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1,456 results for “residualization”
Mining and Metallurgical Residue Database
<p><span>The dataset includes 44 relevant data attributes from 64 mining and metallurgical sites in 27 countries. </span></p>
LDEO pCO2-Residual Method
<p>The ocean reduces human impacts on global climate by absorbing and sequestering CO<sub>2</sub> from the atmosphere. To quantify global, time-resolved air-sea CO<sub>2</sub> fluxes, surface ocean pCO<sub>2</sub> is needed. A common approach for estimating full-coverage pCO<sub>2</sub> is to train a machine learning algorithm on sparse in situ pCO<sub>2</sub> data and associated physical and biogeochemical observations. Though these associated variables have understood relationships to pCO<sub>2</sub>, it is often unclear how they drive pCO<sub>2</sub> outputs. Here, we make two advances that enhance connections between physical understanding and reconstructed pCO<sub>2</sub>. First, we apply pre-processing to the pCO<sub>2</sub> data to remove the direct effect of temperature. This enhances the biogeochemical/physical component of pCO<sub>2</sub> in the target variable and reduces the complexity that the machine learning must disentangle. Second, we demonstrate that the resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of pCO<sub>2</sub>. The final pCO<sub>2</sub> reconstruction agrees modestly better with independent data than most other approaches. Uncertainties in the reconstructed pCO<sub>2</sub> and impacts on the estimated CO<sub>2</sub> fluxes are quantified. Uncertainty in piston velocity drives substantial flux uncertainties in some regions, but does not increase globally integrated estimates of uncertainty in CO<sub>2</sub> fluxes from observation-based products. Our reconstructed CO<sub>2</sub> fluxes show larger interannual variability than smoother neural network approaches, but a lesser trend since 2005. </p>
Long-term trends in pesticide residues and physical chemical parameters of superficial water samples with accompanying macro-benthic invertebrate community surveys from the Palo Verde National Park, Costa Rica: 1993-1994; 2001; 2004-2005; 2009-2011
During the years 1993-1994, 2001, 2003-2005 and 2009-2011, the Central American Institute for Studies on Toxic Substances (IRET-UNA) executed independent research projects which quantified the presence of pesticide residues on superficial water samples from the Palo Verde National Park (PVNP) and surrounding areas. The PVNP (5460 sq km) is a RAMSAR wetland of international importance, which has been subjected to pesticide pressure from agricultural fields (mainly rice and sugarcane) since the 1960s and 1970s. In 1993, the PVNP wetlands were placed on the RAMSAR Montreux Record, indicating that it was considered an “impaired ecosystem” due to ecotoxicology concerns. Water is the key component of all issues regarding the biodiversity, management, restoration, and economic development of this region. Therefore, water quality is a critical component of many social ecological discussions and research efforts. This data package contains uniform pesticide, biological and water quality data from all PVNP wetland projects (1993- 2011) in order to present long-term trends in the environmental water quality and accompanying biological patterns for this conservation area. Study sites were spatially determined to compare clean upstream waters with a gradient of pesticide-affected waters. Superficial water samples were collected at various sites for chemical (pesticide) analysis and water quality parameters were recorded in situ for environmental monitoring. Corresponding biological sampling was completed to survey benthic macroinvertebrate communities and compare with local eco-toxicological profiles. This data package contains information from four separate projects.
Overwintering Fires from 2009-2010 Burns near Fairbanks, Alaska: Residual Soil Organic Layer Depth, Burn Depth and Thaw Depth Collected 2023
This dataset contains data from adjacent overwintering and single-season burn sites. For the overwintering fires, we targeted locations that had burned in the summers of 2009, smouldered through the winter months, and reignited in 2010. Adjacent to these overwintering sites, we identified single-season burn sites from within portions of the 2009 fires that were unaffected by overwintering. A total of seven overwintering fire sites and four single-season fire sites were sampled. Within each site, three plots were established. Data inlcudes within plot measurments of post-fire seedling composition and density, residual SOL, burn depth estimated by black spruce adventitious roots, thaw depth, and pre-fire tree species composition and estimates of combustion. This is one of three packages from this project; this one contains the soils data.
