Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

Supporting data for Assessing clouds using satellite observations through three generations of global atmosphere models

Monthly data from CAM4, CAM5, and CAM6 that are needed to reproduce the analysis and figures in the manuscript entitled: Assessing clouds using satellite observations through three generations of global atmosphere models by Brian Medeiros, Jonah Shaw, Jennifer Kay, and Isaac Davis.

opencc-by-4.0May 2023View details →

Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"

Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"

opencc-by-4.0May 2023View details →

Data and Supplementary Plots for A Shallow Water Model Exploration of Atmospheric Circulation on Sub-Neptunes

This repository contains geopotential maps, zonal wind plots, gifs, and data for the ensemble of possible sub-Neptunes presented in the main manuscript. The data, individual plots, and plot grids are based on the averages of the last 100 simulated days. The data are in the pickle format (see https://docs.python.org/3/library/pickle.html) The gifs are based on the last 1000 simulated hours. These figures and gifs support the analysis presented in the main manuscript.

WRF model configuration and data used for the NHESS manuscript "Heat wave characteristics: evaluation of regional climate model performances for Germany"

The file contains: <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: daily values of maximum temperature for the time period 1980-2009 from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul>

Data for Cryogenic Characterization and Modeling of 14 nm Bulk FinFET Technology

Data set for "Cryogenic Characterization and Modeling of 14 nm Bulk FinFET Technology" https://zenodo.org/record/6901637

Data from: Small molecule inhibitor of tau self-association in a mouse model of tauopathy: A preventive study in P301L tau JNPL3 mice

Advances in tau biology and the difficulties of amyloid-directed immunotherapeutics have heightened interest in tau as a target for small molecule drug discovery for neurodegenerative diseases. Here, we evaluated OLX-07010, a small molecule inhibitor of tau self-association, for the prevention of tau aggregation. The primary endpoint of the study was statistically significant reduction of insoluble tau aggregates in treated JNPL3 mice compared with Vehicle-control mice. Secondary endpoints were dose-dependent reduction of insoluble tau aggregates, reduction of phosphorylated tau, and reduction of soluble tau. This study was performed in JNPL3 mice, which are representative of inherited forms of 4-repeat tauopathies with the P301L tau mutation (eg, progressive supranuclear palsy and frontotemporal dementia). The P301L mutation makes tau prone to aggregation; therefore, JNPL3 mice present a more challenging target than mouse models of human tau without mutations. JNPL3 mice were treated from 3 to 7 months of age with Vehicle, 30 mg/kg compound dose, or 40 mg/kg compound dose. Biochemical methods were used to evaluate self-associated tau, insoluble tau aggregates, total tau, and phosphorylated tau in the hindbrain, cortex, and hippocampus. The Vehicle group had higher levels of insoluble tau in the hindbrain than the Baseline group; treatment with 40 mg/kg compound dose prevented this increase. In the cortex, the levels of insoluble tau were similar in the Baseline and Vehicle groups, indicating that the pathological phenotype of these mice was beginning to emerge at the study endpoint and that there was a delay in the development of the phenotype of the model as originally characterized. No drug-related adverse effects were observed during the 4-month treatment period.

Data from: Crystalline silica-induced proinflammatory eicosanoid storm in novel alveolar macrophage model quelled by docosahexaenoic acid

