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13,343 results for “Primary”
CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization
<p>Overview:<br>----------<br>CEDAR-GPP is a global Gross Primary Productivity (GPP) data product, including monthly GPP estimates at 0.05º spatial resolution. These datasets were generated via upscaling eddy covariance measurements with machine learning and satellite data. CEDAR-GPP uniquely incorporated the direct CO2 fertilization effect (CFE) using both data-driven and theoretical approaches. GPP estimates were produced from ten different model setups that vary by temporal span, direct CFE incorporation method, and GPP partitioning approaches. CEDAR stands for ups<strong>C</strong>aling <strong>E</strong>cosystem <strong>D</strong>ynamics with <strong>AR</strong>tificial intelligence.</p> <p>CEDAR-GPP consists of GPP estimates from ten model setups, differing by temporal range, methods for quantifying CO2 fertilization effects, and the partitioning methods used to derive GPP from eddy covariance measurements. Users are encouraged to refer to the user manual for a structured approach to selecting the most appropriate dataset.</p> <p> </p> <p>Authors:<br>----------<br>Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, Trevor Keenan</p> <p> </p> <p>File Structure:<br>----------<br>Each zip file contains GPP data from a CEDAR model setup.</p> <p> </p> <p>File Naming Convention:<br>----------<br>All netCDF files follow this naming convention:<br>CEDAR-GPP_<version>_<model-setup>_<YYYYMM>.nc</p> <p>Where:<br><model-setup> comprises of <temporal_span>_<CFE_option>_<GPP_partitioning><br><temporal_span>: ST denotes short-term (2001 to 2020); LT denotes long-term (1982 to 2020)<br><CFE_option>: 'Baseline' indicates no direct CO2 fertilization effect, 'CFE-ML' represents direct CO2 fertilization incorporated by ML, 'CFE-Hybrid' implies direct CO2 fertilization incorporated by theory<br><GPP_partitioning>: 'NT' for night-time GPP partitioning method, 'DT' for day-time GPP partitioning method</p> <p><br>NetCDF characteristics:<br>----------<br>- Spatial Resolution: 0.05 degree<br>- Temporal Resolution: Monthly<br>- Temporal Coverage: Short-term (ST): 2001-2020; Long-term (LT): 1982 - 2020<br>- Image Dimension: Rows: 3600, Columns: 7200<br>- Units: gCm^-2day^-1<br>- Fill Value: -9999<br>- Multiply By Scale Factor: 0.01<br>- Data Type: uint16<br>- File Size: Approximately 99 MB per file</p> <p><br>Data variables:<br>----------<br>- GPP_mean: monthly gross primary productivity (gCm^-2day^-1), mean from 30 model ensemble<br>- GPP_std: standard deviation of 30 model ensemble</p> <p><br>Support Contact:<br>----------<br>For any queries related to this dataset, please contact:</p> <p>Name: Yanghui Kang<br>Email: kangyanghui@gmail.com</p> <p> </p> <p> </p>
Functional Near-Infrared Spectroscopy Reveals Delayed Hemodynamic Changes in the Primary Motor Cortex During Fine Motor Tasks and Decreased Interhemispheric Connectivity in Parkinson's Disease Patients
<p>This dataset contains functional near-infrared spectroscopy (fNIRS) data from 20 patients with Parkinson’s disease and 20 age- and sex-matched healthy subjects without movement disorders. There are 3 folders, each corresponding to a different task: a 10-second finger-tapping task, a 2-minute walking task, and a 6-minute resting-state. When using this dataset, please cite our work:</p> <div> <div>Guevara, E., Rivas-Ruvalcaba, F. J., Kolosovas-Machuca, E. S., Ramírez-Elías, M., Zapata, R. D. de L., Ramirez-GarciaLuna, J. L., & Rodríguez-Leyva, I. (2024). Parkinson’s disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: A functional near-infrared spectroscopy study. <em>Neurophotonics</em>, <em>11</em>(2), 025004. <a