The role of injection method on residual trapping at the pore-scale in continuum-scale samples: segmented data
<p>The experiments in this work explore the role of a variable injection rate on gas saturation and residual trapping. There are 2 experiments in this work H2L (high to low injection rate) and L2H (low to high injection rate). The workflow for processing the micro-CT images to get the segmented images is described in [1]. </p><p>The following scans are included in this repository NB. all data for this repository is segmented micro-CT data.: </p><ol><li>Dry scan prior to experiment = merged_binning_2_38_1927</li><li>H2L during high flow = merged_segmented_flow_09_h2lh_merged</li><li>H2L during low flow = merged_segmented_flow_11_h2ll_2_merged</li><li>H2L at the end of drainage (no flow) =merged_segmented_flow_16_dra1_pd5_merged</li><li>H2L at the end of imbibition (no flow) =merged_segmented_flow_21_imb1_pi1_merged</li><li>L2H during low flow = merged_segmented_flow_29_2_l2hl_merged</li><li>L2H during high flow = merged_segmented_flow_30_l2hh_merged</li><li>L2H at the end of drainage (no flow) =merged_segmented_flow_31_dra2_pd1_merged</li><li>L2H at the end of imbibition (no flow) =merged_segmented_flow_33_imb2_pi1_merged</li></ol>
Data Associated with Chemical Cartography with APOGEE: Two-process Parameters and Residual Abundances for 288,789 Stars from Data Release 17
<p>Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity log(g) in 17 elemental abundances of 288,789 evolved stars from the SDSS APOGEE survey. We fit these corrected abundances as the sum of a prompt process tracing core-collapse supernovae and a delayed process tracing Type Ia supernovae, thus recasting each star's measurements into the amplitudes A_cc and A_Ia and the element-by-element residuals from this two-parameter fit. Here we present the log(g)-calibrated abundances, fit parameters, process amplitudes, and element-by-element abundance residuals of 288,789 stars (310,427 spectra) in APOGEE DR17 that accompany <a href="https://arxiv.org/abs/2403.08067" target="_blank" rel="noopener">the paper</a>.</p> <p>calibration_values_final.dat contains all derived calibration offsets, including the grids of log(g) calibration offsets and zero-point offsets for two-process model analysis. The first five rows of this catalog are reproduced in Table 2 of the paper.</p> <p>logg_calib_example.ipynb is a Jupyter notebook containing Python code to load calibration_values_final.dat, extract the log(g) calibration offsets for specific element, and apply calibration offsets to 10 sample stars.</p> <p>2process_residual_abund_catalog_final.fits is the catalog of 310,427 APOGEE DR17 spectra (288,789 unique stars) containing calibrated abundances, two-process fit parameters, and abundance residuals. A full listing of columns in this catalog is given in Table 5 of the paper.</p> <p>catalog_examples.ipynb is a Jupyter notebook containing Python code to load 2process_residual_abund_catalog_final.fits, cross match with other catalogs (using AstroNN and the APOGEE DR17 Globular Cluster Value-Added Catalog as examples), and make some example plots utilizing the cross-matched data.</p>
The stellar parameters and the quantities of the residual emissions of the detected active stars in the LAMOST-K2 survey
<p>The full Table 1 in <em>Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey</em> (Xiang, Gu & Cao, 2022, MNRAS, 514, 4781; <a href="https://arxiv.org/abs/2206.07257">arXiv:2206.07257</a>). The columns are LAMOST obsid, K2 ID, Teff, logg, [Fe/H], EW_res_Halpha, EW_res_Ca II 8498, EW_res_Ca II 8542, EW_res_Ca II 8662, log F_Halpha, log F_Ca, log R'_Halpha, log R'_Ca. The chromospheric emissions were detected and measured with the spectral subtraction technique, which removes the inactive template spectra (photospheric contribution) from the observed stellar spectra. In this work, we used the variational autoencoder neural networks to efficiently generate the proper template spectra in a data-driven manner. More details can be found in the associated paper (<a href="https://arxiv.org/abs/2206.07257">https://arxiv.org/abs/2206.07257</a>). The demo code can be found on GitHub (<a href="https://github.com/xylib/vae-for-spectroscopic-survey">https://github.com/xylib/vae-for-spectroscopic-survey</a>).</p>
Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis
<p>Tables included in the article "Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis"</p>
Variability and bias in measurements of metals mass fractions in automobile shredder residue
