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9,882 results for “scales”
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Lyman-alpha forest simulations used for the measurement of the smoothing scale of the intergalactic medium
<p>This repository contains data from the hydrodynamic and dark-matter simulations used in Rorai et al.2017 to measure the pressure smoothing scale of the intergalactic medium (IGM) using the lyman alpha forest from close quasar pairs. </p> <p>The data includes :</p> <p>synthetic spectra (at z~2,2.4,3,3.6) of the transmitted lya flux from a grid of hydrodynamic model of the IGM assuming various thermal and reionization histories;</p> <p>Velocity and density sight lines from a dark matter simulation (at the same redshifts), with different values of the smoothing parameters, which can be used to calculate the lyman alpha flux for the desired values of the thermal parameters (as well as of the mean flux);</p> <p>More information can be found in the README file</p> <p> </p>
Supplementary Data: Global fits of GUT-scale SUSY models with GAMBIT (arXiv:1705.07935)
<p>Supplementary Data</p> <p><em>Global fits of GUT-scale SUSY models with GAMBIT</em><br> <em>arXiv:1705.07935</em></p> <p>The files in this record contain data for the CMSSM, NUHM1 and NUHM2 models considered in the GAMBIT "Round 1" GUT-scale SUSY paper.</p> <p>For each model, there are</p> <ul> <li>A number of YAML files, each corresponding to a different set of sampling parameters and/or priors</li> <li>A set of YAML files used for postprocessing: CMSSM_intermediate.yaml, CMSSM.yaml, NUHM1.yaml and NUHM2.yaml</li> <li>A final hdf5 file, containing the combined results of all sampling runs</li> <li>An example pip file, for producing plots from the hdf5 file using pippi</li> <li>SLHA1 and SLHA2 files for the best-fit point in each subregion of the fit. These can be found inside the tarball best_fits_SLHA.tar.gz.</li> </ul> <p>The record also contains</p> <ul> <li>StandardModel_SLHA2_scan.yaml and StandardModel_SLHA2_postprocessing.yaml, two universal YAML fragments included from other yaml files</li> <li>gambit_preamble.py, a collection of python functions used for in-line data processing in the pip files</li> </ul> <p>The different YAML files corresponding to different samplers and/or priors follow the naming scheme [model]_[scanner]_[prior]_[slice]_[special].yaml, where</p> <ul> <li>model = CMSSM, NUHM1, NUHM2</li> <li>scanner = Diver, MN</li> <li>prior = log, flat</li> <li>slice = pmu, nmu (positive or negative mu)</li> <li>special = sqcoann, slcoann, [blank] (squark co-annihilation, slepton co-annihilation, or bulk)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>For each model, the final hdf5 results file included here was generated in the following way:</p> <ul> <li>carry out initial runs using YAML files following the naming scheme above</li> <li>combine the resulting hdf5 output files into a single file, using gambit/Printers/scripts/combine_hdf5.py</li> <li>postprocess the samples to remove all points more than 5 sigma from the current best fit, using [model]_strip.yaml</li> <li>postprocess the samples to include a new likelihood term for LHC Run II searches, and to recompute the FlavBit likelihoods (these were buggy in a pre-release version of GAMBIT). For the CMSSM, this happened in two steps, due to persistent flavour bugs, using CMSSM_intermediate.yaml and CMSSM.yaml. For the NUHM1 and NUHM2, this was done in a single step each, using NUHM1.yaml and NUHM2.yaml.</li> </ul> </li> <li> <p>It is not necessary to repeat the steps listed in point 1 when running new scans; the LHC Run II likelihoods can be included in the original YAML file, so that no postprocessing step is required.</p> </li> <li> <p>The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.</p> </li> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip files are tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file for each model is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Board Leadership Database (U.S. Public Firms) + ML Script for Scaling Human Coded Data
