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311 results for “function dataset”
Sensitivity Datasets - Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins
<p><strong>Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins</strong></p> <p>The Sensitivity datasets cover more than 800 proteins and are structured as follows. The sensitivity values are the mean of four DeeProtein replicates.</p> <p>It is uploaded as tar.gz. and contains one directory.</p> <p>File names contain the PDB<sup>1</sup> identifier and the respective chain identifier. </p> <p>The sequences and secondary structure information were downloaded from the RCSB Protein Databank and are available here: <a href="https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz">https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz</a> This URL can be found with some explanation at <a href="http://www.rcsb.org/pdb/static.do?p=download/http/index.html">http://www.rcsb.org/pdb/static.do?p=download/http/index.html</a></p> <p>The secondary structure annotation relies on the DSSP Algorithm by Kabsch and Sander<sup>2</sup>.</p> <p> </p> <p><strong>The files are tab-separated and contain the following columns:</strong></p> <ul> <li><strong>Pos</strong> Position in the sequence, starting from zero</li> <li><strong>AA</strong> Amino acid in that position</li> <li><strong>sec</strong> Secondary structure as annotated in the RCSB Protein Databank</li> <li><strong>dis</strong> if a region has not been experimentally observed (sometimes explains mismatches with crystal structures)</li> <li><strong>GO:_______</strong> Sensitivity for the GO term</li> </ul> <p><strong>References</strong></p> <ol> <li>The Protein Data Bank H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne (2000) Nucleic Acids Research, 28: 235-242. doi:10.1093/nar/28.1.235</li> <li>Kabsch, W. & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637, doi:10.1002/bip.360221211 (1983).</li> </ol>
SFS-A68-16: A dataset for the segmentation of space functions in apartment buildings
<p> </p> <p>SFS-A68-16: "A dataset for the segmentation of space functions in apartment buildings"</p> <p>Authors: "Amir Ziaee, Georg Suter, Mihael Barada, Laura Keiblinger"</p> <p>Copyright: "Design Computing Group TU Wien, 2023"</p> <p>Credits: "Design Computing Group TU Wien"</p> <p>License: "GNU GENERAL PUBLIC LICENSE Version 3"</p> <p>Version: "1.0.3"</p> <p>Maintainer: "Amir Ziaee"</p> <p>Email: "amir.ziaee@tuwien.ac.at"</p> <p>Url: <a href="https://github.com/A2Amir/SFS-A68-16">https://github.com/A2Amir/SFS-A68-16</a></p> <p>Description: "We present the <strong>SFS-A68-16</strong> dataset for space function segmentation in apartment buildings. The dataset consists of 16 multi-viewpoint space layout input and corresponding ground truth images for 68 floor plans of apartment buildings designed or built between 1952 and 2019. It addresses a limitation of the SFS-A68 dataset (version 1.0.1) we created in our previous work, which consists of single-viewpoint projection images only. Each pixel in a ground truth image of the SFS-A68-16 dataset is assigned to a space function class. Space function classes in apartment buildings that are classified by a space function segmentation network are shown below under ground truth classes. We have identified 22 space function classes for the apartment buildings in our dataset. Each element in an input image of the SFS-A68-16 dataset is colored according to a unique class color (below, under input classes). Space elements, such as doors and furnishing elements, are contextual features in input images that may help determine the function of a space. To measure whether excluding space elements in input images affects the accuracy of a space function segmentation network, we create a new dataset, <strong>SFS-A68-16-SEE</strong>, where space elements are excluded in input images of the SFS-A68-16-SEE dataset. The defined class hierarchy of the dataset with the unique RGB color code of each class can be seen below."