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470 results for “Evaluation dataset”

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zenodo52/100

Dataset for "Methodology of Evaluating the Activation Energy of Oxygen Reduction Reaction on Pt-based Electrodes"

High temperature proton-exchange membrane fuel cell (HT-PEMFC) technology is widely studied alternative to current energy conversion technologies based on fossil fuels. Compared to solid oxide fuel cells (SOFCs), HT-PEMFCs allow more flexibility and demand less operation control due to their lower temperature. On the other hand, HT-PEMFCs show an advantage over low-temperature PEMFCs in terms of less demand on the purity of the H2 used, the possibility to recover the generated heat, lower water management requirements, and easy heat management. One of the critical limitations of HT-PEMFC operation is a slow kinetics of the cathodic reduction of O2 (ORR) due to presence of H3PO4 which ensures proton conductivity in the system. Electrochemical dynamic methods such as cyclic voltammetry or linear sweep voltammetry (LSV) can be used to determine the kinetic parameters of ORR. These measurements can provide information on the Tafel slope and exchange current density (jex) of the ORR. However, performing these measurements under conditions relevant for HT-PEMFC operation is challenging due to presence highly concentrated H3PO4 and elevated temperature. First, determination of the kinetic parameters requires correct assessment of equilibrium potential of ORR (EORR). The value of the EORR is generally influenced by the activity (fugacity) of the reactants and products and the temperature, a discussion of the appropriate standard states of the components is also necessary. Second, the relationship between the jex and the reaction rate constant (k°), necessary for calculation of activation energy ( ), must be known. It includes consideration of the likely reaction mechanism. In this paper, the methodology for appropriate determination of  was developed and used for estimation of  of ORR from LSV curves measured on commercially available Pt/C catalyst under HT-PEMFC relevant conditions. In particular, the measurements were carried out using a rotating glassy carbon rod disk electrode (RRE) in purified 98 wt.% H3PO4 (as electrolyte) at temperatures of 120, 140, 160, 180 °C. Though the treatment was developed in context of ORR and HT-PEMFC, the approach is generally applicable to any electrochemical reaction.

opencc-by-4.0Aug 2024View details →

zenodo52/100

Classes of errors in DOI names: evaluation dataset

This dataset contains the results of the evaluation of the methodology presented in the article Identifying and correcting invalid citations due to DOI errors in Crossref data (<a href="https://arxiv.org/abs/2111.11263">https://arxiv.org/abs/2111.11263</a>). The file named 10_random_citations_per_rule.csv contains 193 randomly selected citations from the corrected citations obtained by the process described in the article (<a href="https://doi.org/10.5281/zenodo.4892551">10.5281/zenodo.4892551</a>). They were extracted using the script called evaluation.py, which can be viewed in the GitHub repository open-sci/2020-2021-grasshoppers-code (<a href="https://doi.org/10.5281/zenodo.4723983">10.5281/zenodo.4723983</a>).

opencc-zeroFeb 2022View details →

zenodo52/100

Dataset for Towards improved online dissolution evaluation of Pt-alloy PEMFC electrocatalysts via electrochemical flow cell - ICP-MS setup upgrades

Experimental data comprises raw data from ICP-MS (Inductively coupled plasma mass spectrometry) (i.e. time dependence of signal intensity for Co59 and Pt195) for different cell geometry and operating parameters.   Model data comprise of time- and space-dependent values of Pt ions concentration in the modelling cell and local velocity vectors.

opencc-by-4.0Jan 2024View details →

zenodo52/100

Datasets for evaluating scalable supervised learning for synthesize-on-demand chemical libraries

