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Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Dataset 1. Functional genomic profiling of O-GlcNAc reveals its context-specific interplay with RNA polymerase II.
<p>This dataset is used in the bioformatic methods of the article <strong>Functional genomic profiling of O-GlcNAc reveals its context-specific interplay with RNA polymerase II </strong>(doi: 10.1186/s13059-025-03537-2). The corresponding code can be found in the repository https://github.com/descostesn/chromglycomethods.git</p>
Data from "Exploring Gut Microbiota Profile Induced by Antipsychotics in Schizophrenic Patients: Insights from an Eastern European Pilot Study", Nita (Ilie) et al. 2025
<p>Dataset containing raw demultiplexed FASTQ files of the sequenced samples, generated by the Illumina MiSeq platform. </p> <p>MiSeq_demultiplexed-V3_V4-HC_SCZ.zip - MiSeq raw sequences of the V3-V4 region 16S rRNA gene from subject fecal material. This ZIP file contains the FASTQ files of the paired-end reads (R1: forward reads; R2: reverse reads) produced for each sample using the MiSeq platform.</p> <p>metadata-HC_SCZ.csv - The list of sequenced samples and associated metadata.</p>
AREX 2017 selected hydrographic profiles: CTD and oxygen concentration in the south-west Spitsbergen area
<p>Data from AREX 2017 cruise aboard rv Oceania collected in July 2017 on the south-western Svalbard shelf in the MathWorks Matlab table and Microsoft Excel formats.</p> <p>This data selection was used and described in Vivier et al., 2024: Dense water production in Storfjorden, Svalbard, from a one-year time series of observations and a simple 3 model: Are polynyas in a warming Arctic exporting heat to the deep ocean? (for publication in JGR Oceans).</p> <p>The sampling took place during the Arctic Experiments AREX carried out by the Institute of Oceanology, Polish Academy of Sciences (IO PAN), Sopot, Poland since 1987 until present.</p> <p>During the AREX 2017 cruise CTD (conductivity, temperature, depth) vertical profiles were taken from surface to bottom with Sea-Bird Electronics, Inc. SBE 911plus CTD system (composed of duplicate SBE 3plus Temperature sensors, SBE 4C Conductivity sensors, and a Digiquartz® Pressure sensor, all calibrated by the manufacturer before each cruise). In addition to standard sensors, the system was equipped with the SBE43 dissolved oxygen sensor. Raw data were then filtered and processed with the SBE Data Processing Software (averaged to 1 dbar intervals) and further analysed and visualised in the MATLAB and exported to Excel (variables were rounded, blank values kept). Raw data are available from the IO PAN, Sopot, Poland (https://www.iopan.pl/).</p>
Bibliometric data for sports performance profiling
Open the record for dataset details and reuse information.
GC-MS metabolite profile of Pseudocercospora fijiensis exposed to thiabendazole
Open the record for dataset details and reuse information.