<p>Files include: (1) an open sourced database of CEO duality and board chair orientations developed by scaling human coded data using supervised machine learning techniques (in both .dta and .csv formats), as well as (2) the accompanying training and scoring scripts to scale human coded data.</p> <p>Users may apply the scoring script to score the same variables from company proxy statements, or may adapt the training/scoring scripts and retrain models to scale human coded data of other constructs or measures. </p> <p>We note that early steps in the process to develop our database and script required web-scraping of company filings from SEC Edgar and text extraction from collected filings. We relied on other publicly available scripts to develop our own fetcher and extraction scripts. Users seeking to duplicate those parts of the process may benefit from the following resources from Kai Chen and pipy.org: </p> <p>For resources from Kai Chen: see <a href="https://urldefense.com/v3/__https:/www.kaichen.work/?p=681__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbY9vaIG5A$">https://www.kaichen.work/?p=681</a> and <a href="https://urldefense.com/v3/__https:/www.kaichen.work/?p=946__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbZhp5N_Tw$">https://www.kaichen.work/?p=946</a></p> <p>For resources from pipy.org, see <a href="https://urldefense.com/v3/__https:/pypi.org/project/sec-edgar-downloader/__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbYxYSZGGw$">sec-edgar-downloader</a> and <a href="https://urldefense.com/v3/__https:/pypi.org/project/sec-api/__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbZ_FwY6jw$">sec-api</a></p> <p> </p>
Dataset for "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators"
<p>This dataset includes all supporting data and scritps to generate figure panels in the paper "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators" (A. Nuno, J. Guiet, B. Baranek and D. Bianchi)</p>
Lightning Potential Index Using ICON Simulation at the km-scale over the Third Pole Region: ISS-LIS events and ICON-CLM simulated LPI
<p>This dataset contains records of lightning events recorded by the International Space Station (ISS) Lightning Imaging Sensor (LIS) from October 2019 to September 2022 in the Third Pole region. Furthermore, the Icosahedral Nonhydrostatic Weather and Climate Model in Climate Limited-Area Mode (ICON-CLM) was utilized to simulate the hourly Lightning Potential Index (LPI) over the Third Pole region for the same duration. The aforementioned dataset was utilized in the creation of the research article titled "Modeling Lightning Activity in the Third Pole Region: Performance of a km-scale ICON-CLM Simulation" authored by Prashant Singh and Bodo Ahrens. The paper has been submitted to the journal Atmosphere. In CORDEX-FPS-CPTP contribution no. 17 (GUF), you can find more data from ICON-CLM, such as precipitation, CAPE, wind vectors, and more.</p>
Earthquakes unveil the global-scale fractality of the lithosphere
<p>The repository contains the post-processed data required to produce the figures of the article entitled <i>"Earthquakes unveil the global-scale fractality of the lithosphere"</i> under consideration in Communications Earth & Environment.</p><p>Data are divided in directories, each corresponding to an article figure and numbered accordingly.</p>
Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania
<h3><strong>Reference</strong></h3><p>Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. <a href="https://doi.org/10.1073/pnas.2207398120">https://doi.org/10.1073/pnas.2207398120</a> </p><h3><strong>Abstract</strong></h3><p>Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (<i>P</i> = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.</p><h3><strong>Replication Data</strong></h3><p>We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the <i>hh_data</i> folder and includes:</p><ul><li><i>hh_imputed10_2018:</i> anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times</li><li><i>hh_compensation_2018:</i> anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs</li><li><i>hh_migration_2018:</i> anonymized household extract for 2018 representing household migration behavior following LSLAs</li><li><i>hh_rsdata_2018:</i> extracted remote sensing data at the household geo-location for 2018</li><li><i>hh_land_<strong>2019</strong>:</i><strong> </strong> anonymized household extract for <strong>2019 </strong>of land variables</li></ul><p>Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in <i>lsms_data</i> folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). </p><p>Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the <i>gis_data</i> folder.</p><p>Finally, our data replication includes several model outputs (found in <i>mod_outputs)</i>, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our <i>main_analysis.Rmd</i> script. </p><h3><strong>Replication Code</strong></h3><p>We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:</p><ul><li><i>main_analysis.Rmd:</i> main analysis supporting the finding, graphs, and tables reported in our main manuscript</li><li><i>compensation.R:</i> analysis of benefits and compensation received directly by households from LSLAs</li><li><i>landvalue.R:</i> analysis of household land values as a function of distance from LSLAs</li><li><i>migration.R:</i> analysis of migration behavior following LSLAs</li><li><i>selection_bias.R:</i> analysis of LSLA selection bias between control and treatment enumeration areas</li></ul>
