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47,155 results for “evaluations”
Evaluation of Phosphoproteomics data-driven signalling network inference
<p><strong>Data</strong></p> <p>Each processed data set and its adjacency matrices representing gold standard networks are stored in one specific folder. The folders have the data set name.</p> <p>The process of obtaining the pairwise results and the evaluation metrics are divided into two main parts:</p> <p><strong>1. Pairwise results</strong></p> <ul> <li>First, all the data set folders have to be placed in one main folder.</li> <li>Then run the script: <em>RunAll_Matrices.R</em></li> <li>This script: <ul> <li>Goes through each data set folder: <ul> <li>reads the data set</li> <li>calculates for each method the resulting matrix</li> <li>Save in the data folder one file with all resulting matrices: file type RData</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>2. Evaluation </strong></p> <ul> <li>After this first step, each data set folder has a file that contains all the resulting adjacency matrices.</li> <li>The second step is to run the script RunAll_Golds.R</li> <li>This script <ul> <li>Goes through each folder: <ul> <li>reads the data set</li> <li>reads the result adjacency matrices from RData files</li> <li>reads the gold networks</li> <li>Calculates all evaluation metrics for each result and each gold network type.</li> </ul> </li> </ul> </li> </ul>
Evaluation of Slowfade Diamond as a Buffer for STORM Microscopy: raw data
<p>raw microscope data associated with the article "Evaluation of Slowfade Diamond as a Buffer for STORM Microscopy"</p> <p>See article for details about the imaging conditions.</p> <p> </p>
Companion data of Detection, Evaluation and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters
<p>This is the companion data repository for the paper entitled <strong>Detection, Evaluation, and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters</strong> by Lucas Leandro Nesi and Lucas Mello Schnorr. The manuscript has been accepted for publication in the <a href="http://wscad.sbc.org.br/current/index.html">WSCAD 2020</a>.</p>
Segmentation performance evaluation results and R scripts for generating figures and tables.
<p>This archive contains the evalutation results of 24 segmentation models and the R scripts to produce figures and tables published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>Please refer to the manuscript for further details.</p>
The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales
<p>This is the companion data for the manuscript titled 'The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales', submitted to the Journal of Advances in Modeling Earth Systems in September 2022.</p> <p>derecho_simulation_result: this folder contains part of the SCREAM RRM outputs I ran on NERSC Cori in 2021-2022 corresponding to the simulation in Table 1 of the manuscript.</p> <p>wrf_simulation_result: this folder contains part of the WRF outputs run by Jianfeng Li from PNNL (jianfeng.li@pnnl.gov) in 2022 corresponding to the simulations in Table 4 of the manuscript.</p> <p>plot_script: this folder includes python scripts to plot figures shown in the manuscript.</p> <p>ASOS_station: this txt file contains the processed ASOS station wind speed used in the manuscript.</p> <p><br> Unfortunately, all model outputs are large (~ 3.8 TB for SCREAM RRM and 3.1 TB for WRF). Therefore, I only provide the variables (i.e., OLR, precipitation, composite radar reflectivity, and 10-m wind speed) used directly to generate figures in this repository. All model outputs are archived on tape at NERSC.</p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu).</p>
Data from: Evaluating species richness using proteomic fingerprinting and DNA-barcoding – a case study on meiobenthic copepods from the Clarion Clipperton Fracture Zone