This repository contains datasets for the manuscript "Evaluating scalable supervised learning for synthesize-on-demand chemical libraries": <ul> <li>ams_all_preds.csv.gz: The AMS dataset predictions when using an RF or baseline model trained on the training dataset. Includes the predicted score and rank from each model for each compound. We started with 8,434,707 AMS compounds and detected that 247,025 were in the LC or MLPCN training data. These were removed from the AMS list, leaving 8,187,682 compounds to score. The compound matching was done on the SMILES that we canonicalized in rdkit.</li> <li>ams_order_results.csv.gz: Information about the 1,024 compounds purchased from the AMS library. Excludes the 4 AMS compounds that were incompletely dissolved. Includes the chemical feature representation, information from the vendor, RF and baseline model predictions, screening results, and clustering results.</li> <li>baseline_weight.npy: The saved Similarity Baseline model, which consists of the active compounds in the training data. This model was used to score the AMS library. See the <a href="https://github.com/gitter-lab/pria-ams-enamine">GitHub repository</a> for code to load the model and make predictions on new compounds.</li> <li>cdd_training_data.tar.gz: The LC1234 and MLPCN PriA-SSB screening data exported from CDD.</li> <li>enamine_costs_clustered_v3_with_nneighbor.csv.gz: Contains 5,620 Enamine compounds that were selected based on the RF prediction score and availability. This file also contains the Taylor-Butina cluster ID when clustering the training compounds, 1,024 tested AMS compounds, and top-ranked Enamine compounds at a 0.4 threshold. The nearest neighbor compounds in the training and AMS sets are also included along with compound information from Enamine, RF model scores, and chemical feature representations.</li> <li>enamine_dose_response_curve_plots.xlsx: Images of the dose response curves from all three runs on the 68 Enamine compounds. If a compound was tested multiple times, multiple curves are shown in the same plot. The compound structure images and SMILES are exported from CDD, not generated with RDKit.</li> <li>enamine_dose_response_curves.tsv: The dose response curve summaries from all three runs on the 68 Enamine compounds. If a compound was tested multiple times, only the highest-quality dose response curve was used.</li> <li>enamine_final_list.csv.gz: The final 100 filtered compounds from <code>enamine_top_10000.csv.gz</code>. Contains compound information from Enamine as well as RF model scores, chemical feature representations, and clustering results.</li> <li>enamine_PriA-SSB_dose_response_data.tar.gz: The dose response screening data from all three runs on the 68 Enamine compounds. The 2021-06-16 run was originally screened on 2020-08-24. 2021-06-16 is the date the compound identities were corrected. This run contains two 1,536 well plates.</li> <li>enamine_top_10000.csv.gz: Top 10,000 predictions from the Enamine REAL dataset using the selected RF model. Contains compound information from Enamine as well as RF model scores, chemical feature representations, and clustering results.</li> <li>master_df.csv.gz: The output of preprocessing the files in <code>cdd_training_data.tar.gz</code>. Contains 441,900 rows.</li> <li>random_forest_classification_139.pkl: The saved RF classification model with hyperparameter ID 139. This model was used to score the AMS and Enamine REAL libraries. See the <a href="https://github.com/gitter-lab/pria-ams-enamine">GitHub repository</a> directory for code to load the model and make predictions on new compounds.</li> <li>train_ams_real_cluster.csv.gz: Contains cluster IDs for Taylor-Butina clustering at a 0.4 threshold applied to the training compounds, 1,024 tested AMS compounds, and top-ranked compounds from Enamine. Includes the chemical features, dataset to which the compound belongs, leader compound for each cluster, and whether the compound is a known hit.</li> <li>training_df_single_fold.csv.gz: This is all ten folds in <code>training_folds.tar.gz</code> merged for convenience. Contains 427,300 compounds.</li> <li>training_df_single_fold_with_ams_clustering.csv.gz: Contains cluster IDs for Taylor-Butina clustering applied to the 427,300 training compounds and the 1,024 tested AMS compounds. Different clustering results are shown at the 0.2, 0.3, and 0.4 thresholds. Includes the leader compound for each cluster. Although the training and AMS compounds were clustered jointly, only the training compounds' clusters are shown. The AMS compounds' clusters are in <code>ams_order_results.csv.gz</code>.</li> <li>training_folds.tar.gz: The LC1234 and MLPCN training data split into ten folds. This dataset with 427,300 compounds was used for cross validation and model selection. This dataset is derived from <code>master_df.csv.gz.</code></li> </ul> If you use these datasets in a publication, please cite: Moayad Alnammi, Shengchao Liu, Spencer S. Ericksen, Gene E. Ananiev, Andrew F. Voter, Song Guo, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. <a href="https://doi.org/10.1021/acs.jcim.3c00912">Evaluating scalable supervised learning for synthesize-on-demand chemical libraries</a>. Journal of Chemical Information and Modeling 2023. See PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1272365">1272365</a>, AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1918986">1918986</a>, and the associated publications for details about the PriA-SSB screening data. The screening datasets were compiled from three separate sources that should all be cited if the training dataset is used in a publication: <ul> <li>Moayad Alnammi, Shengchao Liu, Spencer S. Ericksen, Gene E. Ananiev, Andrew F. Voter, Song Guo, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. <a href="https://doi.org/10.1021/acs.jcim.3c00912">Evaluating scalable supervised learning for synthesize-on-demand chemical libraries</a>. Journal of Chemical Information and Modeling 2023.</li> <li>Shengchao Liu+, Moayad Alnammi+, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. <a href="https://doi.org/10.1021/acs.jcim.8b00363">Practical model selection for prospective virtual screening</a>. Journal of Chemical Information and Modeling 2018.</li> <li>Andrew F. Voter+, Michael P. Killoran+, Gene E. Ananiev, Scott A. Wildman, F. Michael Hoffmann, James L. Keck. <a href="https://doi.org/10.1177/2472555217712001">A high-throughput screening strategy to identify inhibitors of SSB protein–protein interactions in an academic screening facility</a>. SLAS Discovery 2018.</li> </ul> <ul> </ul>