Introduction: Workplace exposure to respirable crystalline silica (cSiO2) is associated with chronic inflammatory and autoimmune diseases. At the mechanistic level, cSiO2 particles are quickly phagocytosed by resident alveolar macrophages (AMs) in the lung, causing a robust cycle of proinflammatory cytokine release, lysosomal rupture, mitochondrial toxicity, and immunogenic cell death if the particle is not efficiently cleared by the lung. We and others have demonstrated in bone marrow-derived and transformed macrophage models that supplementation with the ω-3 polyunsaturated fatty acid (PUFA) docosahexaenoic acid (DHA) contributes to increased membrane phospholipid content of DHA and subsequent suppression of cSiO2-triggered inflammatory responses. However, mechanistic exploration of ω-3 PUFA effects in AMs is challenging due to reliance on short-lived primary AMs derived from lung lavage fluid. Methods: To address these limitations, we have employed a recently developed novel self-renewing AM model from C57BL/6 mice, fetal liver-derived alveolar-like macrophages (FLAMs), that is phenotypically representative of primary lung AM populations. We found that incubation of FLAMs with 25 µM DHA as ethanolic suspensions or as complexes with bovine serum albumin were equally effective at increasing ω-3 PUFA content of phospholipids at the expense of the ω-6 PUFA arachidonic acid (ARA) and the ω-9 monounsaturated fatty acid oleic acid. Based on these findings, FLAMs were treated with 25 µM DHA in EtOH or EtOH vehicle (VEH) for 24 h, with or without LPS for 2 h, and with or without cSiO2 for 1.5 or 4 h then proinflammatory cytokine release, lysosomal membrane permeabilization, and mitochondrial depolarization assessed. In addition, oxylipin metabolites were measured using a targeted LC-MS lipidomics panel of 156 metabolites. Results: Regardless of whether FLAMs were LPS-primed, cSiO2-triggered lysosomal permeability, mitochondrial toxicity, and cell death were not impacted by DHA. LPS+cSiO2 elicited marked IL-1α, IL-1β, and TNF-α release after 1.5 and 4 h of cSiO2 exposure, which was significantly inhibited by DHA. In VEH-treated cells, cSiO2 alone and LPS+cSiO2 induced synthesis of ARA-derived proinflammatory oxylipins including prostaglandins, leukotrienes, and thromboxanes that was suppressed by DHA. In addition, DHA promoted synthesis of pro-resolving DHA-derived oxylipins at the expense of ARA-derived oxylipins. Discussion: FLAMs were amenable to lipidome modulation by DHA, which suppressed cSiO2-triggered proinflammatory cytokine responses and ARA-derived oxylipins that potentially contribute to the particle's toxicity in the lung. FLAMs are a promising in vitro alternative to primary AMs for investigating interventions against toxicant-triggered inflammation and autoimmunity in the lung.

Models and data for "Guaranteed safe controller synthesis for switched systems using analytical solutions"

Models and data for the paper "Guaranteed safe controller synthesis for switched systems using analytical solutions" presented at the 7th IEEE Conference on Control Technology and Applications, Barbados, August 16-18, 2023.

Data for: Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA

Regional faunal assessments of stoneflies in the United States Midwest (herein defined as Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin) indicate increasing imperilment resulting from human disturbance and climate change. Large-bodied Perlid stoneflies with multivoltine life cycles are among the most at risk for regional extirpation, with losses reported in several midwestern states. Species distribution modeling was undertaken to describe distribution shifts for four widespread riverine species: Acroneuria abnormis (Newman, 1838), A. frisoni Stark & Brown, 1991, A. internata (Walker, 1852) and A. lycorias (Newman, 1839). The distribution modeling algorithm MaxEnt was selected to predict both the historical (i.e., pre-1960) and contemporaneous distributions for each species using separate occurrence datasets. These models permit the identification of suitable habitat loss through range contractions associated with human disturbance. Predictions of suitable habitat losses were recorded for multiple species but were greatest for A. abnormis and A. internata. These models serve to guide future collection efforts and to further describe patterns of regional biodiversity loss. The data presented within this dataset contain the occurrence data used for modeling with distinction between temporal periods modeled.

