Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Raw data for: Structure of the human dopamine transporter and mechanisms of allosteric inhibition

This repository contains raw data related to "Structure of the human dopamine transporter and mechanisms of allosteric inhibition" by Srivastava et al. Included are molecular dynamics input parameter files, amber prmtop, production trajectories and analysis scripts. Trajectories are subsampled with one frame every 10 ns.    Contact information: Name: Md Fulbabu Sk Institution: Theoretical and Computational Biophysics Group (TCBG), Beckman Institute, University of Illinois Urbana Champaign Address: 405 N. Mathews Avenue, Urbana, Illinois 61801 Email: mfsk@illinois.edu

opencc-by-4.0Jan 2025View details →

Data from "Population genomic structure of Lemna minor and the cryptic species L. japonica in Switzerland"

SNP data and sample annotation: <ul> <li>sampleTab.csv contains the sample annotation (species and population)</li> <li>L.minor.reference.bcftools.snps.vcf.gz(.tbi) contains SNPs from all samples using the L. minor reference genome (Lm7210)</li> <li>L.japonica.reference.bcftools.snps.vcf.gz(.tbi) contains SNPs from all samples using the L. japonica reference genome (Lj9421)</li> </ul>

opencc-by-4.0May 2024View details →

Raw data: Electronic and Structural Property Comparison of a Novel vs. a Commercial Iridium-based OER Catalysts Enabled by Operando Ir L3-edge X-ray Absorption Spectroscopy

Raw data for the manuscript titled: Electronic and Structural Property Comparison of a Novel vs. a Commercial Iridium-based OER Catalysts Enabled by Operando Ir L3-edge X-ray Absorption Spectroscopy

Raw data for "Ancestral structure prediction reveals the conformational impact of the RuBisCO small subunit across time".

The repository contains raw data for the article "Ancestral structure prediction reveals the conformational impact of the RuBisCO small subunit across time". The repository contains: <ol> <li>Dataset for the RbcL and RbcS sequences along with the inferred sequences for the ancestors of interest</li> <li>Phylogenetic tree for the concatenated and separate RbcL-RbcS sequences.</li> <li>Structures for the extant and ancestral RuBisCO complexes used in the study.</li> <li>Solvated pdb files for creating the topology files required for MD-simulations.</li> </ol>

opencc-zeroJun 2024View details →

dryad32/100

Data from: Ecosystem engineers shape ecological network structure and stability: a framework and literature review

Ecosystem engineering is a ubiquitous process where species influence the physical environment and thereby structure ecological communities. However, there has been little effort to synthesise or predict how ecosystem engineering may impact the structure and stability of interaction networks. To assess the current scientific understanding of ecosystem engineering impacts via habitat forming, habitat modification, and bioturbation on interaction networks/food webs, we reviewed the literature covering marine, freshwater, and terrestrial food webs, plant-pollinator networks, and theory. We provide a conceptual framework and identify three major pathways of engineering impact on networks through changes in resource availability and energy flow, habitat heterogeneity, and environmental filtering. These three processes often work in concert and most studies report that engineering increases species richness. This is particularly marked for engineers that increase habitat heterogeneity and thereby the number of available niches. The response of network structure to ecosystem engineering varies, however some patterns emerge from this review. Engineered habitat heterogeneity leads to a higher number of links between species in the networks and increases link density. Connectance can be negatively or positively affected by ecosystem engineer impact, depending on the engineering pathway and the engineer impact of species richness. We discuss how ecosystem engineers can stabilize or destabilize communities through the changes in niche space, diversity, network structure, and the dependency on the engineering impact. Theory and empirical evidence need to inform each other to better integrate ecosystem engineering and ecological networks. A mechanistic understanding how ecosystem engineering traits shape interactions networks and their stability will be important to predict species extinctions and can provide crucial information for conservation and ecosystem restoration.

