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29,897 results for “activism”
USP5 Expression and Purification & Development of a Mass Spectrometry USP5 Catalytic Activity Assay
<p>Expression and purification of full length USP5<sup>1-835</sup> for optimization of a mass spectrometry (MS) based assay to detect polyubiquitin cleavage</p>
Macrophage Secreted TGF-β1 Contributes to Fibroblast Activation and Ureteral Stricture Following Ablation Injury
<p>Original dataset for figures.</p>
SINS database - Node 1 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://kuleuvenadvise.github.io/SINS_database/">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Strong gene activation in plants with genome-wide specificity using a new orthogonal CRISPR/Cas9-based Programmable Transcriptional Activator.
<p>This data set correspond to the supporting data generated in the manuscript: Strong gene activation in plants with genome-wide specificity using a new orthogonal CRISPR/Cas9-based Programmable Transcriptional Activator. </p>
359,569 commits with source code density; 1149 commits of which have software maintenance activity labels (adaptive, corrective, perfective)
<p>This dataset comes as SQL-importable file and is compatible with the widely available MariaDB- and MySQL-databases.</p> <p>It is based on (and incorporates/extends) the dataset "<em>1151 commits with software maintenance activity labels (corrective,perfective,adaptive)</em>" by Levin and Yehudai (<a href="https://doi.org/10.5281/zenodo.835534">https://doi.org/10.5281/zenodo.835534</a>).</p> <p>The extensions to this dataset were obtained using <em>Git-Tools</em>, a tool that is included in the <strong>Git-Density</strong> (<a href="https://doi.org/10.5281/zenodo.2565238">https://doi.org/10.5281/zenodo.2565238</a>) suite. For each of the projects in the original dataset, Git-Tools was run in <em>extended</em> mode.</p> <p>The dataset contains these tables:</p> <ul> <li><strong>x1151</strong>: The original dataset from Levin and Yehudai. <ul> <li>despite its name, this dataset has only 1,149 commits, as two commits were duplicates in the original dataset.</li> <li>This dataset spanned 11 projects, each of which had between 99 and 114 commits</li> <li>This dataset has <strong>71</strong> features and spans the projects <em>RxJava, hbase, elasticsearch, intellij-community, hadoop, drools, Kotlin, restlet-framework-java, orientdb, camel</em> and <em>spring-framework</em>.</li> </ul> </li> <li><strong>gtools_ex</strong> (short for <em>Git-Tools, extended</em>) <ul> <li>Contains <strong>359,569</strong> commits, analyzed using Git-Tools in extended mode</li> <li>It spans all commits and projects from the x1151 dataset as well.</li> <li>All 11 projects were analyzed, from the initial commit until the end of January 2019. For the projects <em>Intellij</em> and <em>Kotlin</em>, the first 35,000 resp. 30,000 commits were analyzed.</li> <li>This dataset introduces <strong>35 new</strong> features (see list below), 22 of which are <em><strong>size</strong></em>- or <em><strong>density</strong></em>-related.</li> </ul> </li> </ul> <p>The dataset contains these views:</p> <ul> <li><strong>geX_L</strong> (short for Git-<em>tools, extended, with labels</em>) <ul> <li>Joins the commits' labels from <em>x1151</em> with the extended attributes from <em>gtools_ex</em>, using the commits' hashes.</li> </ul> </li> <li><strong>jeX_L</strong> (short for <em>joined, extended, with labels</em>) <ul> <li>Joins the datasets <em>x1151</em> and <em>gtools_ex</em> entirely, based on the commits' hashes.</li> </ul> </li> </ul> <p> </p> <p>Features of the <strong>gtools_ex</strong> dataset:</p> <ul> <li><strong>SHA1</strong></li> <li><strong>RepoPathOrUrl</strong></li> <li><strong>AuthorName</strong></li> <li><strong>CommitterName</strong></li> <li><strong>AuthorTime </strong>(UTC)</li> <li><strong>CommitterTime </strong>(UTC)</li> <li><strong>MinutesSincePreviousCommit</strong>: Double, describing the amount of minutes that passed since the previous commit. Previous refers to the <strong>parent</strong> commit, not the previous in time.</li> <li><strong>Message</strong>: The commit's message/comment</li> <li><strong>AuthorEmail</strong></li> <li><strong>CommitterEmail</strong></li> <li><strong>AuthorNominalLabel</strong>: All authors of a repository are analyzed and merged by Git-Density using some heuristic, even if they do not always use the same email address or name. This label is a unique string that helps identifying the same author across commits, even if the author did not always use the exact same identity.