opencc-by-4.0Oct 2021View details →

zenodo48/100

Dataset used for evaluation of GRASP-AOD algorithm

Dataset used for evaluation of GRASP-AOD algorithm 30 AERONET sites where processed using GRASP v1.0.0 and following the methodology described in Torres et. al 2017 and Torres et Fuertes 2020. More sites and data can be found at <a href="http://www.grasp-open.com">www.grasp-open.com</a> . 31 files can be found. 30 Files described the 30 AERONET sites used for GRASP-AOD validation while the file aureole_Granada.csv is used in the section 4.2 for GRASP-AUR test. Description of the columns that can be found in the datafiles: <ul> <li>date</li> <li>FineModeMedianRadius</li> <li>FineModeGeometricStandardDeviation</li> <li>FineModeVolumeConcentration</li> <li>CoarseModeMedianRadius</li> <li>CoarseModeGeometricStandardDeviation</li> <li>CoarseModeVolumeConcentration</li> <li>VolumeConcentration</li> <li>Effective radius</li> <li>abs_error</li> <li>rel_error</li> <li>aod500_fine</li> <li>aod500_coarse</li> <li>aod380_retrieved</li> <li>aod440_retrieved</li> <li>aod500_retrieved</li> <li>aod870_retrieved</li> <li>aod1020_retrieved</li> <li>input380nm</li> <li>input440nm</li> <li>input500nm</li> <li>input870nm</li> <li>input1020nm</li> <li>exist_380nm</li> <li>exist_440nm</li> <li>exist_500nm</li> <li>exist_870nm</li> <li>exist_1020nm</li> <li>number_of_wavelengths</li> <li>min_wavelength</li> <li>max_wavelength</li> <li>climatology_method</li> <li>wavelengths_used</li> </ul> Please follow the data policy of each data source: GRASP-AOD: GRASP-OPEN (<a href="https://www.grasp-open.com/products/">https://www.grasp-open.com/products/</a>) AERONET: <a href="http://www.aeronet.gsfc.nasa.gov/">http://www.aeronet.gsfc.nasa.gov</a> Details can be found in manuscript: Torres B. et Fuertes D., Characterisation of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data, sent to review on 2020

opencc-by-4.0Aug 2020View details →

zenodo48/100

Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset

This dataset contains data used in the research published by MLabs Optronics in the paper: Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018. The dataset is classified into 3 folders: - IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.   - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS". - VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).   Mlabs Optronics PTA Calle Pierre Laffitte, 8 29590 Málaga (Spain) www.mlabsoptronics.com info@mlabsoptronics.com

opencc-by-4.0May 2019View details →

zenodo48/100

Dataset for the fine-tuning of parameters and hyper parameters, and the evaluation of the Scorca agent