href="https://doi.org/10.1117/1.NPh.11.2.025004">https://doi.org/10.1117/1.NPh.11.2.025004</a></div> <div> <div> <div>Guevara, E., Solana-Lavalle, G., & Rosas-Romero, R. (2024). Integrating fNIRS and machine learning: Shedding light on Parkinson’s disease detection. <em>EXCLI Journal</em>, <em>23</em>, 763–771. <a href="https://doi.org/10.17179/excli2024-7151">https://doi.org/10.17179/excli2024-7151</a></div> <div> <div> <div> <div> <div>Guevara, E., Kolosovas-Machuca, E. S., & Rodríguez-Leyva, I. (2024). Exploring motor cortex functional connectivity in Parkinson’s disease using fNIRS. <em>Brain Organoid and Systems Neuroscience Journal</em>, <em>2</em>, 23–30. <a href="https://doi.org/10.1016/j.bosn.2024.04.001">https://doi.org/10.1016/j.bosn.2024.04.001</a></div> </div> </div> </div> </div> </div> </div> </div>
Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells
<p>Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by </p> <p>Nathalie R. Reinhard<sup>1</sup>, Sanne van der Niet<sup>1</sup>, Anna Chertkova<sup>1</sup>, Marten Postma<sup>1</sup>, Theodorus W.J. Gadella Jr.<sup>1</sup>, Peter L. Hordijk<sup>1,2</sup>, and Joachim Goedhart<sup>1*</sup><br> </p> <p><strong>Affiliations:</strong></p> <p><sup>1 </sup>University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands</p> <p><sup>2 </sup>Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands</p> <p> </p> <p>*Correspondence to: j.goedhart@uva.nl</p>
MiRoR11 - P2 - Annotated corpus for primary and reported outcomes extraction
<p>Annotated corpus for outcome extraction</p> <p>This folder contains 2 subfolders:<br> 1. Primary_outcomes - a corpus annotated for primary outcomes<br> The folder contains the following files:<br> po_sent_marked_p1_1000.txt - sentences 1 - 1000 of the annotated corpus, ConstruKT format; coordinated outcomes annotated as single entity<br> po_sent_marked_p2_1000.txt - sentences 1001 - 2000 of the annotated corpus, ConstruKT format; coordinated outcomes annotated as single entity</p> <p>po_sent_marked_col_p1.txt - sentences 1 - 1000 of the annotated corpus, tabulated format; coordinated outcomes annotated as single entity<br> po_sent_marked_col_p2.txt - sentences 1001 - 2000 of the annotated corpus, tabulated format; coordinated outcomes annotated as single entity</p> <p>po_sent_marked_col_p1_coord.txt - sentences 1 - 1000 of the annotated corpus, tabulated format; coordinated outcomes annotated as separate entities<br> po_sent_marked_col_p2_coord.txt - sentences 1001 - 2000 of the annotated corpus, tabulated format; coordinated outcomes annotated as separate entities</p> <p>Subfolders:<br> po - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets); coordinated outcomes annotated as separate entities<br> po_coord - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets); coordinated outcomes annotated as separate entities</p> <p>2. Reported_outcomes - a corpus annotated for reported outcomes<br> The corpus contains sentences from Results and Conclusions sections of the articles for which primary outcomes were annotated. The first part of reported outcomes corpus contains sentences from articles for the first half of the primary outcomes corpus (sentences 1 - 1000). The second part of reported outcomes corpus contains sentences from articles for the second half of the primary outcomes corpus (sentences 1001 - 2000).</p> <p>The folder contains the following files:<br> res_sent_marked_p1.txt - first part of the annotated corpus, ConstruKT format<br> res_sent_marked_p2.txt - second part of the annotated corpus, ConstruKT format</p> <p>res_sent_marked_p1_col.txt - first part of the annotated corpus, tabulated format<br> res_sent_marked_p2_col.txt - first part of the annotated corpus, tabulated format</p> <p>Subfolders:<br> rep - the corpus for 10-fold cross-validation (10 subfolders with train/dev/test sets)</p>