<p>Measured mass fractions of various metals in individually digested test samples of automobile shredder light fraction (single_digestions_ppm.csv) and the calculated means and standard deviations of these (mean_sd_ppm.csv). For all metadata see accompanying readme file Loevik2019_metal_mass_fractions_in_automobile_SLF_Readme.txt.</p>
A Novel Framework to Harmonise Satellite Data Series for Climate Applications: Matchups, Calibration Parameters and Residuals
<p>The datasets included with this archive supplement the journal article:</p> <p>Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. <em>Remote Sens. 2019</em>, <strong>11</strong>, 1002. doi:<a href="https://doi.org/10.3390/rs11091002">10.3390/rs11091002</a>.</p> <p>The archive includes a README file with further explanations.</p>
Dataset: Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment
<p>This dataset and these scripts supports the article 'Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment' as published in Cleaner Waste Systems. https://doi.org/10.1016/j.clwas.2024.100154</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we focused on exploring the potential of large-scale composting of rose waste in Kenyan rose cultivation. The objective of this study was to examine the potential of composting rose waste in this large-scale commercial setting with low operational costs, exploring its benefits and challenges.</p> <p>In piles of 4000 kg green waste the evolution of three mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the pesticide residue levels of mature rose waste were assessed. </p>
Database of Residual Stress Measurements on Hot-rolled Wide Flange Steel Cross Sections
<p><a href="https://zenodo.org/deposit/7677600#:~:text=Delete-,Data_info.csv,-md5%3A98a0f787ce2ea1b81d42ac898f6bb110">Data_info.csv</a>: Database of 'Residual Stress Measurements on Hot-rolled Wide Flange Steel Cross Sections' including cross-sectional and material characteristics as well as information relevant to ploting the residual stress distributions.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=7%20kB-,Distributions.zip,-md5%3Af7f66a9ad607f27edde3dc7438b82ad2">Distributions.zip</a>: Residual stress distributions for the web and the flanges. To be unziped and positioned at the same location with the 'Data_info.csv', 'Processor.m' and 'QP_Coefficients' folder.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=197%20kB-,Processor.m,-md5%3A29935e24d40cbd4398d260124ec71fa8">Processor.m</a>: MATLAB code that plots the residual stress distributions of a selected research work.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=16%20kB-,QP_Coefficients.zip,-md5%3Aafd1a8771cf39c9c6584331d10030d96">QP_Coefficients.zip</a>: Coefficients of a proposed optimization method to fit the measured residual stresses in the web and the flanges. To be unziped and positioned at the same location with the 'Data_info.csv', 'Processor.m' and 'Distributions' folder.</p>
Seedling emergence and biomass data of nine dryland plant species characterizing the impact of soil residual auxin herbicide across two soil types and water pulse events on greenhouse growth; Las Cruces, New Mexico, Spring 2021.
Synthetic-auxin herbicides are often used to control woody plants and aid in grassland restoration. Seed-based restoration is common alongside herbicide applications and there may be unintended effects of these herbicides on dryland plant species at the seed and seedling stages. Additionally, abiotic conditions at the time of herbicide application may influence herbicide-soil-plant interactions. We conducted a greenhouse study to examine the effects of a common shrub-control herbicide mix and its interaction with soil type and a post-herbicide water pulse on common desert plant seeds and seedlings. In this greenhouse study, we found that a subset of species responded negatively to soil residual herbicide activity of a mixture of aminopyralid, clopyralid, and triclopyr at the seed and seedling stages. Species sensitive to soil herbicide residues were primarily shrub and forb species that are often the target species of herbicide applications for woody plant control, such as Prosopis glandulosa (honey mesquite) and Larrea tridentata (creosote bush). However, two shrub species (Atriplex canescens [four-wing saltbush] and Yucca elata [soaptree yucca]) and one perennial grass species (Digitaria californica [Arizona cottontop]), which are used in dryland restoration projects, were found to be particularly sensitive to soil residual herbicide activity. Thus, if using these herbicides to control woody plants and restore herbaceous vegetation via active seeding or relying on the in situ seed bank, considerations should be given to what species are used in the seed mix, what species are already present in the soil seed bank, and other details of the circumstances of herbicide application.