# Scorca Data This repository contains data collected during multiple tests of the Scorca agent. This data was used to generate the various plots seen in the ICAART paper titled ***Knowledge Modelling, Strategy Designing, and Agent Engineering for Reconnaissance Blind Chess***, and to forge decisions for the agent, i.e. with which strategy to go and which hyperparameters to use.  It also contains multiple scripts used to generate the plots in the paper. ## Project overview The project is organized into several key directories, each containing specific components of the Scorca agent's testing and data analysis: - /experiments: Contains various experimental data and scripts.   - /entropy_comp: Data and scripts related to entropy computation experiments.   - /piece_states_removal: Information on experiments involving the removal of piece states.   - /sense_comp: Contains sub-directories for adapted entropy, likely senses, and opponent move weight analysis. - /misc_src: Miscellaneous source files and scripts.   - /efficiency: Scripts and data related to the efficiency analysis of the agent.   - /enemy_bot_movement: Data on enemy bot movement patterns and strategies.   - /history: Historical data and analysis scripts.   - /naive_entropy: Scripts for naive entropy calculations. - /sense_comp: Sense computation related files.   - /all_possible_states: Tracking and analysis of all possible board states.   - /likely_senses: Data on the most likely senses used in various game scenarios.   - /opp_move_weight: Analysis of opponent move weight in different contexts. For the agent source code, please refer to the Scorca GitHub repository: https://github.com/Robinbux/Scorca. - Contact Robin in case of any inquiry (rb.stoehr@gmail.com) - ⁠The licence is CC-BY 4.0.

opencc-by-4.0Dec 2023View details →

zenodo48/100

Evaluation datasets and results for the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays"

Event-logs and Business Process Simulation Models used in the experimentation of the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays", where the 'inputs' folder contains all the files used as input, and the 'output' folder the results of the evaluation.   Inputs: event-logs, BPS models, and simulation parameters used as input in the experimentation. <ul> <li>Real-life: real-life event logs, corresponding to two disjoint subsets of traces from an Academic Credentials' process, and the BPIC 2012 and BPIC 2017 event logs (filtered as explained in the paper), and the BPS model (plus simulation parameters) used as input for each dataset in the presented approach.</li> <li>Synthetic: simulated event-logs and corresponding BPS models (plus simulation parameters) for four different processes with 0, 1, 3 and 5 timer events.</li> </ul> Outputs: results of the experimentation. <ul> <li>Real-life: results corresponding to the evaluation with real-life event logs. Each of the folders is composed by the original and the enhanced BPS models, 10 event logs simulated with each of them, two folders with the best iteration of the two hyperparameter optimization processes, and the values for the injected timers in each case. In addition, a CSV file with the EMD metrics (cycle time and absolute hour event distribution) for each dataset is provided.</li> <li>Synthetic: results corresponding to the simulated event-logs. <ul> <li>Before-After: BPS models and discovered timer events for the four synthetic processes, with five timers placed before and after different activity instances.</li> <li>Complete: BPS models and quality measures (precision, recall, and SMAPE of the discovered timers) for the four synthetic processes with zero, one, three, and five timer events.</li> <li>Individual: event logs enhanced with the discovered extraneous delay for each activity instance, for the four synthetic processes with zero, one, three, and five timer events; and SMAPE of the estimations.</li> </ul> </li> </ul>

opencc-by-4.0Jun 2022View details →

zenodo48/100

Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"