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples - Time course data
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes only the 10X Flex time course data and analysis.</strong></p>
FluoroProbe profiles
<p>FluoroProbe profile data associated with a manuscript submitted to Water Resources Research:</p> <p>Estimation of <em>Microcystis</em> buoyancy by calibrating a numerical model to in-situ observations </p> <p>Mark D. Rowe1*, Alex Kain2, Casey Godwin2, Ashley M. Burtner</p> <p>1. NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA<br>2. Cooperative Institute for Great Lakes Research, University of Michigan, Ann Arbor, MI, USA</p> <p>*Corresponding author: mark.rowe@noaa.gov</p>
Revisiting the Density Profile of the Fuzzy Sphere Model for Microgel Colloids v2
<p>This Zenodo repository contains data supporting the paper “Revisiting the density profile of the fuzzy sphere model for microgel colloids”, Frank Scheffold, Soft Matter 2024, DOI: 10.1039/d4sm01045k </p>
Datasets corresponding to "Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets"
<p><strong>Datasets corresponding to "Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets"</strong></p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>codeforclassification.zip</strong></p> <p>Code for applying model on unlabelled data. Test data is contained in 'sampledata.zip'</p> <p> </p> <p> </p> <p> </p>
Physical Profiling
<p>Profiling of physical capacity and physical performance in training and match in academy and first team football players.</p>
Key Resource Table associated with "Endo-IP and Lyso-IP Toolkit for Endolysosomal Profiling of Human Induced Neurons"
<p>This entry contains a Key Resource table associated with: https://doi.org/10.1101/2024.09.24.614704</p>
Machine-learning-based validation of Microsoft Azure Kinect in measuring gait profiles
<p>The dataset was used to validate Microsoft Azure Kinect in measuring gait profiles, employing machine learning techniques to investigate the impact of residual errors due to environmental, methodological, and processing factors on the accuracy of gait profile assessments. Data were collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system with MAK, and corresponding gait profiles were estimated and compiled into a dataset. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathological gait characteristics.</p> <p>Contact e-mail:</p> <p>claudia.ferraris@cnr.it (Claudia Ferraris)</p> <p>lucavisma@hotmail.com (Luca Vismara)</p> <p>veronica.cimolin@polimi.it (Veronica Cimolin)</p>
Data from: Anopheles gambiae: metabolomic profiles in sugar-fed, blood-fed and Plasmodium falciparum-infected midgut
The mosquito midgut is a physiological organ essential for the nutrient acquisition as well as an interface that encounters various mosquito borne pathogens. Metabolomic characterization would reveal biochemical fingerprints that are generated by various cellular processes. The metabolite profiles of the mosquito midgut will provide an overview of the biochemical events in both physiological states and the dynamic responses to pathogen infections. In this study, the midgut metabolic profiles of Anopheles gambiae mosquitoes following feeding with sugar, human blood, mouse blood, and Plasmodium falciparum-infected human blood were examined. A mass spectrometry system coupled to liquid and gas chromatography produced a time series of metabolites in the midgut at discrete conditions (sugar feeding, 24hr and 48hr post normal blood and P. falciparum-infected blood feeding). Triplicates were included to ensure system validity. A total of 512 individual compounds were identified, 511 were assigned to 8 super-pathways and 75 sub-pathways. The dataset can be used for further inquiry into the metabolic dynamics of sugar and blood digestion and of malaria parasite infection.
Analysis of DNA methylation profile of Korean chronic lymphocytic leukaemia (CLL) patients with MeDIP-Seq
<p>In this study, we performed genome-wide methylation profiling of eight CLL patients and five control subjects, in an Asian cohort. Methyl-CpG-binding domain sequencing (MBD-Seq) was used as the profiling method. Differential methylation analysis identified a number of differentially methylated regions (DMRs) and genes (DMGs). More regions were hypomethylated than were hypermethylated. Promoters contained the highest proportion of DMRs, while distal intergenic and intron regions contained the largest number of DMRs. Gene Ontology, pathway analysis, and network-based prioritization of DMGs were also performed.</p>