openJun 2023View details →

CARRT - Motion Capture Data for Robotic Human Upper Body Model

As advancements in the study of human activity have progressed in recent years, researchers have directed their attention towards analyzing human daily activities to investigate a diverse range of performance metrics unconsciously optimized by individuals while engaged in specific tasks. To replicate these movements in robotic systems based on human models, researchers have developed a framework for robot motion planning capable of utilizing various optimization methods to reproduce such motions through human demonstrations. In this process, capturing the movements of the human body and the objects involved in the demonstrations is imperative, as they provide essential information for the motion planning procedure. The objective of this dataset is to present human motion data while performing activities of daily living. This dataset encompasses comprehensive and precise whole-body motion data of individuals collected using a Vicon motion capture system, which facilitated the development of a full-body model integrated into OpenSim and MATLAB. The dataset comprises nine different daily living activities and eight Range of Motion activities performed by ten healthy participants. A publicly accessible whole-body human motion database has been established, encompassing raw motion data in .c3d format, motion data in .csv format for the OpenSim model, and post-processed motion data for the MATLAB model.

opencc-by-4.0Feb 2023View details →

Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces

Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"

Data for "Propagation Eects of Slanted Narrow Bipolar Events: A Rebounding-Wave Model Study"

The data for generating the figures in "Propagation Eects of Slanted Narrow Bipolar Events: A Rebounding-Wave Model Study".  Each file consists of time (microsecond) and the electric field (V/m).

opencc-by-4.0Apr 2023View details →

Data sets and machine learning models for: Predicting critical properties and acentric factor of fluids using multi-task machine learning

The experimental data sets, data splits, additional features, QM calculations, model predictions, and final machine learning models for the manuscript "Predicting Critical Properties and Acentric Factor of Fluids Using Multi-Task Machine Learning". Citation should refer directly to the manuscript: <ul> <li> Biswas, S.; Chung, Y.; Ramirez, J.; Wu, H.; Green, W. H. Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning. Journal of Chemical Information and Modeling. 2023 63 (15), 4574-4588. DOI: <a href="https://doi.org/10.1021/acs.jcim.3c00546">10.1021/acs.jcim.3c00546</a> </li> </ul> To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a>.  Detailed information can be found in README.md file.   Details on the properties considered The data set includes the following 8 properties: <ul> <li>Tc: critical temperature, in K</li> <li>Pc: critical pressure, in bar</li> <li>rhoc: critical density, in mol/L</li> <li>omega: acentric factor, unitless</li> <li>Tb: boiling point, in K</li> <li>Tm: melting point, in K</li> <li>dHvap: enthalpy of vaporization at boiling point, in kJ/mol</li> <li>dHfus: enthalpy of fusion at melting point, in kJ/mol</li> </ul> Details on the files 1. Data sets under CritProp_v1.1.0: <ul> <li>all_data: includes the data sets used in this work. All data points are listed for each chemical compound as well as its corresponding data source. The details of the data sources can be found in the README.md file. The distribution of the data set is included in each folder. <ul> <li>estimated_data_for_pretraining: contains the estimated data from Yaws' handbook that are used to pre-train our machine learning (ML) model.</li> <li>experimental_data: contains the experimental data (references 1 - 15) used to fine-tune our final ML model.</li> </ul> </li> <li>additional_features: includes the additional features tested for the ML model. The Abraham features are generated for all data (references 1 - 15) while the acsf, qm, and rdkit features are only generated for the data from references 1 - 9. <ul> <li>abraham: Abraham solute parameters (E, S, A, B, L). Molecular features.</li> <li>acsf: ACSF (atom-centered symmetry functions). Atomic features that are coverted from the 3D coordinates of the compound</li> <li>qm_atom: QM (quantum chemical) atomic feature. </li> <li>qm_mol: QM molecular feature.</li> <li>rdkit: Selected RDKit 2D molecular features.</li> </ul> </li> <li>data_splits_and_model_predictions: contains the training and test sets used to evaluate the model. It also contains the predicted values from our final ML model for each test set. <ul> <li>random and scaffold splits: training and test sets that include the data from references 1 - 9.</li> <li>external test set: a test set that includes the data from only references 10 - 15.</li> </ul> </li> </ul> 2. Machine learning (ML) model files: <ul> <li>CritProp_ML_model_files_with_abraham_feat.zip: contains the Chemprop ML model files that are trained using Abraham features as additional molecular features. This gives the best results.</li> <li>CritProp_ML_model_files_without_additional_feat.zip: contains the Chemprop ML model files that are trained without any additional features. This gives the second best results.</li> </ul> To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a> 3. QM (quantum chemical) calculations: <ul> <li>QM_calculations.zip: contains the results of the QM calculations that are performed to compute QM features.</li> </ul>