<h1>Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"</h1> See http://arxiv.org/abs/2411.04543. This data set contains the .fits files envolved in our work, from <a href="https://irsa.ipac.caltech.edu/data/Planck/" target="_blank" rel="noopener">Planck release</a> and <a href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/" target="_blank" rel="noopener">Irfan et. al., 2019</a>:  In order to use these data files,  please follow: (github readme) <h2>Data from Planck release</h2> <h3>Planck Release 1, 2013</h3> Format: .FITS file Type: map for thermal dust model (optical depth, spectral index, and temperature) Relation to this work: provide parameters of model Planck 2013 HFI_CompMap_ThermalDustModel_2048_R1.20.fits <a title="HFI_CompMap_ThermalDustModel_2048_R1.20.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits</a> Format: .FITS file Type: full-sky maps at 217 GHz with zodiacal light and without zodiacal light Relation to this work: used to filter out regions with strong zodiacal emission HFI_SkyMap_217_2048_R1.10_nominal.fits <a title="HFI_SkyMap_217_2048_R1.10_nominal.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits</a> HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits <a title="HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits</a> <h3> </h3> <h3>Planck Release 2, 2015</h3> Format: .FITS file Type: map of synchrotron emission Relation to this work: used to remove synchrotron emission from full-sky maps COM_CompMap_Synchrotron-commander_0256_R2.00.fits <a title="COM_CompMap_Synchrotron-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits</a> Format: .FITS file Type: map of free-free emission Relation to this work: used to remove free-free emission from full-sky maps COM_CompMap_freefree-commander_0256_R2.00.fits <a title="COM_CompMap_freefree-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits</a> Format: .FITS file Type: map of carbon monoxide Relation to this work: used to remove carbon monoxide emission from full-sky maps COM_CompMap_CO21-commander_2048_R2.00.fits <a title="COM_CompMap_CO21-commander_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits</a> Format: .FITS file Type: map of 94/100 GHz molecular emission lines Relation to this work: used to remove 94/100 GHz emission lines from full-sky maps COM_CompMap_xline-commander_0256_R2.00.fits <a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits</a> Format: .FITS file Type: Galactic plane masks with no apodization Relation to this work: used to mask Galactic plane HFI_Mask_GalPlane-apo0_2048_R2.00.fits <a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_GalPlane-apo0_2048_R2.00.fits</a> Format: .FITS file Type: point source masks Relation to this work: used to mask point sources in full-sky maps and inpaint them  HFI_Mask_PointSrc_2048_R2.00.fits <a title="HFI_Mask_PointSrc_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits</a> Format: .FITS file Type: maps for thermal dust model (optical depth, spectral index, and temperature) Relation to this work: provide parameters of model Planck 2015 (GNILC pipeline, without CIB contamination) COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits <a title="COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits</a> COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits <a title="COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits</a> COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits <a title="COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits</a> Format: .FITS file (table) Type: Planck catalogue of compact sources at 30, 44, 70, 100, 143, 217, 353, 545, and 857 GHz Relation to this work: to mask compact sources COM_PCCS_030_R2.04.fits <a title="COM_PCCS_030_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits</a> COM_PCCS_044_R2.04.fits <a title="COM_PCCS_044_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits</a> COM_PCCS_070_R2.04.fits <a title="COM_PCCS_070_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits</a> COM_PCCS_100-excluded_R2.01.fits <a title="COM_PCCS_100-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits</a> COM_PCCS_100_R2.01.fits <a title="COM_PCCS_100_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits</a> COM_PCCS_143-excluded_R2.01.fits <a title="COM_PCCS_143-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits</a> COM_PCCS_143_R2.01.fits <a title="COM_PCCS_143_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits</a> COM_PCCS_217-excluded_R2.01.fits <a title="COM_PCCS_217-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits</a> COM_PCCS_217_R2.01.fits <a title="COM_PCCS_217_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits</a> COM_PCCS_353-excluded_R2.01.fits <a title="COM_PCCS_353-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits</a> COM_PCCS_353_R2.01.fits <a title="COM_PCCS_353_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits</a> COM_PCCS_545-excluded_R2.01.fits <a title="COM_PCCS_545-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits</a> COM_PCCS_545_R2.01.fits <a title="COM_PCCS_545_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits</a> COM_PCCS_857-excluded_R2.01.fits <a title="COM_PCCS_857-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits</a> COM_PCCS_857_R2.01.fits <a title="COM_PCCS_857_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits</a> <h3> </h3> <h3>Planck Release 3, 2018</h3> Format: .FITS file Type: map of CMB anisotropies (SMICA from Planck 2018) Relation to this work: used to remove CMB anisotropies from full-sky maps COM_CMB_IQU-smica_2048_R3.00_full.fits <a title="COM_CMB_IQU-smica_2048_R3.00_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits</a> Format: .FITS file Type: Planck 2018 full-sky maps at 100, 143, 217, 353, 545, and 857 GHz Relation to this work: used to obtain dust data maps at these bands HFI_SkyMap_100_2048_R3.01_full.fits <a title="HFI_SkyMap_100_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits</a> HFI_SkyMap_143_2048_R3.01_full.fits <a title="HFI_SkyMap_143_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits</a> HFI_SkyMap_217_2048_R3.01_full.fits <a title="HFI_SkyMap_217_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits</a> HFI_SkyMap_353_2048_R3.01_full.fits <a title="HFI_SkyMap_353_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits</a> HFI_SkyMap_545_2048_R3.01_full.fits <a title="HFI_SkyMap_545_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits</a> HFI_SkyMap_857_2048_R3.01_full.fits <a title="HFI_SkyMap_857_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits</a> HFI_RIMO_R3.00.fits <a title="HFI_RIMO_R3.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits</a> <h2> </h2> <h2>Thermal dust model from Melis O. Irfan et al. <a href="https://www.aanda.org/articles/aa/abs/2019/03/aa34394-18/aa34394-18.html" target="_blank" rel="noopener">A&A 623, A21 (2019)</a></h2> Format: .FITS file Type: maps for thermal dust model (optical depth, spectral index, and temperature) Relation to this work: provide parameters of model Melis O. Irfan et al. 2019 beta.fits <a title="beta.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits</a> tau.fits <a title="tau.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits</a> temp.fits <a title="temp.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits</a>