</p> <p> </p> <p><strong>Input classes</strong></p> <p>[Root]</p> <p>├──[Space]</p> <p>│ ├── (102, 102, 122)[InternalSpace]</p> <p>│ └── (161, 162, 155)[ExternalSpace]</p> <p>└──[SpaceElement]</p> <p> ├── [SpaceContainedElement]</p> <p> │ ├── [CirculationElement]</p> <p> │ │ ├── (230, 184, 175)[FlightOfStairs]</p> <p> │ │ └── (107, 74, 101)[Landing]</p> <p> │ ├── [FurnishingElement]</p> <p> │ │ ├── (0, 191, 255)[KitchenElement]</p> <p> │ │ └── (70, 130, 180)[SanitaryElement]</p> <p> │ └── [EquipmentElement]</p> <p> │ └── [HomeAppliance]</p> <p> │ └── (159, 140, 81)[TextileCareAppliance]</p> <p> └── [SpaceEnclosingElement]</p> <p> ├── (109, 189, 110)[Opening]</p> <p> ├── (0, 250, 154)[Partition]</p> <p> ├── (255, 215, 0)[Window]</p> <p> └── [Door]</p> <p> ├── (200, 255, 0)[InternalDoor]</p> <p> ├── (72, 112, 39)[UnitDoor]</p> <p> ├── (187, 244, 154)[ElevatorDoor]</p> <p> ├── (47, 79, 79)[BalconyDoor]</p> <p> └── (195, 210, 192)[SideEntrance]</p> <p> </p> <p><strong>Ground truth classes</strong></p> <p>[Space]</p> <p>├── [ResidentialSpace]</p> <p>│ ├── [CommunalSpace]</p> <p>│ │ ├── (255, 218, 185)[DiningRoom]</p> <p>│ │ ├── (166, 206, 227)[FamilyRoom]</p> <p>│ │ └── (255, 0, 0)[LivingRoom]</p> <p>│ └── [PrivateSpace]</p> <p>│ ├── (0, 255, 0)[Bedroom]</p> <p>│ │ ├── (0, 128, 128)[MasterBedroom]</p> <p>│ │ └── (0, 128, 255)[BoxRoom]</p> <p>│ └── (160, 82, 45)[HomeOffice]</p> <p>├── [ServiceSpace]</p> <p>│ ├── (255, 192, 203)[Shaft]</p> <p>│ ├── (245, 245, 220)[StorageRoom]</p> <p>│ │ └── (0, 206, 209)[WalkInCloset]</p> <p>│ └── [SanitarySpace]</p> <p>│ ├── (128, 0, 0)[Bathroom]</p> <p>│ ├── (75, 0, 130)[Toilet]</p> <p>│ ├── (255, 255, 0)[Kitchen]</p> <p>│ └── (0, 128, 0)[LaundryRoom]</p> <p>├── [CirculationSpace]</p> <p>│ ├── [VerticalCirculationSpace]</p> <p>│ │ ├── (0, 0, 128)[Elevator]</p> <p>│ │ └── (0, 0, 255)[Stairway]</p> <p>│ └── [HorizontalCirculationSpace]</p> <p>│ ├── (255, 0, 255)[Entrance]</p> <p>│ └── (255, 100, 0)[Hallway]</p> <p>│ ├── (255, 165, 0)[MainHallway]</p> <p>│ └── (0, 255, 255)[InternalHallway]</p> <p>└── [ExternalSpace]</p> <p> ├── (128, 128, 0)[AccessBalcony]</p> <p> └── (225, 138, 96)[Loggia]</p>
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
DATASET: Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale
<p>This upload contains data for the manuscript "<strong>Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale</strong>." It contains kinetics data, UV-vis data, surface calculations, and activity assays for the systems described in the manuscript.</p>
Dataset for "Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia"
<p><strong>Article: Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia</strong></p> <p><em>Cancer Discovery</em>, <strong>DOI:</strong> 10.1158/2159-8290.CD-21-0410</p> <p> </p> <p>Data Types:</p> <p>1. Clinical summary</p> <p>2. Drug response data</p> <p>3. Exome-sequencing data</p> <p>4. RNA-sequencing data</p> <p> </p> <p><strong>Updates:</strong></p> <p>- <strong>FILE</strong>: File_3.2. <strong>DATE</strong>: 28.11.2022.</p> <p> </p> <p><strong>1. Clinical summary</strong></p> <p><strong>File_0: </strong>Common sample annotation including patient and sample IDs, stage of the disease, tissue type and availability of different data types.</p> <p><strong>File_1.1: </strong>Clinical data for 186 AML patients including clinical diagnosis, disease classification, gender, age at diagnosis, treatments, cytogenetic and molecular details. The description of the variables/column titles is given below the clinical data.</p> <p><strong>File_1.2</strong>: Description of the clinical variables in File_1.1.</p> <p> </p> <p><strong>2. Drug response data for 164 AML patient samples and 17 healthy samples</strong></p> <p><strong>File_2: </strong>Drug library details for 515 chemical compounds. The compound collection includes drugs names, drug class defined by molecular targets or mode of action, concentration range used for drug testing, supplier information, solvent information and vendor information.