</li> <li><strong>CommitterNominalLabel</strong>: The same as <em>AuthorNominalLabel</em>, but for the committer this time.</li> <li><strong>IsInitialCommit</strong>: A boolean indicating, whether a commit is preceded by a parent or not.</li> <li><strong>IsMergeCommit</strong>: A boolean indicating whether a commit has more than one parent.</li> <li><strong>NumberOfParentCommits</strong></li> <li><strong>ParentCommitSHA1s</strong>: A comma-concatenated string of the parents' SHA1 IDs</li> <li><strong>NumberOfFilesAdded</strong></li> <li><strong>NumberOfFilesAddedNet</strong>: Like the previous property, but if the net-size of all changes of an added file is zero (i.e. when adding a file that is empty/whitespace or does not contain code), then this property does not count the file.</li> <li><strong>NumberOfLinesAddedByAddedFiles</strong></li> <li><strong>NumberOfLinesAddedByAddedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesDeleted</strong></li> <li><strong>NumberOfFilesDeletedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesDeletedByDeletedFiles</strong></li> <li><strong>NumberOfLinesDeletedByDeletedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesModified</strong></li> <li><strong>NumberOfFilesModifiedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfFilesRenamed</strong></li> <li><strong>NumberOfFilesRenamedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesAddedByModifiedFiles</strong></li> <li><strong>NumberOfLinesAddedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByModifiedFiles</strong></li> <li><strong>NumberOfLinesDeletedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesAddedByRenamedFiles</strong></li> <li><strong>NumberOfLinesAddedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByRenamedFiles</strong></li> <li><strong>NumberOfLinesDeletedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>Density</strong>: The ratio between the two sums of all lines added+deleted+modified+renamed and their resp. gross-version. A density of zero means that the sum of net-lines is zero (i.e. all lines changes were just whitespace, comments etc.). A density of of 1 means that all changed net-lines contribute to the gross-size of the commit (i.e. no useless lines with e.g. only comments or whitespace).</li> <li><strong>AffectedFilesRatioNet</strong>: The ratio between the sums of <em>NumberOfFilesXXX</em> and <em>NumberOfFilesXXXNet</em></li> </ul> <p> </p> <p>This dataset is supporting the paper <strong>"<em>Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities</em></strong><strong>"</strong>, as submitted to the <em>QRS2019</em> conference (The 19th IEEE International Conference on Software Quality, Reliability, and Security). Citation: Hönel, S., Ericsson, M., Löwe, W. and Wingkvist, A., 2019. Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities. In <em>The 19th IEEE International Conference on Software Quality, Reliability, and Security</em>.</p>
PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity
<p><strong>Introduction</strong></p> <p>These are the <em>Summary Level-data</em> as presented in:</p> <p>"PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity". <em>Nature Communications </em>volume 9, Article number: 2904 (2018) doi: 10.1038/s41467-018-04766-9 </p> <p>If you use these data please cite the corresponding manuscript, which can be downloaded here: https://www.nature.com/articles/s41467-018-04766-9. When you have any questions or comments regarding this study or these files, please contact me via:</p> <p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p> <p> </p> <p><strong>Files and description</strong></p> <p>The file PR_interval_July2018_summary_results.txt is gzipped and contains the PR interval GWAS meta-analysis summary results of 92,000 European samples. The imputation reference panel used for most studies was HapMap2 (please note: hg18, build36). </p> <ul> <li><em>SNP</em> - variantID (rsID).</li> <li><em>CHR</em> - chromosome numbers [1-22 and X].</li> <li><em>POS</em> - base pair position, hg18 / build36.</li> <li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li> <li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li><em>BETA_FIXED</em> - beta from the fixed-effects model.</li> <li><em>SE_FIXED </em>- standard error from the fixed-effects model.</li> <li><em>P_FIXED </em>- P-value from the fixed-effects model.</li> </ul>
On the Origin of Sub-subgiant Stars. II. Binary Mass Transfer, Envelope Stripping, and Magnetic Activity