Primary-level worker interventions for the care of mental disorders and distress in low- and middle-income countries - GRADE evidence profiles
<p>This file includes all GRADE evidence profiles for the following Cochrane review:</p> <p>van Ginneken N, Chin WY, Lim YC, Ussif A, Singh R, Shahmalak U, Purgato M, Rojas-García A, Uphoff E, McMullen S, Foss HS, Thapa Pachya A, Rashidian L, Borghesani A, Henschke N, Chong L-Y, Lewin S. Primary-level worker interventions for the care of mental disorders and distress in low- and middle-income countries. Cochrane Database of Systematic Reviews. In press</p>
Vertical temperature profiles obtained from Venus Express and Akatsuki radio occultation data using FSI
<p>Vertical temperature profiles of Venusian atmosphere obtained from selected radio occultation data taken in ESA's Venus Express and JAXA's Akatsuki missions. The temperatures were retrieved using a radio holographic method, Full Spectrum Inversion (FSI). The data list and format are given in two Excel files and the data are given in text files with an extension ".dat".</p>
Protein Identification by Nanopore Peptide Profiling
<p>This dataset belongs to “Protein Identification by Nanopore Peptide Profiling” and describes the raw data and analysis of tryptic digested peptides translocating through a mutant Fragaceatoxin C nanopore. A jupyter notebook describing the analysis and structure is added to this dataset.</p> <p> </p> <p><strong>Data description:</strong></p> <p><strong>Protein Identification by Nanopore Peptide Profiling.ipynb</strong></p> <p> Jupyter notebook contained data analysis of data contained in data_0.zip and data_1.zip (Python 3.7)</p> <p><strong>python_scripts.zip</strong></p> <p> Supplementary scripts belonging to “Protein Identification by Nanopore Peptide Profiling.ipynb”. See explanation of custom classes in the jupyter notebook.</p> <p><strong>data_0.zip</strong> - Folder containing raw electrophysiology data and result after analysis with “Protein Identification by Nanopore Peptide Profiling.ipynb”, with each folder containing the following:</p> <p> Alpha casein: Tryptic digest of alpha casein</p> <p> Beta casein: Tryptic digest of beta casein</p> <p> BSA: Tryptic digest of bovine serum albumin</p> <p> Control: Tryptic digest of water (no protein, control measurement)</p> <p> Cytochrome c: Tryptic digest of cytochrome c</p> <p> DHFR_His6: Tryptic digest of dihydropholate reductase (His6 tagged)</p> <p> EFP: Tryptic digest of elongation factor P</p> <p> HMW1Act: Tryptic digest of high molecular weight adhesin protein</p> <p><strong>data_1.zip</strong> - Folder containing raw electrophysiology data, comma-separated MS peptide masses, and result after analysis with “Protein Identification by Nanopore Peptide Profiling.ipynb”, with each folder containing the following:</p> <p> Lysozyme: Tryptic digest of lysozyme </p> <p> PAN: Tryptic digest of proteasome-activating nucleotidase</p> <p> TbpA_Y27A: Tryptic digest of periplasmic binding protein</p> <p> Trypsin: Tryptic digest of bovine trypsin</p> <p> Mass_spec: csv files containing measured ESI-MS peptides</p> <p> Lysozyme synthetic peptides: Synthetic peptides:</p> <p> Lys1: TPGSR</p> <p> Lys2alk: C(+57.02)ELAAAMK</p> <p> Lys3: HGLDNYR</p> <p> Lys4alk: WWC(+57.02)NDGR</p> <p> Lys5: GTDVQAWIR</p> <p> Lys6alk: GYSLGNWVC(+57.02)AAK</p> <p> Lys7: FESNFNTQATNR</p> <p>The structure of the data files is registered data_1.zip in <strong>'index.csv'</strong> (digested proteins) and <strong>'index</strong><strong>_peptides</strong><strong>.csv'</strong> (synthetic peptides) contained in the data folder. In this file, we describe the protein that was measured as well as the folder location and the expected baseline / standard deviation.<br> <br> <strong>Structure of <em>./data/index.csv</em></strong></p> <p><strong>Protein (string) | Folder (string) | Baseline (pA) (float) | Baseline Error (pA) (float)</strong></p> <p> </p> <p>In each <strong>Folder</strong>, there is another <strong>'index.csv'</strong>, explaining which files are with protein and which are without (blank).