</p> <p><strong>File_3.1.: </strong>Drug response data including selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The DSS is modified area under the curve values and are calculated as shown in Yadav et al publication (1). The selective drug sensitivity scores (sDSS) is healthy control normalized DSS that gives estimated cancer-selective drug responses. The higher the sDSS values indicate drug sensitivities and negative sDSS values represent drug resistance.</p> <p><strong>File_3.2.: </strong>Drug response data including drug sensitivity scores (DSS) and selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The data is identical to the Supplementary Table 7 in the manuscript.</p> <p><em>Note: We recommend using selective DSS values instead of raw values (% inhibition, IC50, DSS). </em></p> <p><em>Note: If the value is missing, </em><em>the drug was not tested for </em><em>that</em><em> given sample</em><em>.</em></p> <p><strong>File_4: </strong>Drug sensitivity and resistance testing (DSRT) assay details for 181 samples (164 AML patient samples and 17 healthy control samples). The information includes medium (MCM or CM) used for the drug testing, % cell viability after 72 h without drug testing and blast cell percentage of each sample.</p> <p><em>Note: Column E is </em><em>the ratio of luminescence values at 72 h and 0 h. The fold change in the cell viability without drug treatment was calculated as % cell viability. That is why the value could be more than 100% e.g. 70% cell viability meaning that 30% cells died during 72 h and 300% cell viability meaning that cells grew 3 times in 72 h incubation period.</em></p> <p> </p> <p><strong>3. Exome-sequencing data for 225 AML patient samples</strong></p> <p><em>Note: The number of samples in the manuscript is 226. The correct number used in the analyses is 225.</em></p> <p>Mutation data. The cancer specific gene list was prepared by combining AML related genes from TCGA(2) (n=23), InToGen(3) (n=32), Papaemmanuil et al.(4) (n=111) and Census database(5) (n=616). Out of these genes, we found 340 genes as mutated across 225 AML patient samples. The mutation was called with P-values less than 0.05.</p> <p><strong>File_5: </strong>VAF (variant allele frequency) of 340 cancer-specific genes across 225 AML patient samples. The VAF was calculated using paired skin samples as a control from the same AML patient.</p> <p><strong>File_6:</strong> Binary data for 57 cancer specific genes frequently mutated (a given mutation detected in 5 or more samples) across 225 AML patient samples.</p> <p> </p> <p><strong>4. RNA-sequencing data for 163 AML patient samples and 4 healthy</strong></p> <p>CPM (count per million) data: The CPM values are batch corrected values used for direct comparison of gene expression.</p> <p><strong>File_7:</strong> Log2CPM values for 18,202 protein coding genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p><strong>File_8: </strong>Raw read count data RNA-seq library information for all 60,619 genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples). The raw read count data was used to calculate differential gene expression.</p> <p><strong>File_9: </strong>RNA-seq library information including RNA extraction method and sequencing library preparation information for 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p> </p> <p><strong>References</strong></p> <p>1. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R<em>, et al.</em> Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports <strong>2014</strong>;4:5193.</p> <p>2. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A<em>, et al.</em> Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med <strong>2013</strong>;368(22):2059-74.</p> <p>3. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A<em>, et al.</em> IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods <strong>2013</strong>;10(11):1081-2.</p> <p>4. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND<em>, et al.</em> Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine <strong>2016</strong>;374(23):2209-21.</p> <p>5. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N<em>, et al.</em> COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research <strong>2019</strong>;47(D1):D941-D7.</p> <p> </p>