Data and code: A Comparison of Contemporary versus Older Studies of Aspirin for Primary Prevention
<p>Data (comma-separated values format) and analytical code (Stata format) relating to: Moriarty F, Ebell MH. A Comparison of Contemporary versus Older Studies of Aspirin for Primary Prevention. 2019 (under review). Available on medRxiv at https://doi.org/10.1101/19004267</p> <p><em><strong>Abstract</strong></em></p> <p><strong>Purpose</strong>: This study compares the benefits and harms of aspirin for primary prevention before and after widespread use of statins and colorectal cancer screening.</p> <p><strong>Methods: </strong>We compared studies of aspirin for primary prevention that recruited patients from 2005 onward with previous individual patient meta-analyses that recruited patients from 1978 to 2002. Data for contemporary studies were synthesized using random-effects models. We report vascular (major adverse cardiovascular events [MACE], myocardial infarction [MI], stroke), bleeding, cancer, and mortality outcomes.</p> <p><strong>Results</strong>: The IPD analyses of older studies included 95,456 patients for CV prevention and 25,270 for cancer mortality, while the four newer studies had 61,604 patients. Relative risks for vascular outcomes for older vs newer studies follow: MACE: 0.89 (95% CI 0.83-0.95) vs 0.93 (0.86-0.99); fatal hemorrhagic stroke: 1.73 (1.11-2.72) vs 1.06 (0.66-1.70); any ischemic stroke: 0.86 (0.74-1.00) vs 0.86 (0.75-0.98); any MI: 0.84 (0.77-0.92) vs 0.88 (0.77-1.00); and non-fatal MI: 0.79 (0.71-0.88) vs 0.94 (0.83-1.08). Cancer death was not significantly decreased in newer studies (RR 1.11, 0.92-1.34). Major hemorrhage was significantly increased for both older and newer studies (RR 1.48, 95% CI 1.25-1.76 vs 1.37, 95% CI 1.24-1.53). There was no effect in either group on all-cause mortality, cardiovascular mortality, fatal stroke, or fatal MI.</p> <p><strong>Conclusions</strong>: In the modern era characterized by widespread statin use and cancer screening, aspirin does not reduce the risk of non-fatal MI or cancer death. There are no mortality benefits and a significant risk of major hemorrhage. Aspirin should no longer be recommended for primary prevention.</p> <p> </p> <p>Version 1.1 - Updated Stata code to correct name of .csv data file for import.</p>
Dataset for "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements"
<p>Data of measurements and model output of the publication "Gross primary productivity of four European ecosystems constrained by joint CO<sub>2</sub> and COS flux measurements".</p> <p>Data consists of micrometeorological data, COS and CO<sub>2</sub> flux measurements for 4 sites including filters for the fluxes.</p> <p>The sites include: a managed temperate mountain grassland in Austria (18.06.-21.08.2015), a Mediterranean savanna ecosystem in Spain(29.04.-24.05.2016)), a Temperate beach forest in Denmark(07.06.-03.07.2016) and an agricultural soy bean field in Italy(03.07.-01.08.2017).</p> <p>Version 2: param2950** are now correct (were filled with the same values for all field sites) </p> <p>For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a></p>
Development and validation of statistical shape models of the primary functional bone segments of the foot.
<p>This dataset comprises manually segmented three-dimensional point clouds (.STL) of magnetic resonance images of the primary functional segments of the foot - first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus. These data were used to create statistical shape models of the foot bones, utilising the GIAS2 toolbox (https://pypi.org/project/gias2/).</p>
The LAVA mutants defective in auxin-regulated primary or lateral root development.