<br> <br> <strong>Structure of <em>./data/[protein]/[repeat]/index.csv</em></strong></p> <p><strong>blank (boolean) | fname (string)</strong></p> <p> </p> <p>Each folder in data_0.zip and data_1.zip contains a folder for each measure protein, which contains a folder for each repeat. The repeats contain raw axon binary files (.abf), each file contains measurement conditions as follows:</p> <p> [Date of measurement]_[Pore type]_[Buffer conditions]_[added analyte(s)]_[operator initials]</p> <p> <em>e.g</em>: 20200312_1M_KCl_50mM_Citricacid_50mM_BTP_pH_38_FraC_G13F_neg70mV_20ul_CytC_TrypsinGold_FL_0000</p> <p> Measured on 12-03-2020, in 1M KCl buffered with Citricacid (50 mM) adjusted using bis-tris-propane to pH 3.8, using Fragaceatoxin C mutant G13F at a negatively applied potential of 70 mV. 20 µL cytochrome c was added to the cis compartment.</p> <p>The total volume of the container used for all electrophysiology experiments was 400 µL, all samples were prepared at a 1 g/L concentration. A prefix “perf” before analyte description indicates that the chamber was flushed with approximately 2 mL fresh buffer prior to analysis. The buffer condition "BTP" means bis-tris-propane, which is used to titrate to the exact pH of 3.8.</p> <p>Each analysed folder contains<strong> results.pkl</strong> file, containing the analysis result as provided by “Protein Identification by Nanopore Peptide Profiling.ipynb” - see the jupyter notebook</p> <p>Each analysed folder contains <strong>results_analysis.xlsx</strong>, which contains sheets with excluded currents, standard deviations, dwell time and beta value for the pore without analyte added “Blank” and results from the analyte added in “Results”. Parameters used for fitting are contained in “Parameters”. The “Histograms” tab shows the raw data of the excluded current spectra.</p> <p> </p> <p><strong>mass_spec_peaks.zip</strong> – Folder containing mass spectrometry files as analysed by PEAKS Studio</p> <p>The folder contains an subfolder for each protein measured using electrospray ionisation mass spectrometry (ESI-MS).</p> <p> acasein: alpha casein protein</p> <p> b_casein: beta casein protein</p> <p> BSA: bovine serum albumin</p> <p> CytC: cytochrome C digested</p> <p> DHFR: dihydropholate reductase</p> <p> HMW1_Act: high molecular weight adhesin protein</p> <p> PAN: proteasome-activating nucleotidase</p> <p> ThBP: periplasmic thiamine binding protein</p> <p> Trypsin: bovine trypsin</p>
Single-cell profiling of human bone marrow progenitors reveals mechanisms of failing erythropoiesis in Diamond-Blackfan anemia
<p>Ribosome dysfunction underlies the pathogenesis of many cancers and heritable ribosomopathies. Here we investigate how mutations in either ribosomal protein large (RPL) or ribosomal protein small (RPS) subunit genes selectively affect erythroid progenitor development and clinical phenotypes in Diamond-Blackfan anemia (DBA), a rare ribosomopathy with limited therapeutic options. Using single-cell assays of patient-derived bone marrow, we delineated two distinct cellular trajectories segregating with ribosomal protein genotypes: almost complete loss of erythroid specification were observed in <em>RPS</em>-DBA. In contrast, we observed relative preservation of qualitatively abnormal erythroid progenitors and precursors in <em>RPL</em>-DBA. Although both DBA genotypes exhibited a pro-inflammatory bone marrow milieu, <em>RPS</em>-DBA was characterized by erythroid differentiation arrest, whereas <em>RPL</em>-DBA was characterized by preserved GATA1 expression and activity. Compensatory stress erythropoiesis in <em>RPL</em>-DBA exhibited disordered differentiation underpinned by an altered glucocorticoid molecular signature, including reduced <em>ZFP36L2</em> expression<em>,</em> leading to milder anemia and improved corticosteroid response. This integrative analysis approach identified distinct pathways of erythroid failure and defined genotype-phenotype correlations in DBA. These findings may help facilitate therapeutic target discovery.</p> <p> </p>
Imaging Mass Cytometry for high-dimensional tissue profiling in the eye
<p>Imaging mass cytometry data (folders containing single tiffs + cell masks) generated for the analysis of healthy conjunctiva and conjunctival melanoma.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.