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1,454 results for “large scale”
Novel Activation Modulating Sites in a GPCR Identified with a Large-Scale Alchemical Mutation Scan
<p>This dataset contains the starting configurations and mdp files for the alchemical free energy calculations using pmx.<br>The MD parameter files (*.mdp) for the forward reaction (lambda 0 to 1, wt to mutant) and reverse reaction (lambda 1 to 0, mutant to wt) start with f (forward) and r (reverse). Mdp files are available for the energy minimization (em), npt (npt), equilibrium run (equilibrium) and non equilibrium alchemical transition (nonequilibrium). <br>The starting configurations for mutants of the DRD2 receptor can be found in WT7jvr and WT6cm4 for the active and inactive state respectively. Mutant folders are labeled with the residue number and the amino acid it is mutated to.</p>
Datasets for evaluating SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data
<p>This resource contains pre-processed A. thaliana root , the H. sapiens aortic valve datasets, PBMC Covid atlas and public 10x datasetse used in the paper, SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data. The raw datasets provided in the links below are pre-processed for quality control with respect to both cells and genes. </p> <p>A. thaliana datasets are sourced from the following locations at <a href="https://www.ebi.ac.uk/gxa/sc/home">Single-cell Gene expression Atlas </a>and <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus (GEO)</a>:</p> <ol> <li>E-GEOD-121619 : <a title="E-GEOD-121619" href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results</a></li> <li>E-GEOD-152766 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results</a></li> <li>E-GEOD-158761 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results</a></li> <li>E-GEOD-123013 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results</a></li> </ol> <p>H. sapiens datasets are obtained from the NCBI database : <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/ </a></p> <ol> <li>GSE152766: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766</a></li> <li>GSE158761: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761</a></li> </ol> <p>All COVID atlas datasets are from: <a href="http://covid19.cancer-pku.cn">http://covid19.cancer-pku.cn</a> . covid_atlas_data1.zip contains the h5ad files and covid_atlas_data2.zip contains the Seurat rds files.</p> <p>PBMC datasets are from the following public sources:</p> <table> <tbody> <tr> <td>Dataset Name</td> <td>Chemistry Version</td> <td>Web Link</td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high</a></td> </tr> <tr> <td>20k Human PBMCs, 3' HT v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0">https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0</a></td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium Controller</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 1)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 5)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0</a></td> </tr> <tr> <td>10k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v2 chemistry)</td> <td>v2.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>Fresh 68k PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor B)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor C)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0</a></td> </tr> <tr> <td>PBMCs from a Healthy Donor: Whole Transcriptome Analysis</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0">https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0</a></td> </tr> <tr> <td>PBMC 600K</td> <td>v1</td> <td><a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads">https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads</a></td> </tr> <tr> <td>GSM4560071</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071</a></td> </tr> <tr> <td>GSM4560074</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074</a></td> </tr> <tr> <td>GSM4560070</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070</a></td> </tr> </tbody> </table> <p>References for the Datasets :</p> <ol> <li>H. sapiens dataset: Kang Xu, Shangbo Xie,Yuming Huang,Tingwen Zhou, Ming Liu, Peng Zhu, Chunli Wang, Jiawei Shi, Fei Li,Frank W. Sellke and Nianguo Dong (2020) Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.</li> <li>E-GEOD-152766: Shahan R, Hsu C, Nolan TM, Cole BJ, Taylor IW et al. (2020) A single cell Arabidopsisroot atlas reveals developmental trajectories in wild type and cell identity mutants.</li> <li>E-GEOD-121619: Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. (2019) Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana.</li> <li>E-GEOD-123013: Ryu KH, Huang L, Kang HM, Schiefelbein J. (2019) Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells.</li> <li>E-GEOD-158761: Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. (2020) A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.</li> <li>COVID Atlas Reference: Xianwen Ren, Wen Wen, Xiaoying Fan et.al. (2021) COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas</li> <li>PBMC data are downloaded from respective links</li> </ol>
Data from: Unravelling large-scale patterns and drivers of biodiversity in dry rivers
<p>We conducted a coordinated experiment and a metabarcoding approach on environmental DNA targeting multiple taxa (i.e. Archaea, Bacteria, Fungi, Algae, Protozoa, Nematoda, Arthropoda, and Streptophyta). Dry sediments were collected from 84 non-perennial rivers across 19 countries on four continents to investigate biodiversity patterns and drivers.</p>