Data accompanying the manuscript "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales"
<p>This dataset contains time series of wind energy production aggregated over France and Europe, obtained from a 1000-year climate simulation from the CESM model (version 1.2.2, Hurrel et al. 2013), coupled to a simple energy model to compute grid-point capacity factor from surface wind. Wind power is then computed by multiplying the capacity factor by the installed capacity, taken from 5 e-Highway scenarios (X5, X7, X10, X13 and X16), and integrated over the regions of interest. More details about the climate simulation, wind energy model and installed capacity scenarios can be found in the associated manuscript, "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales" (Cozian et al. 2023).</p><p>The data is organized into 10 files for France and 10 files for Europe. In each case, the 10 files correspond to 10 batches of 100 years each, with 3-hourly output. Each file contains 5 time series corresponding to the 5 installed capacity scenarios.</p><h4>References</h4><ul><li>Hurrell J W, Holland M M, Gent P R, Ghan S, Kay J E, Kushner P J, Lamarque J F, Large W G, Lawrence D, Lindsay K, Lipscomb W H, Long M C, Mahowald N, Marsh D R, Neale R B, Rasch P, Vavrus S, Vertenstein M, Bader D, Collins W D, Hack J J, Kiehl J and Marshall S (2013). The community earth system model: A framework for collaborative research. Bulletin of the American Meteorological Society, 94, 1339–1360. <a href="https://doi.org/10.1175/BAMS-D-12-00121.1">https://doi.org/10.1175/BAMS-D-12-00121.1</a></li><li>e-Highway 2050 (2015). Europe's future secure and sustainable electricity infrastructure. <a href="https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results">https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results</a></li><li>Cozian B, Herbert C and Bouchet F (2023). Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales. <a href="https://doi.org/10.48550/arXiv.2311.13526">https://doi.org/10.48550/arXiv.2311.13526</a></li></ul>
A Large Scale Side-Scan Sonar Dataset of Seafloor Sediments for Self-Supervised Pretraining
<p>This dataset serves as an extension to the dataset part of "A convolutional vision transformer for semantic segmentation of side-scan sonar data" published in Ocean Engineering, Volume 86, part 2, 15 October 2023,<strong> </strong>DOI: <a href="https://www.sciencedirect.com/science/article/pii/S0029801823020310">10.1016/j.oceaneng.2023.115647</a> for self-supervised pretraining.</p><p>This dataset consists of patches of side-scan sonar waterfalls collected along the coast of Catalunya during an extensive survey. The waterfalls were partitioned in batches of 384 lines to generate images of size 384 × 384 with a 192 pixel-overlap along-track and across-track. This resulted in a total of 434,164 images capturing various seafloor types including rocky bottoms, sand ripples, detrital funds, posidonia, cymocea, mud, corals, artificial reefs etc.</p><p>Additional tools for using the data for self-supervised pretraining can be found under <a href="https://github.com/DeeperSense/deepersense-seafloorscan">https://github.com/DeeperSense/deepersense-seafloorscan</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected by Tecnoambiente SL as part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020. Project Number: 101016958.</p>
Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale
<p>This artifact packages the data for the paper: <em>Privacy-Respecting Type Error Telemetry at Scale</em></p> <p>There are two files on Zenodo:</p> <ul> <li>data.tar.gz has the original Luau telemetry data</li> <li>artifact.tar.gz has a result PDF, intermediate data, and scripts for processing the data</li> </ul> <p>The artifact code and the source for the paper are also on GitHub:</p> <ul> <li><a href="https://github.com/bennn/luau-telemetry">https://github.com/bennn/luau-telemetry</a></li> </ul> <p>This artifact is primarily a **dataset**. It shows how we reached the conclusions in the paper.</p> <p>The scripts in this artifact are provided as-is for completeness. They may have bugs. They may not work as advertised.</p>
Large-scale attributed graph & hypergraph datasets: TWeibo, Amazon2M, Amazon, MAG-PM
<p>Here we provide additional large-scale datasets used in our work "A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation", along with the index files for constructing KNN graphs using ScaNN and Faiss.</p> <p>Usage:</p> <p>cd ANCKA/</p> <p>unzip ~/Download_path/ANCKA_data.zip -d data/</p>
Using RS-DAT to study continental-scale phenology at high-spatial resolution
This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.