5D-NP-FABTECH_SALV - Open Dataset for "3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction"
<p>This is the open dataset for the paper: "Omar Tricinci*, Francesca Pignatelli, Virgilio Mattoli*, 3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction, On line (2023) [DOI: 10.1002/adfm.202206946] "</p> <p>This include the Supplementary Information file ("SI-PaperSalvinia3_PostRevOKV2.pdf.pdf"), all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata</p>
DATASET: Near-Infrared Photothermal Ablation of Biofilms using Protein-Functionalized Gold Nanospheres with a Tunable Temperature Response
<p>This dataset contains the DLS, TEM, temperature data, and other experimental data to accompany the manuscript.</p>
Dataset and Experiment Scripts for "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance"
<p>This repository contains the Experiment Results and Collection scripts for our ESEC/FSE 2023 submission, "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance"</p> <p>Our runtime experiment data is located in the <em>Experiment Results</em> directory. This directory contains two subdirectories, <em>All Experiment Results</em> and <em>Counterintuitive Results Only</em>. In <em>All Experiment Results</em>, we present the runtime results from our Experiments 1-5 and the Libsodium.js case study as CSV files. For the Chromium and Firefox results, each CSV file list the sample names and multiple columns for each of the four optimization levels, O0-O3. Under each optimization level, we list the runtime (in milliseconds) with inlining enabled (from the Baseline experiment), the runtime with inlining enabled (from one of Experiment #1-5), and percent change in runtime after disabling inlining.</p> <p>The <em>Counterintuitive Results Only</em> directory contains CSV files presenting only the samples from each Experiment #1-5 that meet our threshold of at least a 5% decrease in runtime after disabling inlining.</p> <p>The Excel file, <em>Wasm Function Inlining Experiment Data.xlsx</em>, contains all of these results in a single workbook, as well as formatting applied to highlight the counterintuitive runtime values presented in our paper.</p> <p>The raw data collected from our experiments, including the generated WebAssembly, HTML, and JS files to run the samples, is found under the <em>RawCollectedData</em> directory. This folder contains a zipped file that, when extracted, contains subdirectories for each sample's collected data.</p> <p>The <em>CollectionScripts</em> directory contains the scripts necessary to run our experiments. The <em>PatchFiles</em> directory contains the files from the Binaryen and LLVM infrastructures with the changes that we introduced to enable and disable select optimization passes through environment variables. The <em>Scripts</em> directory contains the scripts we used to run our experiments. The main file within this directory that serves as the entry point is <em>opt_level_inlining.py</em>. This file uses the other scripts to run Experiments 1-5 in our study. The script <em>libsodiumjs_script.py</em> is used to run our Libsodium.js case study.</p> <p>The scripts are written in Python and Node.js, and the dependencies to run the scripts are Node.js, Python, and MySQL. To download the necessary dependencies for the scripts, run the command `pip install -r requirements.txt` in <em>Scripts </em>folder<em>, and run `npm install` in the Scripts/performance-measurement-tool</em> folder. Then, import the included MySQL schemas under the<em> Scripts </em>folder into a MySQL database. Update the MySQL connection details in the <em>Scripts\__db_utils.py</em> and <em>Scripts\performance-measurement-tool\src\MySQLConnector.ts</em> files, and then run `npm run build` in the <em>Scripts\performance-measurement-tool</em> folder.</p>
