top rank hit rate. ├── Each folder here stands for an experiment type, the number in the folder name is a number of the split. Inside each folder there are the following elements: ├── predictions Folder with predictions for each assay-compound pair for each modality ├── 2022_01_evaluation_all_data.csv File with AUC scores for each assay for the test set in the split ├── 2022_01_evaluation_all_data_EF.csv File with enrichment factor (EF) values for each assay for the test set in the split. Those files exist only for *chemical* folders. ├── assay_matrix_discrete_train(test)_old_scaff.csv Training and test subsets of data for the split. The first column contains broad_id. ├── assay_matrix_discrete_train(test)_old_scaff.csv Same, but SMILES strings in the first column. Those files are used as input to ChemProp! Experiments in this folder are the following: - chemical Scaffold-based 5-fold cross-validation splits, the main results in the paper are reported with this series of experiments. - chemical_bal Same splits as in chemical, but training were run with ChemProp built-in data balancing. - chemical_st Same splits as in chemical, but separate models were trained for each assay. - CV Random 5-fold cross-validation splits. - GE 5-fold cross-validation splits based on same-size clustering of gene expression features. - MOBC 5-fold cross-validation splits based on same-size clustering of batch-corrected morphology features. - random 10 random splits, ~80% of compounds in the training set and the rest in the test set. ├── splitting This folder contains numpy files which help to match compounds and features to create training and test sets for a split, which can be reused in the analysis notebook for data preparation. ├── scaffold_based_split.npz Splitting for scaffold-based splits. ├── random_split_{}.npz Random split indices of test set compounds (10 files). ├── cross_validation_indicies.npz Indices for random cross-validation splits ├── GE_clusters_size_constrained.npz Indicies of clusters of same-size clustering for gene-expression features. ├── MOBC_clusters_size_constrained.npz Indices of clusters of same-size clustering for batch-corrected morphology features.</code></pre> <p> </p>
A unified dataset of co-located sewage pollution, periphyton, and benthic macroinvertebrate community and food web structure from Lake Baikal (Siberia)
Sewage released from lakeside development can introduce nutrients and micropollutants that can restructure aquatic ecosystems. Lake Baikal, the world's most ancient, biodiverse, and voluminous lake, has been experiencing localized sewage pollution from lakeside settlements. Increasing filamentous algal abundance suggests benthic communities are responding to this localized pollution. We surveyed 40-km of Lake Baikal's southwestern shoreline 19-23 August 2015 for sewage indicators, including pharmaceuticals, personal care products, and microplastics with co-located periphyton, macroinvertebrate, stable isotope, and fatty acid sampling. Unique identifiers corresponding to sampling locations are retained throughout all data files to facilitate interoperability among the dataset's 150+ variables. The data are structured in a tidy format (a tabular arrangement familiar to limnologists) to encourage future reuse. For Lake Baikal studies, these data can support continued monitoring and research efforts. For global studies of lakes, these data can help characterize sewage prevalence and ecological consequences of anthropogenic disturbance across spatial scales.
Survey of high marsh plant structure and biomass for Spartina, Juncus, Borrichia and Batis specimens on Sapelo Island, Georgia during May to June 2015
From May 29th to June 27th of 2015, plant specimens of Spartina, Juncus, Borrichia, and Batis were extracted from Georgia Coastal Ecosystems Study Site 6 and brought to the UGA Marine Institute research center for structural and biomass measurements. The focus of the structural measurements were to capture the top-down horizontal length and branching angles of rhizomes connecting the above-ground plant shoots. The height of plant shoots were also measured, along with the dry-weight mass of all measured plant components. The purpose of these measurements was to provide data that will be the basis for parameter values used in the Virtual Prairie agent-based model for clonal plant growth and competition. Note that a web-based index of plant photographs from this study are available at http://gce-lter.marsci.uga.edu/public/datasets/ancillary/PLT-GCET-1508/.
MCR LTER: Coral Reef: Asynchrony in coral community structure contributes to reef‑scale community stability, data for Srednick et al., Nature 2023
These data were generated in support of the manuscript: Srednick G, Davis K, and Edmunds P, Nature To evaluate whether spatial insurance effects are important on coral reefs, we explored variation over 2006–2019 in coral community structure and environmental conditions in Moorea, French Polynesia. We studied coral community structure at a single site with fringing, back reef, and fore reef habitats, and used this system to explore associations among community asynchrony, asynchrony of environmental conditions, and community stability. The daily range in seawater temperature among habitats suggests it could be a factor contributing to the variation in coral community structure. Wave-forced seawater flow facilitated larval exchange among connected habitats, differing in strength among years, and accentuated periodic connectivity among habitats at 1-7 year intervals. At this site, connected habitats harboring taxonomically similar coral assemblages and exhibiting asynchronous population dynamics can provide insurance against extirpation and may promote community stability. If these effects apply at larger spatial scale, then among-habitat community asynchrony is likely to play an important role in determining reef-wide coral community resilience. This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
SBC LTER: Reef: Benthic community structure along a gradient of historic kelp variability
These data are estimates of biomass of approximately 225 taxa of reef algae, invertebrates, and fish in transects at 11 non-core sites in the Santa Barbara Channel in summer 2018 (3 transects per site). Sites were selected along a gradient of historic kelp (Macrocystis pyrifera) variability, from sites with highly persistent kelp to sites exhibiting extensive variation in kelp biomass since 2008. See the site characteristics data table for site locations and depths. The purpose of the sampling was to explore to what extent the findings from the long-term experiment (e.g. Castorani et al. 2018) apply to natural gradients in kelp persistence. Surveys were conducted following the same methodology used in the annual surveys of kelp forest community structure, such that data from the non-core sites may be paired with annual survey data collected in summer 2018.
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.