Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11,710
datasets available to search
ShareScore release 0.7.1
Dataset results
11,710 results for “interaction”
Molecular dynamics simulations of the interaction of the quadruple mutant human CYP2J2 (R111A + R117A + R382A + R446A) with arachidonic acid (POSES 4-6)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_quadmut_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of the quadruple (R111A + R117A+R382A+R446A) mutant CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p>
Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness"
<p><strong>Raw data for the article:</strong> Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J. Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness. Proceedings of the Royal Society: B. 2019. 286:20192227. https://doi.org/10.1098/rspb.2019.2227</p> <p><strong>This data should be cited as</strong>: Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J (2019). Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness", Bestion et al 2019 Proceedings B. (Version 1). Zenodo. https://doi.org/10.5281/zenodo.3475402</p> <p><strong>This data is composed of</strong> one dataset with 21 columns and a README file</p> <p>Composition of the Bestion_2019_isotopy_dataset_for_zenodo.csv dataset</p> <p>- Individual: numerical index corresponding to each of the 327 individuals in the dataset<br> - Age: age class, J = juvenile (<1 year old), A = adult (1 and 2+ year old)<br> - Sex: F (female) or M (male)<br> - Climate: Present-day climate or Warm climate<br> - Enclosure: enclosure number (10 enclosures, 5 per climatic treatment)<br> - delta13C_september: stable isotope values for delta13C in september<br> - delta15N_september: stable isotope values for delta15N in september<br> - delta13C_september_corrected: stable isotope values for delta13C in september corrected for the stable isotope value of the three invertebrate prey categories<br> - delta15N_september_corrected: stable isotope values for delta15N in september corrected for the stable isotope value of the three invertebrate prey categories<br> - Prop_predator_eaten: proportion of predatory invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_phytophagous_eaten: proportion of phytophagous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_detritivorous_eaten: proportion of detritivorous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Levins_diet_index: levins' dietary index corresponding to lizard diet specialization (with 3 = completely generalist and 1 = completely specialist lizard)<br> - Body_Size_september: lizard body size (snout-vent length in mm)<br> - Body_Mass_september: lizard body mass (in g)<br> - Body_Condition_september: lizard body condition (residuals of body mass by body size)<br> - Microbiota_shannon_index: shannon index representing gut microbial bacteria community diversity<br> - Survival_winter: survival during the winter (1 = survived, 0 = died)<br> - Abundance_predator_enclosure: abundance of predatory invertebrates within the enclosure<br> - Abundance_phytophagous_enclosure: abundance of phytophagous invertebrates within the enclosure<br> - Abundance_detitivorous_enclosure: abundance of detritivorous invertebrates within the enclosure</p> <p> </p> <p> </p>
Prey nutrient content is associated with the trophic interactions of spiders and their prey selection under field conditions
<h2>Materials and Methods</h2> <h2><a name="_Toc58843581"></a><em><span>Fieldwork</span></em></h2> <p><a name="_Hlk173879015"></a><a name="_Hlk56335325"></a><span><span>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae), the two most abundant spider groups in this study, were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground in daylight hours between April and September 2018. Each belt transect was adjacent to a randomly selected crop tramline and were distributed across the entire field and ran its length. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected. The 300 spiders taken forward for molecular dietary analysis in this study were taken from 64 randomly selected locations along the aforementioned transects. </span></span><span><span>Following collection of spiders, 4 m<sup>2</sup> of ground and crop stems was suction sampled <a name="_Hlk173879230"></a>in each of these 64 sampling locations for approximately 30 seconds, with the collected material emptied into a bag and any organisms immediately killed with ethyl-acetate. Suction sampling used a ‘G-vac’ modified garden leaf-blower. All material was later frozen at -20 ºC for storage before sorting in the lab. Sticky trap data were also collected, but were not used in this study as suction sampling was found to represent the interactions of spiders more closely (Cuff, Tercel et al., 2024). These invertebrates were collected for background population densities and macronutrient analysis, not for molecular dietary analysis.