Data for "Beyond theory driven discovery: hot random search and datum derived structures"

Data associated with "Beyond theory driven discovery: hot random search and datum derived structures" contributed to: FD 2025: Data-driven discovery in the chemical sciences <code>.</code> <code>├── README</code> <code>├── b</code> <code>│   ├── collection</code> <code>│   ├── eddp</code> <code>│   ├── test-105-111</code> <code>│   ├── test-192</code> <code>│   └── test-56</code> <code>├── c</code> <code>│   ├── collection</code> <code>│   ├── collection-0.5eV</code> <code>│   ├── collection-1eV</code> <code>│   ├── manifest-dia</code> <code>│   ├── test-dia-25-48</code> <code>│   ├── test-dia-8-24</code> <code>│   └── test-dia-nosymm-8</code> <code>└── pyrope</code> <code>    ├── airss</code> <code>    ├── collection-gamma-relax</code> <code>    ├── manifest</code> <code>    └── test</code> C J Pickard, 2024 Cambridge

opencc-zeroJun 2024View details →

dryad32/100

Data for: Population structure and inbreeding in wild house mice (Mus musculus) at different geographic scales

House mice (Mus musculus) have spread globally as a result of their commensal relationship with humans. In the form of laboratory strains, both inbred and outbred, they are also among the most widely-used model organisms in biomedical research. Although the general outlines of house mouse dispersal and population structure are well known, details have been obscured by either limited sample size or small numbers of markers. Here we examine ancestry, population structure, and inbreeding using SNP microarray genotypes in a cohort of 814 wild mice spanning five continents and all major subspecies of Mus, with a focus on M. m. domesticus. We find that the major axis of genetic variation in M. m. domesticus is a south-to-north gradient within Europe and the Mediterranean. The dominant ancestry component in North America, Australia, New Zealand, and various small offshore islands is of northern European origin. Next, we show that inbreeding is surprisingly pervasive and highly variable, even between nearby populations. By inspecting the length distribution of homozygous segments in individual genomes, we find that inbreeding in commensal populations is mostly due to consanguinity. Our results offer new insight into the natural history of an important model organism for medicine and evolutionary biology.

openmit-licenseJun 2024View details →

Formation, Structure, and Detectability of the Geminids Meteoroid Stream (Data)

Data generated and analyzed in the Cukier and Szalay (2023) paper "Formation, Structure, and Detectability of the Geminids Meteoroid Stream" (DOI: 10.3847/PSJ/acd538)

Data for PhD Thesis: "Chemical and electronic structure of Cu$_2$O, NiO, and Cu$_2$O-NiO combinatorial material libraries as hole-transport material for halide perovskite solar cells"

Here, the whole data measured during the PhD time of L. CW. Bodenstein-Dresler + Labbook is uploaded. T data in the "data"-folder was measured at HZB with XPS, UPS and IPES.    The PEYS and CPD and XRD data was measured by A. Kama at BIU.

MD data for Structural basis for allosteric regulation of human phosphofructokinase-1

This dataset contains MD data related to the article "Structural basis for allosteric regulation of human phosphofructokinase-1". MD_files.zip: Initial geometries, input files, and final geometries for MD simulations presented in the article. node_degeneracies.xlsx: Results of network path analysis. The spreadsheet contains normalized node degeneracies for residues along the possible paths connecting the C-terminal tail residues and selected residues from the active site and allosteric sites, as shown in Fig S7.

Experimental data supporting the paper "Rep structures can be tuned by ionicity via metastable intermediates in the absence of DNA"

experimental data sets for our paper on the conformational dynamics of Rep helicase "Rep structures can be tuned by ionicity via metastable intermediates in the absence of DNA"

Double Trouble : Multiple infections and the coevolution of virulence-resistance in structured host-parasite populations - Scripts, Data and Supplementary Material

Supplementary material   Contains the Mathematica notebook for analytical and numerical computations, and figure generation. An Rscript used to reproduce the coevolutionary figures from section "Coevolution" The set of appendices in a .pdf file.

Data for: When do contemporary wildfires restore forest structures in the Sierra Nevada?

This dataset contains GeoTIFF raster layers derived from the analyses described in Chamberlain et al. (2024) ("When do contemporary wildfires restore overstore structures in Sierra Nevada forests"). Each layer represents classified predictions of the probability (using a 0.5 threshold) of restorative fire effects for the year 2020 under a mild (burning index = 53) and moderate (burning index = 71) fire weather scenario. The three layers include predicted probabilities for cover restoration, partial restoraiton, and full restoration. Cover restoration suggests that only canopy cover is likely to be restored in subsequent first-entry wildfires, partial restoration indicates that canopy cover and ladder fuel densities are likely to be restored, and full restoration indicates that canopy cover, ladder fuel density, and clump complexity are all likely to be restored. Please refer to the text in Chamberlain et al. (2024) for complete descriptions of each forest structure metric and how the restoration indices were defined.  The codes in each raster layer are as follows: NoData = outside study area 0 = restoration unlikely (probability < 0.5) under mild or moderate fire weather conditions 1 = restoration likely (probability > 0.5) under mild fire weather conditions 2 = restoration likely (probability > 0.5) under moderate fire weather conditions Terms of use: These data are solely for the purpose of general public information; the user should not rely upon the contents of this data for any specific purpose without making independent investigation. The authors assume no responsibility for any risk, loss, or liability that may result from the use of the data. Please contact Caden Chamberlain at cc274@uw.edu if there are any questions or concerns.