opencc-by-4.0Nov 2024View details →

zenodo48/100

Automated Literature Screening for Systematic Reviews: Dataset for Evaluation Against Human Title and Abstract and Full-Text Screening Decisions

This Zenodo entry contains the supplementary material associated with the manuscript titled Automated Literature Screening for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement of Greater Than 0.99. The paper will be presented at <a href="https://dbis.rwth-aachen.de/LLMs4MI2024/">LLMsMI 2024</a> in November 2024. A script is provided for replicating the executed experiments, along with a comprehensive evaluation file that reports all the experiment results. Provided data files represent an extension to the original datasets as provided by [1]. For associated systematic review manuscripts and eligibility criteria, please refer to [1] as well.  [1] Guo, Eddie; Gupta, Mehul; Deng, Jiawen; Park, Ye-Jean; Paget, Mike; Naugler, Christopher (2023). "Automated Paper Screening for Clinical Reviews Using Large Language Models." Mendeley Data, V1, doi: 10.17632/np79tmhkh5.1. Accessed from: <a href="https://data.mendeley.com/datasets/np79tmhkh5/1" target="_new" rel="noopener">https://data.mendeley.com/datasets/np79tmhkh5/1</a>.

opencc-by-4.0Dec 2023View details →

zenodo48/100

Dataset to manuscript: Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems

Raw data to the manuscript entitled "Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems" by Severin-Luca Bellè, Jean Riotte, Norman Backhaus, Muddu Sekhar, Pascal Jouquet and Samuel Abiven.  Data files include all raw data of farmer interviews (20211209_Biochar_India_rawdata_Bellè_Abiven_farmer_interviews) and all raw data from the soil incubation study (20211209_Biochar_India_rawdata_Bellè_Abiven_soil_incubation).  File ending with var_names is the README file.

opencc-by-4.0Dec 2021View details →

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Dataset for: Evaluating phylogenetic methods for quantifying risks and opportunities presented by forks in open source software (master dissertation).

This is the data for my master dissertation [1]. If you wish to get a copy, download it from Zenodo and open docs/master.pdf. Data acquisition and encoding techniques are described in paragraph 3.1.1 (table 3.1). The data is described in more detail in paragraph 4.1 (table 4.2). * fork1_all.csv: MySQL server / MariaDB server * fork2_all.csv: Linux kernel / Android kernel * fork3_all.csv: Apache OpenOffice / LibreOffice ==Cite== [1] A. Ortiz-Troncoso. Evaluating phylogenetic methods for quantifying risks and opportunities presented by forks in open source software (master dissertation). Zenodo, 2018. doi: http://doi.org/10.5281/zenodo.1158292

opencc-by-4.0Feb 2018View details →

zenodo48/100

Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"

This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.

opencc-by-4.0Sep 2024View details →

zenodo48/100

Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)

Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project. <ul> <li> H2020 EuroSea project: The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626). </li> <li> Task 2.3: Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France). </li> <li> Subtask 2.3.1: Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT. </li> <li> Where? The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase. </li> </ul> Report with the full analysis The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Codes for the analysis The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a> The dataset The dataset includes: 1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid.   2) Simulated configurations (or sampling strategies) in each region (PKL file format). 3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). 4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)). 5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).

opencc-by-4.0Jul 2022View details →

zenodo48/100

An analyst-created, high-resolution seismic arrival time dataset for evaluating machine learning phase detectors