<p><span>The Clarion Clipperton Fracture Zone (CCZ) is a vast deep-sea region harboring a highly diverse benthic fauna, which will be affected by potential future deep-sea mining of metal-rich polymetallic nodules. Despite the need for conservation plans and monitoring strategies in this context, the majority of taxonomic groups remains scientifically undescribed. However, molecular rapid assessment methods such as DNA-barcoding and Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) provide the potential to accelerate specimen identification and biodiversity assessment significantly in the deep-sea areas. In this study, we successfully applied both methods to investigate the diversity of meiobenthic copepods in the eastern CCZ, including the first application of MALDI-TOF MS for the identification of these deep-sea organisms. Comparing several different species delimitation tools for both datasets, we found that biodiversity values were very similar, with Pielou's Evenness varying between 0.97 and 0.99 in all datasets. Still, direct comparisons of species clusters revealed differences between all techniques and methods, which are likely caused by the high number of rare species being represented by only one specimen, despite our extensive dataset of more than 2000 specimens. Hence, we regard our study as a first approach toward setting up a reference library for mass spectrometry data of the CCZ in combination with DNA-barcodes. We conclude that proteome fingerprinting, as well as the more established DNA-barcoding, can be seen as a valuable tool for rapid biodiversity assessments in the future, even when no reference information is available.</span></p>
DiffLinker Evaluation Files
<p>This dataset contains ground-truth SMILES and SDF files of molecules, fragments and linkers from all test sets (recomputed with OpenBabel) + SMILES of the training linkers – for computing novelty</p>
Evaluation of insecticidal properties of botanicals for sustainable management of sucking pests of horticultural crops
<p>The Bioassay data on the insecticidal properties of botanicals <em>viz</em>., <em>Annona squamosa</em>, <em>Ricinus communis</em> and <em>Sapindus</em> <em>mukorossi</em> seed extracts against sucking pests <em>viz.,</em> aphids (<em>Aphis gossypii, A.craccivora</em>)<em> </em>on okra <em>(Abelmoschus esculentus) </em>and bittergourd (<em>Momordica charantia</em>), thrips (<em>Scirtothrips dorsalis</em>) on chilli (<em>Capsicum frutescens</em>), whiteflies (<em>Bemisia tabaci</em>) on eggplant (<em>Solanum melongena)</em>, mealy bugs (<em>Maconellicoccus hirsutus</em>) on betel vine (<em>Piper betle</em>) and mites (<em>Tetranychus urticae</em>) on egg plant (<em>Solanum melongena) </em>under laboratory conditions.</p>
Data from: Clustering Deviation Index (CDI): A robust and accurate internal measure for evaluating scRNA-seq data clustering
<div> <div> <p>The clustering of cells has been widely used to explore the heterogeneity of cell populations in single-cell RNA-sequencing (scRNA-seq). We proposed a parametric model for monoclonal and polyclonal scRNA-seq data to evaluate clustering results. Based on the parametric model, we proposed a metric (CDI) to quantify the goodness-of-fit of cell clustering to the data. Here we presented CT26.WT and T-CELL as two datasets to examine the performance of our model and metric. CT26.WT contains wild-type CT26 cells from the murine colorectal carcinoma cell line, and cells in CT26.WT are highly homogeneous. T-CELL contains T-cells from tumor tissue of mice three weeks after 4T1 tumor injection. From these datasets and public datasets, we validated our model and benchmarked our metric.</p> </div> </div>
oLIVER progress presentation 2022: Identifying biomarkers for diagnosis and treatment evaluation of NAFLD
<p>This is a recorded talk with head of the oLIVER project - Jørgen Kjems, where he presents the latest project progress.</p> <p>The talk was given at one of ODIN's (the Open Discovery Innovation Network) Knowledge Sharing Events in May 2022.</p>
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation
<p>This repository contains the following 3 datasets for legal document summarization :</p> <p>- IN-Abs : Indian Supreme Court case documents & their `abstractive' summaries, obtained from http://www.liiofindia.org/in/cases/cen/INSC/<br> - IN-Ext : Indian Supreme Court case documents & their `extractive' summaries, written by two law experts (A1, A2).<br> - UK-Abs : United Kingdom (U.K.) Supreme Court case documents & their `abstractive' summaries, obtained from https://www.supremecourt.uk/decided-cases/</p> <p>Please refer to the paper and the README file for more details.</p>
Figure 1 in Biodiversity of mites in mango orchards (Mangifera indica L.) and evaluation of some mineral and essential oils against Cisaberoptus kenyae Keifer (Acari: Eriophyidae) management
Figure 1 The homogenous of some species during 2020–2021 year.
Figure 6 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 6. Motion of colonies toward their relevant imperialist (AtashpazGargari 2009).
Figure 4 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 4. Flowchart of Imperialist Competitive Algorithm (AtashpazGargari 2009).