Data from: Functional Frogs: Using swimming performance as a model to understand natural selection and adaptations

The 'Functional Frogs' 5E lesson plan is an outcome of a Research Experience for Teachers (RET) program funded by the National Science Foundation (NSF). The NSF project integrates research on swimming locomotion in frogs with broader goals of improving science education. The aim of this RET program was to increase scientific literacy in secondary school teachers in Oklahoma, so that they can transfer knowledge from academic research experience to the classroom and thus improve their students' understanding of science. In the this lesson, we focus primarily on comparing the peak velocity of five species of frogs, guiding students to collect data from videos of frogs swimming. The students then make evidence-based interpretations about the morphological traits that may underlie differences in swimming velocity across species. The five species differ both in morphology and ecology, with some inhabiting the aquatic environment more than others. Therefore, the lesson helps guide students through important steps of the scientific method to achieve a greater understanding of how adaptations arise in nature through examining variation in ecology, morphology, and locomotor performance.

Data and codes: Speech-recognition in landlide predictive modelling

This is the data and codes for the manuscript "Speech-recognition in landlide predictive modelling"

Data for Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies

Data from Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies (2023). This includes linkage disequilibrium graphical models (LDGMs) created from <a href="https://www.biorxiv.org/content/10.1101/2021.02.06.430068v2">high-coverage 1000 Genomes Project sequencing data</a>. This dataset consists of LDGM precision matrices, LDGM graphical models of SNPs, and lists of SNPs, all split into <a href="https://www.biorxiv.org/content/10.1101/2022.03.04.483057v1">1,361 approximately independent LD blocks across the genome</a>. The dataset additionally contains genotype information from chromosomes 21 and 22, and inferred tree sequences of high coverage 1000 Genomes Project Data, summary statistics from four traits in the UK Biobank, and UK biobank correlation matrices from chromosomes 21 and 22. All genomic data is in the GRCh38 build. The data can be cited as follows: Pouria Salehi Nowbandegani, Anthony Wilder Wohns, Jenna L. Ballard, Eric S. Lander, Alex Bloemendal, Benjamin M. Neale, and Luke J. O’Connor. Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies. Nat Genet. (2023) DOI: 10.1038/s41588-023-01487-8   The directory contains `.tar.gz` files, which can be extracted and unzipped with: <pre><code class="language-bash">$ tar -xvf FILENAME.tar.gz</code></pre> All LD block files are named by chromosome and start/end basepair coordinates. <ul> <li> 1kg_nygc_trios_removed_All_pops_geno_ids_pops.csv: The file contains 5008 rows, 2 for each individual in the 1000 Genomes Project. Each row contains the individual ID of the 1000 genomes individual, and the ancestry group and continental ancestry group that individual was assigned to. Rows correspond to columns in `.genos` files.  </li> <li>AFR/AMR/EAS/EUR/SAS.precision.tar.gz: Precision matrices for the relevant ancestry group for each LD block. Edge lists contain one row for each non-zero entry of the precision matrix. There are no column names.</li> <li>genos_chr21_22.tar.gz: for the 40 LD blocks on chromosomes 21-22, .genos files are 0/1 matrices, with dimension number-of-SNPs by number-of-samples . Each LD matrix contains one column for each row in the SNP list files, and one row for each row in the sample ID files.