Supplementary data to the paper "Large scale voxel-based FEM formulation for NMR relaxation in porous media"
<p>This repository contains supplementary data to the paper "Large scale voxel-based FEM formulation for NMR relaxation in porous media", which is being considered for publication. The files are:<br><br>**_data.zip - Binary image files;</p> <p>FEM_T2_**.csv - T2 simulation results using finite elements;</p> <p>FEM_**_inv.csv - T2 inversion for the finite element simulations;</p> <p>RW_T2_**.csv - T2 simulation results using random walk;</p> <p>RW_**_inv.csv - T2 inversion for the random walk simulations;</p> <p>local_digital_correction.c - Vector containing the local geometry correction factors, accessed integers formed by the 8-bit binary key of the node neighborhood;</p> <p>Where ** is the rock type:</p> <p>AC - Austin Chalk</p> <p>BS - Berea Stripe</p> <p>DP - Desert Pink</p> <p>IB - Idaho Brown</p> <p>Obs.: in order to obtain the relaxation curves the following parameters were utilized in the simulations:</p> <p>Bulk diffusivity: 2500 micro-m2/s (all rock samples) </p> <p>Bulk relaxation: 2.6 s (all rock samples)</p> <p>Voxel size: {1.0; 2,0; 0.9; 2.0} micro-m (for AC, BS, DP, IB respectively)</p> <p>Surface relaxivity: {23.3; 12.1; 12.3; 8.3} micro-m/s (for AC, BS, DP, IB respectively)</p>
Large-scale UK Biobank Whole Body Atlases
<p>Population atlases are commonly utilised in medical imaging to facilitate the investigation of variability across populations. <br>Such atlases enable the mapping of medical images into a common coordinate system, promoting comparability and enabling the study of inter-subject differences. <br>Constructing such atlases becomes particularly challenging when working with highly heterogeneous datasets, such as whole-body images, where subjects show significant anatomical variations.<br>In this work, we propose a pipeline for generating a standardised whole-body atlas for a highly heterogeneous population by partitioning the population into anatomically meaningful subgroups. <br>Using magnetic resonance (MR) images from the UK Biobank dataset, we create six whole-body atlases representing a healthy population average. <br>We furthermore unbias them, and this way obtain a realistic representation of the population.<br>In addition to the anatomical atlases, we generate probabilistic atlases that capture the distributions of abdominal fat (visceral and subcutaneous) and five abdominal organs across the population (liver, spleen, pancreas, left and right kidneys).<br>We demonstrate a clinical application of these atlases, using the differences between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects from the atlas space.<br>With this work, we make the constructed anatomical and label atlases publically available and anticipate them to support medical research conducted on whole-body MR images.</p>
Large-scale full-length HTT Q19 and Q42 purification from HEK293T – 2018/04/16
<p>Huntingtin structure-function open lab notebook project. Large-scale full-length HTT Q19 and Q42 purification from HEK293T – 2018/04/16.</p>
Large-scale full-length HTT Q23 purification from HEK293T – 2018/04/30
<p>Huntingtin structure-function open lab notebook project. Large-scale full-length HTT Q23 purification from HEK293T – 2018/04/30.</p>
Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software
<p>Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software, SCAM'13.</p> <p>https://www.monperrus.net/martin/companion-diversity-api-usages</p>
Data from: A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration
<p>This is a complete dataset for achieving results of the manuscript "<strong>A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration</strong>". </p> <p> </p> <p><strong>Abstract: </strong></p> <ol> <li>Achieving global targets for forest restoration will require cost-effective strategies to return agricultural land to forest, while minimizing implementation costs and negative outcomes for agricultural production.</li> <li>We present a landscape approach for optimizing the cost-effectiveness of large-scale forest restoration. Across three different landscapes within Brazil's Atlantic Forest biodiversity hotspot, we modelled landscape scenarios based on spatially-explicit data on the probability of natural regeneration, restoration costs, land opportunity costs, and forest restoration outcomes for increasing carbon stocking and landscape connectivity<em>.</em> We compare benefits of our cost-reduction approach to the legally mandated riparian restoration and randomly distributed approaches.</li> <li>Compared with riparian prioritization and considering both implementation and opportunity costs, our cost-reduction scenario produced the greatest savings (20.9%) in mechanized agricultural landscapes.</li> <li>When only considering implementation costs, our cost-reduction scenario led to the highest savings (38.4%) in the landscape with highest forest cover where natural regeneration potential is highest and enables cost-effective carbon stocking and connectivity.</li> </ol> <p><em>Synthesis and applications.</em> We present a guide for forest restoration planning that maximizes specific outcomes with minimal costs and reduction of agricultural production. Furthermore, we show how policies could encourage prioritization of low-cost restoration via natural regeneration, increasing cost-effectiveness. While our study focuses on Brazil’s Atlantic Forest, the approach can be parameterized for other regions.</p> <p><strong>Resumo:</strong></p> <ol> <li>Atingir metas globais para a restauração florestal exigirá estratégias economicamente viáveis para transformar terras agrícolas em floresta, minimizando custos de implementação e os resultados negativos para a produção agrícola.