Figure 7 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 7. Distribution of T. urticae in different stages of sampling.
Data to reproduce the results presented in Lake et al. 2022. Hydrological processes, https://doi.org/10.1002/hyp.14726 ("Using particle size distributions to fingerprint suspended sediment sources – evaluation at laboratory and catchment scales")
<p>This repository contains data on particle size distribution data obtained from the laboratory and field experiments as described in Lake et al., 2022. </p> <p>The data contains the input files as needed for the modelling:</p> <p>- In the excel files the particle size distribution data for the target SS</p> <p>- In the text file the particle size distribution data from the sources.</p> <p> </p> <p>Furthermore, the data contains the resulting output files.</p> <p> </p>
SSiB5/TRIFFID/DayCent-SOM datasets for the paper's in-situ validations and global evaluations
<p>The various data used for the paper "A plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model: Its parameterization, implementation, and evaluation" submitted to Geoscientific Model Development for publication are shared here.</p>
Dataset for "Towards Better Evaluation for Dynamic Link Prediction"
<p>These are the datasets used in <em>Towards Better Evaluation for Dynamic Link Prediction</em></p> <p>For preparing the datasets, we closely follow the baseline methods' data preparation strategy.<br> The original networks are saved as <network>.csv.</p> <p>The networks are formatted as follows:<br> * Each edge is denoted in one line.<br> * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features.<br> * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task.<br> * The first line denotes the network format.<br> * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges.</p> <p>The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline).<br> Ater preprocessing the network edge-list, there are three files that are used by the models:<br> * <ml_network>.csv: this file contains the timestamp edge-list.<br> * <ml_network>.npy: this file contains the edge features in the dense `npy` format that has the features in binary format.<br> * <ml_network_node>.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format.<br> Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods.</p>
Re-evaluating coho salmon (Oncorhynchus kisutch) conservation units in Canada using genomic data
<p><span>Conservation units (CUs) are important tools for supporting the implementation of standardized management practices for exploited species. Following the adoption of the Wild Salmon Policy in Canada, CUs were defined for Pacific salmon based on characteristics related to ecotype, life history, and genetic variation using microsatellite markers as indirect measures of local adaptation. Genomic datasets have the potential to improve the definition of CUs by reducing variance around estimates of population genetic parameters, thereby increasing the power to detect more subtle patterns of population genetic structure and by providing an opportunity to incorporate adaptive information more directly with the identification of variants putatively under selection. We used one of the largest genomic datasets recently published for a non-model species, comprising 5,662 individual Coho salmon (<em>Oncorhynchus kisutch</em>) from 149 sampling locations and a total of 24,542 high-quality SNPs obtained using genotyping-by-sequencing and mapped to the Coho salmon reference genome to 1) evaluate the current delineation of CUs for Coho in Canada and 2) compare patterns of population structure observed using neutral and outlier loci from genotype-environment association analyses to determine whether separate CUs that capture adaptive diversity are needed. Our results reflected CU boundaries on the whole, with the majority of sampling locations managed in the same CU clustering together within genetic groups. However, additional groups not currently represented by CUs were also uncovered. We observed considerable overlap in the genetic clusters identified using neutral or candidate loci, indicating a general congruence in patterns of genetic variation driven by local adaptation and gene flow in this species. Consequently, we suggest that the current CU boundaries for Coho salmon are largely well-suited for meeting the Canadian Wild Salmon Policy's objective of defining biologically distinct groups, but we highlight specific areas where CU boundaries may be refined.</span></p>
Dataset for the paper "Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts"
<p>This dataset was used in the context of the article <em>Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts</em>, at the Computational Humanities Research 2022 conference.</p> <p>Predictions.zip contains the XML ALTO for the HTR and Segmentation prediction of around 10 pages of 1900 manuscripts from the Bibliothèque nationale de France.</p> <p>Varying ground truth contains the original training material for training a classifier for CER classification (see the article).</p> <p>The ManuscriptsIIIF.csv contains metadata about the manuscripts.</p> <p>This work was funded by the DIM MAP under the CREMMALab funding.</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.