Contour method and neutron diffraction dataset to determine the weld fusion zone shape on residual stress in submerged arc welding
<p>This is a dataset which formed the basis for "The effect of the weld fusion zone shape on residual stress in submerged arc welding" by A. Ishigami, M. J. Roy, J. N. Walsh and P. J. Withers appearing in the Journal of Advanced Manufacturing Technology.</p> <p>Two X-grade steel specimens with different high speed, submerged arc welds with very slight differences in fusion zone shape were compared with a novel contour method application as well as with neutron diffraction. Neutron diffraction was carried out with the SALSA instrument at the Institut Laue-Langevin in Grenoble, France with the assistance of T. Pirling. Data files with 441 in the descriptor refer to 'conventional' parameters (see publication), while 241 refers to 'new'.</p> <p>Provided in this dataset are four *.dat files, which contains data is in the form of a point cloud with one point per line, whitespace delimited in microns. Data was captured with a Nanofocus CF-4 laser profilometer sensor with point spacing 30 µm apart. Data with z coordinates below or above 500 µm are considered outside of the surface detection limits.</p> <p>Also included is an Excel worksheet, which contains the calculated residual stresses as found with LAMP (https://www.ill.eu/instruments-support/computing-for-science/cs-software/all-software/lamp/). Raw data is available here:</p> <p>P. J. Withers, A. Ishigami, T. Pirling, M. Roy, J. Walsh (2014). The effect of weld bead shape on residual stress in novel low heat input welding of steel [Data set]. ILL. http://doi.ill.fr/10.5291/ILL-DATA.1-02-145</p> <p>The authors would like to thank JFE Steel Corporation for both direct and in-direct support of this research. The authors would also like to thank the Institut Max von Laue-Paul Langevin for the allocation of beamtime at SALSA and gratefully acknowledge the help of Thilo Pirling for his assistance in performing the neutron diffraction experiments. A. Ishigami would like to thank Kenji Oi for his support of this research. M. J. Roy would like to thank Ian Winstanley for his assistance in performing the contour cuts. M. J. Roy acknowledges financial support from the EPSRC (EP/L01680X/1) through the Materials for Demanding Environments Centre for Doctoral Training.</p>
Montenegro results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. </p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2022 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2021 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2020 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2019 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2018 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2017 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Dataset and code to reproduce analysis on the impact of indoor residual spraying (IRS) on malaria at Illovo Nchalo, Malawi
<p><strong>V3 edit: </strong>The latest R file contains extra lines of code to produce prediction intervals. </p> <p> </p> <p><strong>The repository contains:</strong></p> <p>- Excel sheets for each round of indoor residual spraying from 2014 - 2018 for villages based on the Illovo Nchalo Estate (provided by public health officer)</p> <p>- Weather data for 1999 - 2019 downloaded from Sasri Weather web for Malawi - Illovo Nchalo (Open access after signing up)</p> <p>- Explanation of variables downloaded from Sasri Weather Web</p> <p>- Expected population: number of residents living in Illovo clinic's catchment areas based on 2016 and 2019 census. Linear interpolation for the other years</p> <p>- Malaria data per month per clinic from the public health officer's records at Illovo Nchalo for 7 clinics for 2014 - 2018</p> <p>- Malaria data downloaded and selected from DHIS2 (access upon request and approval)</p> <p>- R file to reproduce figures, tables, and results for the paper under submission for PLOS GPH</p> <p>- Geopackages of data that is not open-source already to reproduce the map in figure 1</p> <p> </p> <p><strong>Description of IRS data:</strong></p> <p>- Village: Name of the villages based at Illovo being targeted for IRS</p> <p>- Target_spray: Number of structures within the village targeted for spraying</p> <p>- Sprayed: Number of structures actually sprayed</p> <p>- Date_start: Start date of the IRS campaign in a village</p> <p>- Date_end: End date of the IRS campaign in that village</p> <p>- Coverage_p: Percentage of structures sprayed calculated from "target_spray" and "sprayed"</p> <p> </p> <p><strong>Notes on reconciling the different years of IRS:</strong></p> <p>1. Post office and D. compound have been added to Nkombedzi</p> <p>2. B compound has been added to Riverside/Mess</p> <p>3. The following villages attend the following clinics</p> <p> </p> <p><strong>The following villages attend the assigned clinics:</strong><br>- Mess and Bonksville -> Factory<br>- Mlambe and Paxman -> Mangulenje<br>- Sande Ranch -> Lengwe<br>- Mechanical Pool -> Mwanza</p> <p> </p> <p><strong>Description of the malaria data:</strong></p> <p>- Date, month, year</p> <p>- Time_dummy: 1 to 48, over the study period</p> <p>- Village: The name of the village the clinic is based in. In further analyses, this is referred to as "clinic" instead to avoid confusion.</p> <p>- Total_cases: total number of cases testing positive for malaria by RDT, or in a very small percentage of cases microscopy (only used when RDT gives inconclusive or conflicting results, or when symptoms persist with negative RDT). Cases_on + cases_off = total_cases</p> <p>- Cases_on: Number of malaria cases from residents of villages located within the boundaries of the Illovo estate</p> <p>- Cases_off: Number of malaria cases from residents of villages located (just) outside the boundaries of the Illovo estate</p> <p>- Total_patients: Total number of patients attending the clinic that month</p> <p> </p> <p>From the selected control clinics only "WHO NMCP P Confirmed malaria cases" was used to indicate the number of malaria cases and "CMED Total Population" to indicate the clinic catchment population. Further info on DHIS2 website. </p> <p> </p> <p>For further information don't hesitate to contact Remy Hoek Spaans. </p> <p> </p> <p> </p> <p> </p>
Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations
<p>SD-WACCM data used in "<strong>Climatology of Mesosphere and Lower Thermosphere Residual Circulations and Mesopause Height derived from SABER Observations"</strong></p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</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.