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017ApJ...840...67L">Leiner et al. (2017)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aa6aff">10.3847/1538-4357/aa6aff</a></p>
Supplementary Data for "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation"
<p>DFT-optimized structures, energies, and computed physicochemical properties of metal-organic frameworks that correspond with work in "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation" (DOI: <a href="https://pubs.acs.org/doi/10.1021/acscatal.8b05178">10.1021/acscatal.8b05178</a>).</p>
Dataset on production, processing and trading activities in Ghana's cocoa value chain
<p>This covers data on production, processing and trading activities in Ghana's cocoa value chain that were used to populate a stock-and flow diagram representing the baseline model of the cocoa value chain. Data are retrieved from different secondary sources, collated in excel sheets</p>
AEROARMS Visual servoing with actively movable camera
<p>This dataset includes a library of elementary behaviours, namely atomic tasks to be assigned to a dual arm aerial manipulator in a priority order. The devised approach, developed within the EU funded research project AEROARMS (AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance) funded by the European Commission under Horizon 2020 Program (grant agreement No. 644271), is based on a set-based inverse kinematics control [1]. For each elementary task the Jacobian matrix and the task function are provided.</p> <p>The code has been developed in C++ under ROS environment and it has been experimentally tested on the AEROARMS dual-arm prototype developed by the University of Seville constituted by an under-actuated multi-rotor with two arms, each of them characterized by 4 DOFs. The total number of DOFs of the system is 12, while the amount of implemented tasks required a total number of 23 DOFs. This is possible since the different tasks are never activated all together but only a subset of them, based on the state of the system. More in detail, the following equality tasks (i.e. tasks characterized by a specific desired value) have been implemented: </p> <p>• Position and orientation trajectory tracking of left arm end-effectors. Such a tasks requires 6 DOFs.</p> <p>• Center of mass, this task is aimed at ensuring that the center of mass of the dual-arm system is, as much as possible, aligned with that of the UAV, in such a way to avoid to destabilize the flight and reduce the power consumption. Such a task requires 1 DOFs.</p> <p>• Field of View of the end effector of the right arm, equipped with a micro camera.</p> <p>As concerns the set-based tasks (i.e. tasks whose desired value is represented by an interval) the following have been implemented:</p> <p>• Joint limits: for each joint, upper and lower limits are set in order to avoid its mechanical limits. Such a task requires 1 DOF for each joint of the arms, thus the total number of required DOFs is 8.</p> <p>• Virtual wall between the two arms: to avoid collisions between the two arms, a virtual wall is implemented in order to delimit their working spaces. Such a task requires 2 DOF.</p> <p>• Virtual wall between the arms and the vehicle: to avoid collisions between the arms and the vehicle, virtual walls are implemented in order to delimit their working spaces. Such a task requires 1 DOF for each arm, thus the total number of required DOFs is 2.</p> <p>• Manipulability, aimed at keeping the manipulators far enough from singular configurations, at which the structure loses mobility. Such a task requires 1 DOF.</p> <p>The equality tasks are always active, while the set-based tasks are active only when the task variable is close to the domain border. The experimental results are reported in the paper:</p> <p>E. Cataldi, F. Real, A. Suarez, P.A. Di Lillo, F. Pierri, G. Antonelli, F. Caccavale, G. Heredia, A. Ollero, Set-based Inverse Kinematics Control of an Anthropomorphic Dual Arm Aerial Manipulator, 2019 IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada.</p> <p>The following data and files are included:</p> <p>• The ROS code: ros_code_set_based.zip, that includes the nodes ros_aeroarms_set_based_seville and lib_multitasks that can be tested in simulation with the simulator V-Rep.</p> <p>• The model of a dual arm aerial manipulator, developed in V-Rep.</p> <p>• The experimental data both in the .mat format for reading with Matlab and in the .bag format for ROS.</p> <p>• The experimental results in ASCII files.</p> <p>[1] S. Moe, G. Antonelli, A. R. Teel, K. Y. Pettersen e J. Schrimpf, «Set-Based Tasks within the Singularity-Robust Multiple Task-Priority Inverse Kinematics Framework: General Formulation, Stability Analysis, and Experimental Results,» Frontiers in Robotics and AI , vol. 3, n. 16, 2016.</p>