</span></span></p> <p><span>All invertebrates were identified to family level using morphological keys: Araneae </span><span><span>(Roberts, 1993)</span></span><span>, Diptera </span><span><span>(Ball, 2008)</span></span><span>, Coleoptera </span><span><span>(Duff, 2012)</span></span><span>, Hymenoptera </span><span><span>(Goulet & Huber, 1993)</span></span><span>, Hemiptera </span><span><span>(Unwin, 2001)</span></span><span>, Collembola </span><span><span>(Dallimore & Shaw, 2013)</span></span><span> and Chilopoda </span><span><span>(Barber, 2008)</span></span><span>. Further identifications were not carried out due to the inability to identify some of the invertebrate groups further via the associated metabarcoding-derived dietary data (e.g., Sciaridae), and the difficulty associated with finer taxonomic resolution of many damaged or immature specimens. The only taxa not identified to family level were springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to differentiate them) which were left at super-family, mites (many of which were immature or in poor condition, or lacked appropriate taxonomic keys) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage); in these cases, these taxonomic assignments were pooled to family-level for later analyses. <a name="_Hlk96098198"></a></span></p> <p><span><span>Extraction, amplification and sequencing of DNA from the individually collected spiders, and its bioinformatic analysis are described by </span></span><span><span><span>Cuff, Tercel, et al. (2022)</span></span></span><span><span> and </span></span><span><span><span>Drake et al. (2022)</span></span></span><span><span> and are also detailed in Supplementary Information 1. In short, dietary metabarcoding was carried out using two primer pairs, one excluding predator DNA and the other amplifying it, to overcome the problem of overamplification of predator DNA </span></span><span><span><span>(Cuff, Kitson, et al., 2023)</span></span></span><span><span>. Amplified DNA was sequenced on an Illumina MiSeq V3 2x300 cartridge, and resultant data screened for false positives following bioinformatic processing via minimum sequence copy thresholds applied according to read counts in controls and control DNA counts present in samples </span></span><span><span><span>(Drake et al., 2022)</span></span></span><span><span>.</span></span></p> <p><span> </span></p> <h2><a name="_Toc58843582"></a><em><span>Macronutrient determination</span></em></h2> <p><span>Specimens were taken for macronutrient analysis from the same suction samples collected for invertebrate community identification. Representatives were taken from each family found in the community samples for which specimens were intact, in visually good condition and relatively clean of soil and other contaminants. If specimens were from a relatively uncommon family but unclean, soil and other surface contaminants were physically removed, and the specimen then momentarily dipped in water to remove remaining surface contaminants without greatly dislodging surface lipids. <a name="_Hlk173879439"></a>Macronutrient contents were determined following the MEDI protocol </span><span><span><span>(Cuff, Wilder, et al., 2021; Cuff & Wilder, 2021)</span></span></span><span><span> with minor alterations to account for the small size of most of the invertebrates processed </span></span><span><span><span>(Cuff, 2021)</span></span></span><span><span> and with the omission of exoskeletal measurement. </span></span><span>During extraction, half volumes (i.e., 500 µl) of solvents were used. For the lipid assays, 15 µl of sulfuric acid was added for a 15 min incubation, followed by only 200 µl of vanillin reagent to increase the concentration and development of analyte for more accurate readings from smaller invertebrates. Lipid and protein standard series were diluted to 50% of the concentration specified in the original protocol (i.e., 0-1 mg ml<sup>-1</sup>). Carbohydrate assays used 140 µl of reagent with 30 min incubation at 92 °C followed by a further 30 min at room temperature. Carbohydrate standard series were diluted to 1 % of the concentrations specified in the original protocol (i.e., 0-0.02 mg ml<sup>-1</sup>) to ensure signals overcame the higher limit of detection relative to typical invertebrate carbohydrate content. <span> </span><a name="_Hlk173926384"></a>Mean macronutrient contents were calculated for each taxon and converted into proportions of the total macronutrient mass detected for each taxon (i.e., macronutrient values are given as % total macronutrient mass). Macronutrient data were allocated to each prey taxon. Where macronutrient data were not available for a family (due to no or very few individuals being present in vacuum samples), average data for that order were used.