Electronic structure properties of the SmartNanoTox data set (nanomaterials) for the use of meta models assesing cytotoxicity

Important set of electronic structure properties data on the SmartNanoTox dataset consisting of large molecular systems representing coated materials. The data were used to study lung inflammation within a service offered through EU Horizon 2020 NanoCommons project.

Experimental and simulation data from Stransky et al. "Ionization by XFEL radiation produces distinct structure in liquid water"

This deposition contains two separate gzipped tarballs: <ol> <li>preprocessed_diffraction_data.tar.gz pre-processed diffraction data, specifically I(q) traces for each X-ray pulse along with associated metadata</li> <li>XMDYN_simulations.tar.gz simulation trajectories produced by the XMDYN code that describe water ionization following an intense X-ray pulse</li> </ol> In more detail, the preprocessed_diffraction_data.tar.gz contains 36 HDF5 files, each corresponding to a single LCLS run. Each HDF5 contains the following data arrays: <pre><code>ebeam Group event_time Dataset {8674/Inf} evr Group fiducials Dataset {8674/Inf} gas_detector Group phase_cav Group probe_energy Dataset {8674/Inf} probe_mag Dataset {8674/Inf} pump_energy Dataset {8674/Inf} pump_mag Dataset {8674/Inf} radial_profile Dataset {8674/Inf, 500} radial_profile_qvalues Dataset {500/8192}</code></pre> The X contains trajectories for 7 different simulations, each in a separate directory. These top level directories are named according to simulation parameters: <div>sim<BOX_SIZE>A_<probe_delay[fs]/singl>_<fraction_of_nominal_fluence></div> <div> </div> <div>Box sizes are in [Å], probe delay is in [fs] or there is only single pulse, 1.00 is the nominal fluence.</div> <div> </div> <div>Each of these directories contain subdirectories recording snapshots every 5 fs.</div> <div>The subdirectory name is the snapshot time in attoseconds.</div> <div>The simulation starts at 0 fs, the pump pulse is centered at the time 30 fs.</div> <div>That means that if there is a probe pulse with delay of 110 fs, it is centered</div> <div>at 140 fs.</div> <div> </div> <div>The snapshot directories contain 6 text files:</div> <ul> <li>econf.dat : list of all atomic configurations observed in the simulation snapshot, each line starts with the atomic Z, followed by the electron configuration</li> <li>T.dat : the electronic configuration for each particle in the simulation, in the form of a list of indices. The n-th element of the list indicates the electronic configuration of the n-th particle in the simulation by specifying a line in econf.dat. For instance, if the 10th element is "0", it indicates the 10th simulated particle has the electronic configuration written on the first line of econf.dat</li> <li>Z.dat : sequence of atomic numbers for all simulated atoms.</li> <li>q.dat : charge of atoms in elementary charge units (redundant data).</li> <li>r.dat : cartesian position in [m], each atom position on one line.</li> <li>v.dat : velocity components in [m/s], each atom on one line.</li> </ul>

Data Sets for: Spiers Memorial Lecture: From Cold to Hot, The Structure and Structural Dynamics of Dense Ionic Fluids

Data Sets for Section 2.1 of Article: Spiers Memorial Lecture: From Cold to Hot, The Structure and Structural Dynamics of Dense Ionic Fluids

Source data of Mirtronstructdb - A comprehensive database of mirtrons with predicted secondary structure

Open the record for dataset details and reuse information.

opencc-by-4.0Feb 2018View details →

Molecular dynamics simulation trajectory data for "Permeability and ammonia selectivity in aquaporin TIP2;1: linking structure to function"

Trajectories and input files corresponding to entries in Supplementary Table S1.

openbsd-3-clauseMar 2018View details →

SQUID: Transcriptomic Structural Variation Detection from RNA-seq -- simulation data part 3

Simulation data part 3 for SQUID software.