FIND: A Function Interpretation Dataset and Benchmark for Evaluating Interpretability Methods
<p><strong>FIND</strong> is an interactive dataset for evaluating AI interpretability methods on black box functions. </p> <p>This dataset contains all function files for the <strong>FIND</strong> benchmark and JSON files with associated metadata. The utilities provided in the associated <strong>FIND</strong> <a href="https://github.com/multimodal-interpretability/FIND">GitHub Repository</a> support running and evaluating interpretation of the functions with user-defined interpreters.</p>
Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques - dataset
<p>Dataset from Tuyishime M, Spreng RL, et al. Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques. Frontiers in Immunology 2023 doi: 10.3389/fimmu.2023.1260377</p>
Geo-referenced crop-nutrient response function dataset for Tropical Africa
The profit potential for a given investment in fertilizer use can be estimated using representative crop nutrient response functions. Where response data is scarce, determination of representative response functions can be strengthened by using results from homologous crop growing conditions. Maize (Zea mays L.) nutrient response functions were selected from the Optimization of Fertilizer Recommendations in Africa (OFRA) database of 5500 georeferenced response functions determined from field research conducted in Sub-Saharan Africa. Three methods for defining inference domains for selection of response functions were compared. Use of the OFRA Inference Tool (OFRA-IT; http://agronomy.unl.edu/OFRA) resulted in greater specificity of maize N, P, and K response functions with higher R2 values indicating superiority compared with using the Harvest Choice Agroecological Zones (HC-AEZ) and the recommendation domains of the Global Yield Gap Atlas project (GYGA-RD). The OFRA-IT queries three soil properties in addition to climate-related properties while the latter two options use climate properties only. The OFRA-IT was generally insensitive to changes in criteria ranges of 20–25% used in queries suggesting value in using wider criteria ranges compared with the default for information scarce crop nutrient response functions.
Dataset of "Synaptic function in two-contact devices for neuromorphic circuit applications"
<p><span>This dataset supports the article "Synaptic function in two-contact devices for neuromorphic circuit applications" </span></p> <p> </p> <p><span>Raw data for the article "Synaptic function in two-contact devices for neuromorphic circuit applications". For further details see the Readme.txt file.</span></p>
Dataset for "Computational prediction of structure, function and interaction of Myzus persicae (green peach aphid) salivary effector proteins "
Open the record for dataset details and reuse information.
Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset
<p>There are four datasets:</p> <p>1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.</p> <p>2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.</p> <p>3. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.</p> <p>The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.</p>
Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids - Datasets
<p>This repository contains data from work "Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids"<br><br></p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/BFA.fasta/content" target="_blank" rel="noopener noreferrer">BFA.fasta</a> - fasta file containing sequences of bacterial functional amyloids used as a query for identification of novel bacterial functional amyloids in UHGP dataset</p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/UHGPAmyloids.fasta/content" target="_blank" rel="noopener noreferrer">UHGPAmyloids.fasta </a>- fasta file containing sequences of amyloids identified in UHGP dataset</p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/UHGPAmyloids.csv/content" target="_blank" rel="noopener noreferrer">UHGPAmyloids.csv</a> - csv file containing