</span></p> <p><span> </span></p> <h2><a name="_Toc58843584"></a><em><span>Statistical analysis</span></em></h2> <p><span>We have assessed nutritional dynamics through a combination of multivariate models and network-based null modelling. All analyses were conducted in R v.4.0.3 </span><span><span>(R Core Team, 2020)</span></span><span>. </span></p> <p><span>To compare the nutritional balance of prey consumed by different spider groups, the mean nutrient contents of all prey consumed by each spider were calculated and compared using a multivariate linear model (MLM) via the ‘manylm’ command in mvabund </span><span><span>(Wang et al., 2012)</span></span><span>.<span> </span><span>Differences were visualised using ternary plots via ‘ggtern’ </span></span><span><span>(Hamilton & Ferry, 2018)</span></span><span> and ‘ggplot2’ </span><span><span>(Wickham, 2016)</span></span><span>. How spider diets differ between spider groups (genera, sexes and life stages) and how this is related to the nutrient contents of those prey was assessed using a fourth corner analysis (FCA). Fourth corner analyses assess how the relationship between the presence of species (or consumed resources in a dietary context) and environmental (or consumer) traits relates to species traits (or prey traits; </span><span><span>(Brown et al., 2014)</span></span><span>. </span><span>First, overall relationships between dietary composition and spider traits were assessed using a multivariate generalized linear model (MGLM) via the ‘manyglm’ command in the ‘mvabund’ package </span><span><span>(Wang et al., 2012)</span></span><span> with a binomial error family<span>. </span>These relationships were identified via likelihood ratio test using the ‘anova.manyglm’ command. A fourth corner analysis was performed using the ‘trait.glm’ command in mvabund with the ‘R’, ‘Q’ and ‘L’ matrices representing dietary detections of prey families in each spider, spider trait data (genus (a proxy for many unmeasured traits such as morphology), sex and life stage) and prey proportional macronutrient contents, respectively, with a binomial error family. Log-likelihood ratio tests were carried out using the ‘anova.traitglm’ command with 999 bootstrap iterations and Monte-Carlo resampling. The model was repeated with the least absolute shrinkage and selection operator (LASSO) applied, which is a method of penalised likelihood that reduces model terms to zero if they lack predictive power (i.e., do not reduce the Bayesian information criterion), thereby selecting models with greater predictive accuracy </span><span><span>(Brown et al., 2014)</span></span><span>. </span></p> <p><span>To assess whether the proportions of mean prey nutrient contents deviated from those expected based on random foraging, null diets were simulated using network-based null models in ‘econullnetr’ </span><span><span>(Vaughan et al., 2018)</span></span><span> with the ‘generate_null_net’ command. The ‘generate_null_net_indiv’ function </span><span><span>(Cuff, Windsor, et al., 2023)</span></span><span> was used to generate null diets for each individual spider based on local prey communities determined via suction sampling. The mean prey macronutrient contents of spider diets were compared between expected and observed diets </span><span>using a MLM in mvabund, and significant differences visually represented through a ternary plot using ggtern<span>. To ascertain how differences between spider groups factor into any deviations from random nutrient intake, the difference in macronutrient proportions between expected and observed spider diets was also compared between spider genera, life stages and sexes in a MLM.</span></span></p> <p><span>To relate prey preferences of different spider groups to different prey and their macronutrient contents, observed interactions were compared against null models based on prey abundances using the ‘generate_null_net’ command in econullnetr (as above) for each of the spider groups and, separately, for individual spiders. Ternary plots representing preference effect sizes for prey of varying macronutrient contents were generated using the group-specific data via ‘ggtern’. The observed interactions of individual spiders were divided by the interactions expected in the null model; infinite values (i.e., zero interactions expected and more than zero observed) and NAs (e.g., no interactions expected nor observed) were converted to zero. These observed/expected values were compared between spider groups via permutational multivariate analysis of variance (PerMANOVA). These results were visualised by plotting mean standardised effect sizes for each spider genus, sex and life stage from the prey choice null models via ggplot2. </span></p>
Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.