</li> <li>Apresentamos uma abordagem de paisagem para otimizar a relação custo-eficácia da restauração florestal em larga escala. Em três diferentes paisagens, no Bioma da Mata Atlântica, modelamos cenários baseados em dados espacialmente explícitos sobre a probabilidade de regeneração natural, custos de restauração, custos de oportunidade da terra e resultados de restauração florestal com o objetivo de aumentar o estoque de carbono e a conectividade da paisagem. Por fim, comparamos os benefícios da nossa abordagem de redução de custos com a tradicional abordagem de restauração da paisagem em zonas ripárias (áreas de preservação permanente) e abordagens de espacialidade aleatoriamente distribuídas.</li> <li>Comparado com a priorização ripária e considerando os custos de implementação e de oportunidade, nosso cenário de redução de custos produziu as maiores economias (20,9%) em paisagens agrícolas mecanizadas.</li> <li>Ao considerar apenas os custos de implementação, nosso cenário de redução de custos levou à maior economia (38,4%) na paisagem com maior cobertura florestal, onde o potencial de regeneração natural é maior e permite uma melhor relação de custo-oportunidade no estoque de carbono e na conectividade da paisagem.</li> </ol> <p><em>Síntese e aplicações.</em> Apresentamos aqui um guia para o planejamento de restauração florestal que maximiza resultados específicos com redução de custos e mínima influência na produção agrícola. Além disso, mostramos como políticas públicas poderiam incentivar a priorização da restauração de baixo custo via regeneração natural, aumentando a relação custo-benefício. Enquanto nosso estudo se concentra na Mata Atlântica do Brasil, a abordagem pode ser parametrizada para outras regiões.</p>
Bubble and ppt data set at CIEM large scale wave flume
<p>The present work was developed in work of an internal UPC project. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona.</p> <p>The data set aims to evaluate the false reading of Optical Backscatter Sensors produced by air bubbles at breaking conditions. In order to do that a set of different wave conditions (wave height ranging from 0.2 up to 0.85 and period from 2 to 7 s) have been tested at two different Still water conditions (2.5 and 2.65) with a barred profile in the wave flume.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p>
Large scale expression and purification of full-length huntingtin Q23 with and without HAP40 from baculoviral expression system production in sf9 insect cells – 2018/06/04
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment and rationale</strong> - Purified huntingtin samples with and without stabilising binding partners (e.g. HAP40) are required for use in structural and functional studies, in particular SAXS experiments. </p>
Large scale expression and purification of full-length huntingtin Q23 with HAP40 from baculoviral expression system production in sf9 insect cells – 2018/05/28
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment and rationale</strong> - Purified huntingtin samples with stabilising binding partners (e.g. HAP40) are required for use in structural and functional studies, in particular SAXS experiments. </p>
Large scale expression and purification of full-length huntingtin Q19 and Q23 from baculoviral expression system production in sf9 insect cells – 2018/06/18
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment and rationale</strong> - Purified huntingtin samples are required for use in structural and functional studies. </p>
Large scale expression and purification of full-length huntingtin Q23 and Q54 from baculoviral expression system production in sf9 insect cells – 2018/07/06
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment and rationale</strong> - Purified huntingtin samples are required for use in structural and functional studies. </p>
Large scale expression and purification of full-length huntingtin Q23 with HAP40 from baculoviral expression system production in sf9 insect cells – 2018/07/11
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment and rationale</strong> - Purified huntingtin samples are required for use in structural and functional studies. </p>
Large scale expression and purification of full-length huntingtin Q42 from baculoviral expression system production in sf9 insect cells – 2018/07/10
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment and rationale</strong> - Purified huntingtin samples are required for use in structural and functional studies. </p>
Large scale expression and purification of huntingtin fragment A667-C3140 from baculoviral expression system production in sf9 insect cells – 2018/08/02
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale: </strong>To obtain monodisperse and conformationally constrained huntingtin protein samples for high resolution structural biology.</p>
FIG. 2 in Les mantes (Dictyoptera, Mantodea) du massif du Mitaraka (Guyane), in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 2. — Liturgusa milleri Svenson, 2014, femelle. Photo S. Hugel.
FIG. 1 in Les mantes (Dictyoptera, Mantodea) du massif du Mitaraka (Guyane), in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 1. — Bantia fusca Chopard, 1912, mâle. Photo S. Hugel.
FIG. 6 in Les mantes (Dictyoptera, Mantodea) du massif du Mitaraka (Guyane), in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 6. — Plesiacanthops tuberculata (Saussure, 1870), juvénile. Photo S. Hugel.
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.