information about amyloids identified in UHGP</p> <p>Columns:<br>query_id - Uniprot id of the protein from BFA<br>query_gene_name - gene name of the protein from BFA<br>target_id - UHGP id of the found homolog<br>ProbabilityAMYPred-FRL - score obtained for the target_id sequence according to AMYPred-FRL<br>Archcandy - Prediction of beta arch motif for identified amyloid<br>Genome - UHGP genome id of the target_id<br>Localization - predicted subcellular localization with BUSCA for target_id sequence<br>Lineage - full taxonomy of the target_id sequence (which bacteria produced this specific sequence) </p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/PPIPositivePredictionsBetween_UHGPAmyloids_And_HPAIntestine_filtered.csv/content" target="_blank" rel="noopener noreferrer">PPIPositivePredictionsBetween_UHGPAmyloids_And_HPAIntestine_filtered.csv</a> - csv file contining inforamtions about predicted protein-protein interactions between UHGPAmyloids and human proteins expressed in guts</p> <p>Columns:<br>UHGPAmyloids_id - UHGPAmyloids id (same as target_id in UHGPAmyloids.csv)<br>hp_uniprot_name - Uniprot name of a human protein<br>negative and score - scores returned by ProteinPrompt softwawre for prediction of PPI<br>hp_uniprot_id - Uniprot id of a human protein<br>BFA_sp_uniprot_id - Uniprot id of a BFA source protein<br>BFA_sp_uniprot_name - gene name of a BFA source protein<br>UHGPAmyloids_localization - predicted subcellular localization with BUSCA for UHGPAmyloids_id sequence<br>UHGPAmyloids_lineage - full taxonomy of the UHGPAmyloids_id sequence (which bacteria produced this specific sequence) </p>
Dataset for the publication entitled: "Assessment of lithium ion battery ageing by combined impedance spectroscopy, functional microscopy and finite element modelling""
<p>Related to the publication: <a href="https://doi.org/10.1016/j.jpowsour.2021.230459">https://doi.org/10.1016/j.jpowsour.2021.230459</a></p> <p>Datasets for the following Figures:</p> <p>Figure 2.</p> <p>Figure 3.</p> <p>Figure 5.</p> <p>Figure 7.</p>
CX-MS Datasets for "Comprehensive Structure and Functional Adaptations of the Yeast Nuclear Pore Complex"
<p>This repository contains chemical cross-linking mass spectrometry data of affinity-purified Yeast nuclear pore complexes.</p> <p>Data Files Description:</p> <p>NPC_XL_Identification_Inter_Crosslinked.csv: Inter-protein cross-links identified by pLink 2.</p> <p>NPC_XL_spectra.mgf: MS2 spectra data for the identified cross-links.</p> <p>NPC_XL_proteins.fasta : Protein sequences used for search.</p> <p>Sample Processing:</p> <p>NPCs were immuno-purified from Mlp1 tagged S. cerevisiae strains (Kim et al., 2018). After native elution, 1.0 mM disuccinimidyl suberate (DSS) was added and the sample was incubated at 25ºC for 40 min with shaking (1,200 rpm). The reaction was quenched by adding a final concentration of 50 mM freshly prepared ammonium bicarbonate and incubating for 20 min with shaking (1,200 rpm) at 25ºC. The sample (50 µg) was then concentrated and denatured at 98ºC for 5 min in a solubilization buffer (10% solution of 1-dodecyl-3-methylimidazolium chloride (C12-mim-Cl) in 50 mM ammonium bicarbonate, pH 8.0, 100 mM DTT). After denaturation, the sample was centrifuged at 21,130 g for 10 min and the supernatant was transferred to a 100 kDa MWCO ultrafiltration unit (MRCF0R100, Microcon). The sample was quickly spun at 1,000 g for 2 min and washed twice with 50 mM ammonium bicarbonate. After alkylation (50 mM iodoacetamide), the cross-linked NPC in-filter was digested by trypsin and lysC O/N at 37ºC. After proteolysis, the sample was recovered by centrifugation and peptides were fractionated into 10-12 fractions by using a stage tip self-packed with basic C18 resins (Dr. Masch GmbH). Fractionated samples were pooled prior to LC/MS analysis.