Laser wakefield acceleration with active feedback at 5 Hz
<p>The data and code used to produce the plots in Dann <em>et al.</em>, "Laser wakefield acceleration with active feedback at 5 Hz", <em>Phys. Rev. Accel. Beams</em> (2019).</p> <p>This is a sub-set of the data collected during an experiment investigating the use of active feedback in laser wakefield acceleration driven by a 5 Hz, 20 TW laser. The complete data set weighed in at over 800 GB, so this only includes the data used in the above-mentioned publication.</p>
Synchronous Activity-Space Segregation: Simulated Data
<p>Simulations of full census populations of Buffalo and Utica, New York, doing Levy flights along street networks.</p>
Interesting spectra discovered by Active Deep Learning in LAMOST DR2
<p>PDF previews of interesting spectra found by Active Deep Learning in LAMOST DR2 spectra survey. These are referred to in an article Škoda,Podsztavek&Tvrdík "Active deep learning method for the discovery of objects of interest in large spectroscopic surveys" submitted to Astronomy and Astrophysics. Previews show both the whole LAMOST spectrum created from original FITS file and its zoomed part around Halpha line used for machine learning.</p>
Activity-based proteomics reveals nine target proteases for the recombinant protein-stabilizing inhibitor SlCYS8 in Nicotiana benthamiana
<p>Complete dataset (MS Label-free quantification) for the publication 'Activity-based proteomics reveals nine target proteases for the recombinant protein-stabilizing inhibitor <em>Sl</em>CYS8 in <em>Nicotiana benthamiana</em>'</p> <p>DOI: 10.1111/pbi.13092</p> <p> </p>
Online Resources Chapter 3 - Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate
<p>Online Resources to Chapter 3 "Decomposition of standing litter biomass in newly constructed wetlands associated with direct effects of sediment and water characteristics and the composition and activity of the decomposer community using Phragmites australis as a single standard substrate" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2019 in Wetlands 39(1): 113-125. https://doi.org/10.1007/s13157-018-1081-y. </p>
Bats actively use leaves as specular reflectors to detect acoustically camouflaged prey
<p>Measured target strength from 541 positions for 5 different frequency bands. Bat positions in incidence angles of for 33 flight paths.</p>
Dataset - Improved Isolation of Microbiologically Produced (2R,3S)-Isocitric Acid by Adsorption on Activated Carbon and Recovery with Methanol
<p>This dataset in form of an Excel-format contains the data of the journal article “Improved Isolation of Microbiologically Produced Isocitric Acid by Adsorption on Activated Carbon and Recovery with Methanol”. The dataset shows the values of different experiments, calculations carried out for this publication.</p>
Invertebrate activity data from an experiment in Malaysian Borneo, 2014-16 [HMTF]
<b>Description: </b><p>This resource comprises a time series dataset of ant and other invertebrate abundance measured fortnightly at bait monitoring cards on an experimental plot in the Maliau Basin Conservation Area, Malaysian Borneo. The resource includes data regarding the amount of food resource removed from experimental plots when either ants or vertebrates were excluded from the resource. The data were collected to assess the roles that the different groups (ants, invertebrates, vertebrates) play in ecosystem function, and the capacity for functional redundancy within and between these groups. Data were collected between 2014 and 2016 during a project run by the University of Liverpool, which was part of the NERC Human-modified tropical forest (HMTF) Programme.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/54"><b>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Experimental manipulations of biodiversity at SAFE</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NERC grant NE/L000016/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence na)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265748">here</a></p><p><b>Files: </b>This consists of 1 file: Ant_Monitoring.xlsx</p><p><b>Ant_Monitoring.