<p>Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload.</p> <p>Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output. </p> <p>Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.</p>
Twin Test 2: Wake interactions of a cluster of turbines and wake steering techniques. Wind tunnel data.
<p>The aerodynamic performance of two identical wind turbine models was characterized under various static and dynamic conditions in a synchronous configuration within the wind tunnel test section. Two experimental campaigns were performed at Technische Universität München (TUM) and at the National Technical University of Athens (NTUA) to investigate wake flow control techniques. This document contains the necessary information to understand the performed experiments and to access and use the available data. While both experimental set ups are detailed, only data from the TUM campaign are available at the time of writing, as the NTUA campaign results will form Phase II of an ongoing blind test campaign and cannot be published.</p>
Synthetic dataset of user interactions - postpartum depression.csv
<p>A synthetic data set composed of 200 users' utterances as possible answers to questions related to these topics:</p> <p>(i) Feeling sad or Tearful<br>(ii) Irritable towards baby & partner<br>(iii) Trouble sleeping at night<br>(iv) Problems concentrating or making decision<br>(v) Overeating or loss of appetite<br>(vi) Feeling of guilt<br>(vii) Problems of bonding with baby <br>(viii) Suicide attempt</p>
PhasAGE Training School 1 - Phase separation in virus-host interactions- LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Structure and protein interactions of repeated and low complexity regions - LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
<p>This dataset contains replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". The dataset consists of pickled Pandas DataFrame files, along with training, validation, and (for DB5-Plus) test filename lists for cross-validation, that can be used to develop and evaluate protein interface prediction models. This dataset also contains the externally generated residue-level PSAIA and HH-suite3 features for users' convenience (e.g. raw MSAs and profile HMMs for each protein complex). Our GitHub repository linked in the "Additional notes" metadata section below provides more details on how we parsed through these files to create our cross-validation datasets. The GitHub repository for DIPS-Plus also includes scripts that can be used to impute missing feature values and convert the final "raw" complexes into DGL-compatible graph objects. Since our final DGL graph representation for each complex uses PyTorch tensors in its construction of residue embeddings, the final representation of each complex can easily be adapted to fit the users' needs (e.g. feeding a complex's 2D residue feature tensors into a convolutional neural network).</p>
Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death
<p>This dataset contains the raw data that were used for the publication entitled, "Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death" published in Nanomaterials on 4 August 2021.</p> <p>Abstract:</p> <p>DNA nanotechnology offers to build nanoscale structures with defined chemistries to precisely position biomolecules or drugs for selective cell targeting and drug delivery. Owing to the negatively charged nature of DNA, for delivery purposes DNA is frequently conjugated with hydrophobic moieties, positively charged polymers/peptides, cell surface receptor recognizing molecules or antibodies. Here, we designed and assembled cholesterol-modified DNA nanotubes to interact with cancer cells and conjugated them with cytochrome c to induce cancer cell apoptosis. By flow cytometry and confocal microscopy, we observed that DNA nanotubes efficiently bound to the plasma membrane as a function of the number of conjugated cholesterol moieties. The complex was taken up by the cells and localized to the endosomal compartment. Cholesterol-modified DNA nanotubes, but not unmodified ones, induced increased membrane permeability, caspase activation and cell death. Irreversible inhibition of caspase activity, with Z-VAD-FMK, however, only partially prevented cell death. Cytochrome c conjugated DNA nanotubes were also efficiently taken up but did not increased the rate of cell death. These results demonstrate that cholesterol-modified DNA nanotubes induce cancer cell death associated with increased cell membrane permeability and only partially dependent on caspase activity, consistent with a combined form of apoptotic and necrotic cell death. DNA nanotubes may be further developed as primary cytotoxic agents, or drug delivery vehicles, through cholesterol mediated cellular membrane interactions and uptake.</p>
Nuclear Magnetic resonance Dataset of 2D spectra of S100B and Tau to study their protein-protein interaction
<p>Nuclear Magnetic resonance dataset of 2D spectra corresponding to raw data of research published in Nature Communication in a communication entitled "Dynamic interactions and Ca2+ 1 -binding modulate the holdase-type chaperone activity of S100B preventing tau aggregation and seeding" by Moreira G. et al.</p> <p>Dataset corresponds to</p> <p>raw data files in Bruker format of NMR 2D spectra (ser), associated with files of acquisition parameters and processing parameters (pdata),</p> <p>files in .ucsf format that can be read with NMRFAM-Sparky (free download) of 2D spectra (in sub-directory pdata/1)</p> <p>files of chemical shift value lists that can be read as text files or in NMRFAM sparky together with the corresponding ucsf files.</p> <p>physico-chemical conditions are found in title in pdata\1</p> <p>Data were acquired on a Bruker 900-MHz spectrometer equipped with a triple-resonance cryogenic probe (Bruker, Karlsruhe, Germany)</p>
Predicting COVID-19 Incidence Through Spatiotemporal Human Interactions
<p>This repository contains data (features) necessary to run STXGB model. STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Flow-topography interactions in the western tropical Atlantic boundary off Northeast Brazil.