</p> <p>Desalted cross-link peptides were dissolved in the sample loading buffer (5% Methanol, 0.2% FA), separated with an automated nanoLC device (nLC1200, Thermo Fisher), and analyzed by an Orbitrap Q Exactive HFX (Pharma mode) mass spectrometer (Thermo Fisher) as previously described (Xiang et al., 2020; Xiang et al., 2021). Briefly, peptides were loaded onto an analytical column (C18, 1.6 μm particle size, 100 Å pore size, 75 μm × 25 cm; IonOpticks) and eluted using a 120-min liquid chromatography gradient. The flow rate was approximately 300 nl/min. The spray voltage was 1.7 kV. The QE HF-X instrument was operated in the data-dependent mode, where the top 10 most abundant ions (mass range 380 – 2,000, charge state 4 - 8) were fragmented by high-energy collisional dissociation (HCD). The target resolution was 120,000 for MS and 15,000 for tandem MS (MS/MS) analyses. The quadrupole isolation window was 1.8 Th; the maximum injection time for MS/MS was set at 200 ms.</p> <p>Data Processing:</p> <p>The raw data were searched with pLink2 (Chen et al., 2019b). An initial MS1 search window of 5 Da was allowed to cover all isotopic peaks of the cross-linked peptides. The data were automatically filtered using a mass accuracy of MS1 ≤ 10 ppm (parts per million) and MS2 ≤ 20 ppm of the theoretical monoisotopic (A0) and other isotopic masses (A+1, A+2, A+3, and A+4) as specified in the software. Other search parameters included cysteine carbamidomethyl as a fixed modification and methionine oxidation as a variable modification. A maximum of two trypsin missed-cleavage sites was allowed. The initial search results were obtained using a default 5% false discovery rate (FDR) expected by the target-decoy search strategy. Spectra were manually verified to improve data quality (Kim et al., 2018; Shi et al., 2014). Cross-linking data were analyzed and plotted with CX-Circos (http://cx-circos.net).</p>
Dataset of Functionally Equivalent Java Methods
<p>This is a dataset of functionally equivalent Java methods.</p> <p>This dataset is published as a supplemental data as the following submission.</p> <p> </p> <p>Yoshiki Higo, Shinsuke Matsumoto, Shinji Kusumoto, and Kazuya Yasuda, "Constructing Dataset of Functionally Equivalent Java Methods Using Automated Test Generation Techniques", submitted to MSR 2022.</p> <p> </p> <p>This dataset includes 276 groups of functionally equivalent Java methods, which have been manually verified by the authors.</p> <p>The 276 groups include 728 Java methods in total.</p>
Dataset for "Thresholds in road network functioning on US Atlantic and Gulf barrier islands"
<p>This dataset accompanies the paper "Thresholds in road network functioning on US Atlantic and Gulf barrier islands" (<a href="https://doi.org/10.31223/X55D1G">https://doi.org/10.31223/X55D1G</a>) and is intended to be used to reproduce the analysis. In this dataset you can find the graphml files generated for each island (103 islands with drivable roads). Each intersection of the island road network is a node, and has an associated elevation and extreme water level value. Also included are the statistics for each network, and a single table with the analysis results for each of the networks with >100 nodes.</p>
TocoDecoy: a new approach to design unbiased datasets for training and benchmarking machine-learning scoring functions
<p>This dataset file contains TocoDecoy datasets generated based on the targets and active ligands of LIT-PCBA.</p> <p>1_property_filtered.zip :</p> <ul> <li>TD set: the ligand file name, 2D T-sne vectors, Smiles, molecular weight (MW), Wildman-Crippen partition coefficient (log P), number of rotatable bonds (RB), number of hydrogen-bond acceptors (HBA), number of hydrogen-bond donors (HBD), number of halogens (HAL), topology similarities of decoys to the seed active ligands, active label (active or inactive) and training set label (whether belongs to training set or test set) <strong>OF active ligands and their topologically dissimilar decoys</strong></li> <li>CD set: the decoy conformations with low docking scores generated by docking active ligands into protein pockets using Glide, Schrödinger.</li> </ul> <p> </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.