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>Ant monitoring data</b> (described in worksheet Ant_Monitoring_Data)</p><p>Description: ant count taken on bait cards at an experimental plot</p><p>Number of fields: 4</p><p>Number of data rows: 18296</p><p>Fields: </p><ul><li><b>Date</b>: Date cards were collected? (Field type: Date)</li><li><b>Plot</b>: Plot in the Maliau basin (Field type: Location)</li><li><b>Treatment</b>: Refers to ant or termite exclusion, or control (Field type: Categorical)</li><li><b>Score</b>: Number of ants (Field type: Numeric trait)</li></ul></li><li><p><b>Non ant invertebrate data </b> (described in worksheet Non_Ant_Invertebrate_Data)</p><p>Description: non ant count taken on bait monitoring cards</p><p>Number of fields: 12</p><p>Number of data rows: 463</p><p>Fields: </p><ul><li><b>Date</b>: Date cards were collected? (Field type: Date)</li><li><b>Plot</b>: Plot in the Maliau basin (Field type: location)</li><li><b>Treatment</b>: Refers to ant or termite exclusion, or control (Field type: categorical)</li><li><b>Wasp</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Cricket</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Fly</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Springtail</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Beetle</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Cockroach</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Spider</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Harvestman</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric trait)</li><li><b>Sum</b>: The score used to estimate the numbers of inverts observed on the bait cards (Field type: Numeric)</li></ul></li><li><p><b>Percentage of bait removed </b> (described in worksheet Percentage_Bait_Removed_Data)</p><p>Description: percentage of bait removed</p><p>Number of fields: 10</p><p>Number of data rows: 377</p><p>Fields: </p><ul><li><b>Date_put_out</b>: Date the bait was placed (Field type: Date)</li><li><b>Date_collected</b>: Date the bait was collected (Field type: Date)</li><li><b>rep</b>: Replicate (Field type: Replicate)</li><li><b>plot</b>: Plot used in the Maliau basin (Field type: Location)</li><li><b>plot_treat</b>: Plot treatment (Field type: Categorical)</li><li><b>bait</b>: Bait used (Field type: Categorical)</li><li><b>cage_treat</b>: Cage structure (Field type: Categorical)</li><li><b>start_weight</b>: Weight of the bait before being placed in the field (Field type: Numeric)</li><li><b>end_weight</b>: Weight of the bait after being placed in the field (Field type: Numeric)</li><li><b>perc_gone</b>: Percentage of the bait consumed (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2014-12-04 to 2017-03-15</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Arachnida<br> -  -  - Araneae<br> -  -  - Opiliones<br> -  - Entognatha<br> -  -  - Collembola<br> -  - Insecta<br> -  -  - Blattodea<br> -  -  - Coleoptera<br> -  -  - Diptera<br> -  -  - Hymenoptera<br> -  -  - Orthoptera<br></div><p></p>
Changes in Phytochemical Composition and Antioxidant Activity in Nine sh2 Sweet Corn Genotypes during Maturation
<p>Table S1. Mean values ± SD of four biological replicates for the five evaluated traits across nine <em>sh2 </em>sweetcorn hybrids and the five maturity stages. Different lowercase letters as well as different uppercase letters indicate a significant difference at P < 0.05.</p>
Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks
<p>Data related to publication:</p> <p>Heider et al.: Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks.</p> <p>The files include:</p> <p>1) Output conformations of the QM Tautomer & Conformation Predictor of Maestro (Schrödinger, LLC, New York, NY, 2018) of the compounds <strong>1</strong>, <strong>3a</strong>, <strong>3b</strong>, <strong>3c</strong>, <strong>3d</strong>, <strong>3e</strong>, <strong>3f</strong>, <strong>3g</strong>, <strong>3h</strong>, <strong>3j</strong>, <strong>3k</strong>, <strong>3l</strong>, <strong>3m</strong>, <strong>6f</strong> and <strong>6g</strong> [.mae and .sdf files].</p> <p>2) Movies of the MD simulations of compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> (6GN1) and movies (*_2) of <strong>3a</strong>, <strong>3j</strong>, <strong>6b</strong>, <strong>6g</strong> (4PTC) [.mpg files] .</p> <p>3) Raw Desmond trajectory files of the MD simulations of the compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> [out.cms and the full trj files].</p> <p> </p> <p><br> </p>
ScienceDex guides
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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.