CONUS-wide Balancing Authority Scale Hydropower Projections derived from 9505 Third Assessment
<p>This dataset provides historical and climate projection monthly hydropower generation timeseries for balancing authorities within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad6ceb" target="_blank" rel="noopener">Broman et al., 2024</a> and in the article's <a href="https://github.com/9505-PNNL/broman-etal_2024_erl">metarepository</a>. </p> <p>The dataset is provided in three separate formats to facilitate ease of use:</p> <p>1) Machine-readable csv in 'tidy' data format:</p> <table> <tbody> <tr> <td><strong>Short Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>class</td> <td>N/A</td> <td>simulation type; control: historical, cc: climate scenario</td> </tr> <tr> <td>forcing</td> <td>N/A</td> <td>meteorological forcing used to drive hydrology model</td> </tr> <tr> <td>model</td> <td>N/A</td> <td>hydrology model</td> </tr> <tr> <td>hp</td> <td>N/A</td> <td>hydropower model</td> </tr> <tr> <td>gcm*</td> <td>N/A</td> <td>global climate model name</td> </tr> <tr> <td>ds*</td> <td>N/A</td> <td>downscaling method; DBCCA (statistical), RegCM (dynamical)</td> </tr> <tr> <td>balancing_authority</td> <td>N/A</td> <td>balancing authority code</td> </tr> <tr> <td>year</td> <td>N/A</td> <td>year</td> </tr> <tr> <td>month</td> <td>N/A</td> <td>month</td> </tr> <tr> <td>modeled_generation_MWh</td> <td>MWh per month</td> <td>simulated generation</td> </tr> </tbody> </table> <p>* only present in the climate projection (cc) files</p> <p>2) xlsx with balancing authority data by tab</p> <p>3) csv by balancing authority:</p> <p>for historical data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp</em> and with the units <em>MWh per month</em>.</p> <p>for climate projection (cc) data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp_gcm_ds</em> and with the units <em>MWh per month</em>.</p>
[Dataset & scripts] to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu
<p>## "Spatial scales of kinetic energy in the Arctic Ocean"</p> <p>Available dataset for each figure (1~9) and figure10 in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5, Fig10) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9).</p> <p>## Description</p> <p>This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9.</p> <p>1) Jupyter notebook scripts for visualization<br>the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5/10. The load path in the script should be changed to your files accordingly.</p> <p>2) Matlab scripts for plots<br>All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.</p>
The Femern-project: a large-scale excavation of a Stone Age landscape - supplementary data
<p>This dataset contains all radiocarbon dates from the Femern project.</p> <p>Please cite the dataset as: </p> <p>Måge, B.T., Groß, D., Kanstrup, M. 2023. The Femern-project: a large-scale excavation of a Stone Age landscape. In: Groß, D. and Rothstein, M.: Changing Identity in a Changing World. Archaeological Studies on Human Interaction in Northern Europe around 4000 cal BC. Leiden: Sidestone, supplementary material.</p> <p>19.02.2024: Dataset updated: Wrong species ID in original dataset for AAR-27426</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Dataset - Papyrus 05.4 - A large scale curated dataset aimed at bioactivity predictions
<div> <p>This repository contains the version 05.5 of the Papyrus dataset, an aggregated dataset of small molecule bioactivities, as described in the article "Papyrus - A large scale curated dataset aimed at bioactivity predictions" <a href="https://doi.org/10.26434/chemrxiv-2021-1rxhk">https://doi.org/10.26434/chemrxiv-2021-1rxhk</a>.</p> <p> </p> <p>With the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers’ time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some example quantitative structure-activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research.</p> </div>
Accompanying data - Papyrus - A large scale curated dataset aimed at bioactivity predictions
<p><strong>Addition of supporting files:<br>- </strong>LICENSE.txt<strong><br>- </strong>data_types.json<strong><br>- </strong>data_size.json</p> <p> </p> <p><strong>Fixed version of Papyrus++ 05.5:<br>- In the previous 05.5 version </strong>data was incorrectly duplicated based on assay type. This resulted in unintended data augmentation.<br><strong>- In this fixed 05.5 version</strong> the duplicates have been eliminated, now reporting the correct amount of data per assay type.</p> <p> </p> <p>This repository contains the version 05.5 of the Papyrus dataset, an aggregated dataset of small molecule bioactivities, as described in the article "Papyrus - A large scale curated dataset aimed at bioactivity predictions" <a href="http://doi.org/10.1186/s13321-022-00672-x">http://doi.org/10.1186/s13321-022-00672-x</a>.</p> <p> </p> <p>With the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers’ time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some example quantitative structure-activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research.</p>
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