<p><span>Regulation of PIN activity, polarity as well as auxin gradient generation and its canalisation remain crucial topics in plant developmental biology especially in the context of organogenesis like the formation new lateral roots. Here, we are presenting the LAVA (LR Alterations Visualised after Auxin) collection of 278 mutants, defective in auxin-induced lateral root (LR) morphogenesis. Those mutants were obtained from a forward genetic screen in which synthetic auxin 1-Naphtyl Acetic Acid (1-NAA) was used to induce LR formation in the mutagenized PIN3::PIN3-GFP population. Our database contains mutant root phenotyping and for a subset of the collection, we recorded PIN polarity and subcellular trafficking, cotyledon vasculature development, primary and LR gravitropism as well as aerial phenotypes with some reminiscent to auxin-regulated organogenesis aberrations. We are convinced that our dataset can serve as a unique tool to identify novel components of auxin signalling, transport, and cell polarity but also be of interest to the broader plant research community interested in the roots system architecture that is vital for plant survival, growth and adaptation to environmental conditions.</span></p> <p><span>The phenotype analysis of the mutant collection is summarized and organised in an Excel spreadsheet (2024-08-07_mutant_database_Table S1). The photographic material is organised in folder form (see Supporting Data S1 in this repository), where each folder number corresponds to a particular mutant and entry in the excel table.</span></p> <p><span> Mutant seeds will be available in the European Arabidopsis Stock Centre (NASC). The seed sending to the repository is in progress. </span></p> <p><span>The manuscript describing this work is currently being submitted to the research journal. Its version will be available on the open access Masaryk University repository.</span></p>
Monthly, Seasonal and Yearly Net Primary Productivity (NPP) data of India from 2003-2020 modelled using the CASA model
<p>We estimated monthly Net Primary Productivity (NPP) at 1km spatial resolution for India using the Carnegie-Ames-Stanford Approach (CASA) Model. The CASA is a light use efficiency (LUE) based model that simulates NPP driven by remote sensing and meteorological data inputs. NPP is calculated as a product of the light use efficiency (LUE) and absorbed photosynthetically active radiation (APAR). The seasonal and annual data are prepared by aggregating the monthly data. The India Meteorological Department (IMD) recognizes four seasons in India based on climate conditions. The four seasons are defined as Winter (January-February), Pre-monsoon (March-May), Monsoon (June-September), and Post-monsoon (October-December). The seasonal data is prepared according to the above classification of the seasons. </p>
Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors
<p>Summary statistics for chromatin accessibility and gene expression quantitative trait loci (ca/eQTLs) from Matoba, N., Le, B.D., Valone, J.M. <em>et al.</em> Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors. <em>Nat Neurosci</em> (2024). https://doi.org/10.1038/s41593-024-01773-6</p>
Formal Methods in Railways: a Systematic Mapping Study - List of Primary Studies and Data Extraction
<p>This Excel file includes the list of papers analyzed in the systematic mapping study titled "Formal Methods in Railways: a Systematic Mapping Study". The study has been submitted for publication, and its preprint is also included in this repository. </p>
Simulated daily weather dynamics and gross primary production in 3 locations for 100,000 years
<p>IMPORTANT NOTE: The data in version 1 of this record, due to an error of units in the precipitation, had a non-physical vegetation growth and gross primary production. This has been fixed in version 2 of the record/dataset. Further, version 2 of the dataset contains 3 locations because the sites called "Grassland" and "Temperate" site produced the same type of vegetation (just grasses) in version 2, that contains therefore only a "Temperate" site.</p> <p>-------------------------------------------------</p> <p>The dataset reports daily temperature, precipitation, radiation and gross primary production in 3 different geographic locations (denoted as Temperate, Boreal and Tropical), representative of different climates and vegetation distributions, for 100,000 years. Each of the .nc files contains the dataset corresponding to one particular site.</p> <p>The weather data was produced using the AWE-GEN stochastic weather generator model ( Fatichi et al., Water Resources, 34(4):448–467 (2011) ). Vegetation dynamics and gross primary production are simulated via the dynamic global vegetation model LPX-Bern v1.4 ( Lienert and Joos, Biogeosciences, 15(9):2909–2930 (2018) ). The foliar projective cover is also reported at an annual scale.</p> <p> </p>
Primary vine varieties of European wine PDOs