</li> <li>ldgms.tar.gz: 1361 LDGMs (*.edgelist files). Edge lists contain one row for each non-zero entry of the LDGM adjacency matrix. There is one LDGM edge list for each LD block. Each row represents an edge, as a tuple (index_1, index_2, entry). For the LDGM adjacency matrices, the entry is the edge weight, where 0 represents a strong dependency and e.g. 6 represents a weak dependency.</li> <li>snplists_GRch38positions.tar.gz: 1361 *.snplist files, each of which contains information on the SNPs in each LD block. Each SNP list is an n x 11 table (n = number of SNPs), one for each LD block. The columns are:  <ul> <li> index: these non-unique indices, starting at zero, correspond to rows and columns of the LDGMs. There can be multiple SNPs for a single index, which occurs when the corresponding mutations occur on the same brick of the bricked tree sequence. SNPs with the same index have high (nearly perfect) LD. </li> <li> anc_alleles: ancestral allele </li> <li> deriv_alleles: derived allele </li> <li> EUR: allele frequency of derived allele in EUR samples </li> <li> EAS: allele frequency of derived allele in EAS samples </li> <li> AMR: allele frequency of derived allele in AMR samples </li> <li> SAS: allele frequency of derived allele in SAS samples </li> <li> AFR: allele frequency of derived allele in AFR samples </li> <li> site_ids: unique identifier of each SNP, mostly as RSIDs </li> <li> position: GRCh38 position of SNP </li> <li> swap: indicates strandness swap </li> </ul> </li> <li> ukb.tar: Correlation matrices and SNP lists for SNPs in the UK Biobank. <ul> <li> correlation_matrices/: Correlation matrices for SNPs in the UK biobank, computed by Weissbrod et al. 2020 Nat Genet and can be downloaded by following the instructions <a href="https://alkesgroup.broadinstitute.org/UKBB_LD">here</a>. </li> <li> snplists/: List of SNPs in the *.snplist format included in the UK Biobank </li> </ul> </li> <li> tree_seqs.tar: contains 22 tree sequences inferred by <a href="https://tsinfer.readthedocs.io">tsinfer</a> from the <a href="https://www.biorxiv.org/content/10.1101/2021.02.06.430068v2">30x 1000 Genomes Project Data</a>. Tree sequences can be unzipped with <a href="https://tszip.readthedocs.io/en/latest/">tszip</a>. </li> <li> Summary statistics: there are four summary statistics files, obtained from <a href="https://alkesgroup.broadinstitute.org/UKBB/">https://alkesgroup.broadinstitute.org/UKBB/</a>, and computed by Loh et al. 2018 Nat Genet. </li> </ul> <table> <tbody> <tr> <td> Phenotype </td> <td> Heritability estimate  </td> <td> Effective sample size </td> <td> Number of SNPs </td> </tr> <tr> <td> Height </td> <td> 0.570 </td> <td> 650K </td> <td> 12 Million </td> </tr> <tr> <td> Body mass index </td> <td> 0.303 </td> <td> 500K </td> <td> 12 Million </td> </tr> <tr> <td> Cardiovascular disease </td> <td> 0.155 </td> <td> 450K </td> <td> 12 Million </td> </tr> <tr> <td> Type 2 diabetes </td> <td> 0.073 </td> <td> 450K </td> <td> 12 Million </td> </tr> </tbody> </table>

Simulation data for the office cell building energy model with the attached overhang

Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".

A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts: data, forecasts, and scores

This data publication contains zipped parquet from the Falling Creek Reservoir multi-model ensemble (MME) forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and baseline models: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations, forecasts.zip contains forecast parquet files generated from the MME workflow (FLARE & baseline models), and scores.zip contains forecast skill metrics required for analysis.

Computer Code and Data - Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model

Computer code and data related to the research project "Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model".

Raw video and pose estimation data of top view open field mouse behavior recordings of acute and chronic stress models

This repository contains raw data for 411 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.