<p>Figures and other media files of the paper Flow-topography interactions in the western tropical Atlantic boundary 2off Northeast Brazil.</p>
Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions (dataset)
<p>This repository contains data (features) necessary to run STXGB model and accompanies the paper titled "Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions".</p> <p> </p> <p>STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code
<p>Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)</p>
Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction
<p>This is the data set supporting the analyses performed in the article entitled "Warming of experimental plant-pollinator communities advances phenologies, alters traits, reduces interactions, and depresses reproduction", by Natasha de Manincor, Alessandro Fisogni, and Nicole E. Rafferty, published in Ecology Letters (2023, 26:323-334, <a href="https://doi.org/10.1111/ele.14158">https://doi.org/10.1111/ele.14158</a>).</p> <p>The experiment has been performed in the greenhouse facilities at the University of California, Riverside, in 2021.</p> <p>The two treatments analyzed are ambient vs warmed (+ 4 °C), the focal pollinator species is <em>Osmia lignaria</em>, and the three focal plant species are <em>Collinsia heterophylla</em>, <em>Nemophila menziesii</em>, and <em>Phacelia campanularia</em>.</p> <p>Data are tab separated .txt files.</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
Supplementary Materials to the publication Wood structure explained by complex spatial source-sink interactions
<p>Model output and visualisation scripts to the publication Wood structure explained by complex spatial source-sink interactions. A readme explains the file origin. model output files and their variables and units are described within the .R analysis code.</p>
3D Charged Particles Dataset - Roto-translated Local Coordinate Frames for Interacting Dynamical Systems
<p>This repository contains the "<strong>3D charged particles</strong>" dataset from the paper</p> <blockquote> <p><strong>Roto-translated Local Coordinate Frames for Interacting Dynamical Systems</strong><br> <a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>, <a href="https://menaveenshankar.github.io/">Naveen Shankar Nagaraja</a>, <a href="https://egavves.com/">Efstratios Gavves</a><br> NeurIPS 2021<br> <a href="https://arxiv.org/abs/2110.14961">https://arxiv.org/abs/2110.14961</a><br> <a href="https://github.com/mkofinas/locs">https://github.com/mkofinas/locs</a></p> </blockquote> <p>It contains simulations of trajectories of 5 charged particles in 3 dimensions, interacting via Coulomb forces.</p> <p>There are 30,000 simulations for training, 5,000 for validation, and 5,000 for testing.</p> <p>Train and validation simulations last for 99 timesteps, while test simulations last for 99 timesteps.</p> <p>The features comprise positions and velocities of particles, while edges describe the product of pairwise charges.</p>
Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces (Figures)
<p>High resolution figures related to the below manuscript:</p> <p>Atul Deshpande, Melanie Loth, et al., <a href="https://doi.org/10.1101/2022.06.02.490672">Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces</a>. <em>bioRxiv</em> 2022. doi:10.1101/2022.06.02.490672</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.