<p>Primary varieties are the traditional vine cultivars of a region that are primarily used for making the wine products of a PDO region. In most cases, they are clearly defined in the legal document that regulate each PDO. We extracted primary varieties by analyzing the product specification files of European wine PDOs.</p>
Global gross primary production (GPP) product generated by data fusion based on random forest
<p>Improving the ability of gross primary production (GPP) estimates to capture extreme climate perturbations and reduce the uncertainty of GPP response processes to extreme climate is a new challenge. Based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. DFRF-GPP is highly reliable and can be used as a valuable data source for various applications, especially in high-temperature and drought-related studies.</p>
Primary data: Signal enhancement of hyperpolarized 15N sites in solution — increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures
<p>Primary data for DOI: 10.1002/nbm.4787</p> <p>NMR in Biomedicine. 2022;e4787</p> <p>Title: Signal enhancement of hyperpolarized 15N sites in solution—increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures</p> <p>Authors: Ayelet Gamliel, David Shaul, J. Moshe Gomori, Rachel Katz-Brull</p> <p>Description:</p> <p>These primary datasets contain data presented in the above publication and consist of:</p> <p>1. 15N-NMR spectra in solutions</p> <p>2. 13C polarization buildup data in solid-state</p> <p>3. 13C microwave profiles in solid state</p> <p>Please consult the Archive Guide.</p>
8-day, 500-m Gross Primary Production for Europe from 2001 to 2016(YEAR_doy: 2001001 to 2004329)
<p>This is the 8-day average GPP (g C m<sup>-2</sup> d<sup>-1</sup>) for Europe estimated by the Farquhar GPP Model. The multisource remote sensing datasets used to drive the FGM in this study included the 500 m 500 m yearly land use and land cover data from the MOD12Q1 C6 product (2001-2016), the 500 m 500 m 8-day LAI data from the GLASS V5 product (2001-2016), the 500 m 500 m 8-day clumping index data in 2006 derived from MODIS bidirectional reflectance distribution function (BRDF) data, the 500 m 500 m 8-day photosynthetic capacity (V<sub>cmax</sub>) data derived from leaf chlorophyll content (2001-2016), and the 5 km 5 km daily downward shortwave radiation (DSR) data from the GLASS V5 product (2001-2016). We obtained the 0.5° 0.5° 6-hr climate data (including air temperature and relative humidity) from the Climatic Research Unit-NCEP (CRUNCEP) V7 (2001-2016). The vapor pressure deficit (VPD) was calculated from atmospheric pressure, the minimum and maximum air temperature, and relative humidity. Meteorological data used to drive FGM included the mean air temperature and VPD. In addition, site-level daily ambient CO<sub>2 </sub>concentrations observed at the Mauna Loa Observatory (MLO) site (2001 to 2016) were used to drive the FGM. DSR data were resampled to a spatial resolution of 500 m 500 m with bilinear interpolation. Climate data were aggregated to a targeted temporal resolution of 8 days and downscaled to a spatial resolution of 500 m 500 m with bilinear interpolation. </p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform
<p>Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform<br> =======================================================</p> <p>• If you publish material based on this dataset, please cite the following :</p> <p> • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, & Francesco Mondada (2023). Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7912941</p> <p><br> • The corresponding article : El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751</p> <p><br> • License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)</p> <p>• Creator: El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.</p> <p>• Date: May 9th 2023</p> <p>• Subject: Educational change, Scalability, Professional Development, Digital Education, Curricular<br> Reform, Primary School</p> <p>• Dataset format: CSV</p> <p>• Dataset collection: September 2018 to September 2022</p> <p>• Dataset size : < 100 kB</p> <p>• Dataset content : one excel file with detailed description below. Please note that the spreadsheet may contain missing values due to teachers either choosing not to respond to the questions or the questions not being presented at each of the training sessions. To have access to the specific survey questions please refer to the associated publication [a].</p> <p>• Abbreviations :<br> - DE : Digital Education<br> - PD : Professional Development</p> <p>• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)</p> <p># References</p> <p>[a] El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751</p>
The $E_2$ page of the 2-primary Adams spectral sequence in a large range
<p>This dataset contains a description of the cohomology of the 2-primary Steenrod algebra in a large range. Broadly, it contains</p> <ul> <li>complete additive data up to stem 256,</li> <li>complete multiplicative and secondary data up to stem 200 (including the Adams <span class="math-tex">\(d_2\)</span> differentials).</li> </ul> <p>This data was generated using `<a href="https://github.com/SpectralSequences/sseq/tree/master/ext">ext-rs</a>` running on the Wayne State Grid. The Grid is a high-performance computing cluster operated by Wayne State University Computing & Information Technology. See the accompanying document for a more detailed breakdown of the timeline and the resource usage.</p> <p>This repository is comprised of several files.</p> <ul> <li>`rank.csv`: The rank of the cohomology in each bidegree. This data is complete up to and including stem 256 and homological degree 132. The bidegrees with rank zero are omitted. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`dimension`: The rank of the cohomology at the given stem and homological degree.</li> </ul> </li> </ul> <p>We use `rank.csv` to define a basis for the cohomology. The symbol <span class="math-tex">\(x_{n,s,i}\)</span> refers to the <span class="math-tex">\(i\)</span>th basis element in bidegree <span class="math-tex">\((n,s)\)</span>, where <span class="math-tex">\(n\)</span> is the stem and <span class="math-tex">\(s\)</span> is the homological degree.</p> <ul> <li>`d2.csv`: The Adams <span class="math-tex">\(d_2\)</span> differentials. This data is complete up to and including stem 200 and homological degree 128. If the target bidegree is zero-dimensional, the differentials are omitted. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`index`: The index <span class="math-tex">\(i\)</span> in the basis of the bidegree <span class="math-tex">\((n,s)\)</span> given by `rank.csv`.</li> <li>`target`: The value of <span class="math-tex">\(d_2(x_{n,s,i})\)</span>.</li> </ul> </li> <li>`products.csv`: The multiplication table of the product structure with respect to our basis. This data is complete for all pairs of elements whose product is in stem at most 200 and homological degree at most 128. The pairs of elements that multiply to zero are omitted. The columns are as follows: <ul> <li>`stem_1`, `Adams_filtration_1`, `index_1`: The stem <span class="math-tex">\(n_1\)</span>, homological degree <span class="math-tex">\(s_1\)</span>, and index <span class="math-tex">\(i_1\)</span> of the first element.</li> <li>`stem_2`, `Adams_filtration_2`, `index_2`: The stem <span class="math-tex">\(n_2\)</span>, homological degree <span class="math-tex">\(s_2\)</span>, and index <span class="math-tex">\(i_2\)</span> of the second element.</li> <li>`product`: The vector representing the product <span class="math-tex">\(x_{n_1, s_1, i_1} \cdot x_{n_2, s_2, i_2}\)</span> in bidegree <span class="math-tex">\((n_1 + n_2, s_1 + s_2)\)</span>.</li> </ul> </li> <li>`differentials.txt.zst`: The differentials in our minimal resolution, in a human-readable plain text format. The output was generated using <a href="https://github.com/SpectralSequences/sseq/blob/v0.0.4/ext/examples/differentials.rs">this</a> example code. It is compressed using Zstandard for convenience, and decompresses to a 64 GiB file.</li> <li>`change_of_basis.csv`: The correspondence between our basis and the Bruner-Rognes (BR) basis defined in <a href="https://arxiv.org/abs/2109.13117">arXiv:2109.13117</a>. Each row expresses one of our basis elements in terms of the BR basis. The columns are as follows: <ul> <li>`stem`: The stem <span class="math-tex">\(n\)</span>.</li> <li>`Adams_filtration`: The homological degree <span class="math-tex">\(s\)</span>.</li> <li>`index`: The index <span class="math-tex">\(i\)</span> in the basis given by `rank.csv` in bidegree <span class="math-tex">\((n,s)\)</span>.</li> <li>`BR`: The vector in the BR basis corresponding to our element <span class="math-tex">\(x_{n,s,i}\)</span>.</li> </ul> </li> </ul> <p>All CSV files are in UTF-8 encoded plain text. Unless otherwise noted, the entries are ordered by stem, then homological degree, then index.</p> <p>v1.0.1: We fixed an error in the `products.csv` file. Due to a software error, some products involving elements in bidegrees <span class="math-tex">\((0,9)\)</span>, <span class="math-tex">\((9,5)\)</span>, <span class="math-tex">\((11,5)\)</span>, <span class="math-tex">\((11,6)\)</span>, <span class="math-tex">\((20,5)\)</span>, <span class="math-tex">\((25,9)\)</span>, <span class="math-tex">\((30,9)\)</span>, <span class="math-tex">\((31,9)\)</span>, and <span class="math-tex">\((33,5)\)</span> were missing. We emphasize that none of the data that was already present in the original file was incorrect.</p> <p>Moreover, we have checked that our multiplication table is compatible with Bruner and Rognes' data (<a href="https://doi.org/10.11582/2022.00015">doi:10.11582/2022.00015</a>), and satisfies the relations described by Lin in (<a href="https://doi.org/10.11582/2022.00015">doi:10.5281/zenodo.7786290</a>).</p>
Primary raw observation input file for the Ring Laser Analysis program
<p>This dataset is the primary input for the ring laser analysis program. It comprises a tab-delimited table, containing time information, the observed Sagnac frequency and a fairly large number of auxiliary measurements in order to compute the necessary raw data reductions. </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.