This data set contains phase arrival times and phase labels for one hour of continuous seismic data recorded at the three-component broadband station WY.YNR on 2014 30 March from 13:00:00 to 14:00:00 UTC. This hour of data follows a Mw 4.8 occurring at 12:34 UTC in the Yellowstone region and contains many small events close together in space and time. All 687 picks (404 P and 283 S) were made by a seismic analyst trained at the University of Utah Seismograph Stations. The goal was to pick as many reasonable arrivals as possible for evaluating the performance of machine-learning-based phase detectors on continuous data during high seismicity rates.  Phase picks were made using the Seismic Analysis Code (SAC; Goldstein and Snoke, 2005) and Pyrocko (Heimann et al., 2017). In general, a 1 to 17 Hz bandpass filter was used. The csv file contains the pick id, phase arrival time in UTC and Unix epoch formats, and the phase labels (P or S).

opencc-by-4.0Apr 2023View details →

zenodo48/100

Quality evaluation criteria, best practices, and assessment systems for Institutional Publishing Service Providers (IPSPs): dataset

The dataset contains tabular information on the elements of best practice in scholarly publishing found in a set of documents (high-level recommendations and principles, indexation criteria and specific assessment guidelines used on the national and institutional levels). The set of documents subject to analysis (58 items) were identified by the DIAMAS project team members (bibliographic metadata are provided in IPSP-best-practice-documents.xml and IPSP-best-practice-documents.ris). The dataset was compiled by the DIAMAS project team using an analysis matrix that included the general information about the documents (title, issuing entity, scope and purpose, etc.) and the the seven core components of scholarly publishing identified in the Diamond Open Access Action Plan (2022) and revised by the DIAMAS project team. More information about the data collection methodology can be found in the report D3.1 IPSP Best Practices Quality evaluation criteria, best practices, and assessment systems for Institutional Publishing Service Providers (IPSPs) (<a href="https://doi.org/10.5281/zenodo.7859172">https://doi.org/10.5281/zenodo.7859172</a>), which is based on this dataset.   ****Dataset contents**** IPSPs_best-practices-overview.csv IPSPs_best-practices-overview.ods IPSP-best-practice-documents.xml IPSP-best-practice-documents.ris README.txt   ****Column headers and field types*** Title (original) (text) Title (English) (text) Publication date (date, DD/MM/YY) Last accessed (date, DD/MM/YY) URL (text-web address) Scope (text, controlled) Type of document (text, controlled) Original language (text) Other languages (text) Entity issuing the document (text) Entity responsible for the assessment (text) Scope of the assessment (text, controlled) Scope of assessment: region or country (text) Disciplines’ coverage (text) Periodicity of the assessment (text) Reassessment frequency? If yes: periodicity (text) Benefits linked to the assessment (text) (1) Funding (text) (2) Ownership and governance (text) (3) Open science practices (text) (4) Editorial quality, editorial management and research integrity (text) (5) Technical service efficiency (text) (6) Visibility (including indexation), communication, marketing and impact (text) (7) Diversity, Equity and Inclusion (text)

opencc-by-4.0Apr 2023View details →

zenodo48/100

CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia

General Info This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively.  Contact René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>)  Dataset Contents  <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET). <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>) <pre><code>model_scripts.tar</code></pre> Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset.  <pre><code>glacier_final.tar</code></pre> Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).

opencc-by-4.0Apr 2023View details →

zenodo48/100

Dataset for Evaluation of a novel microfluidic chip-like device for purifying bovine frozen-thawed semen for in vitro fertilization

VetCountTM Harvester (MotilityCount ApS, Copenhagen, Denmark) is a novel sperm purification device. It consists of two chambers separated by a 10 μM microporous membrane. Untreated semen is applied in one chamber, sperm collection medium in the other. Motile sperm cells are selected by actively swimming through the membrane pores into the medium containing chamber. After 30 min incubation, the sperm collection medium can be aspirated and the purified sperm is ready for further use. In a first experiment we assessed sperm quality and recovery of frozen-thawed semen from six different bulls (n = 6) prior to and after purification with the VetCountTM Harvester or BoviPureTM gradient centrifugation, a commercial available standard technique. In a second approach, a competitive fertilization assay was performed. Ten straws per bull were pooled, split in two subsamples, and simultaneously purified either with the VetCountTM Harvester or BoviPureTM gradient centrifugation. Following purification, sperm cells from each treatment group were fluorescently labeled with either MitoTrackerTM Red FM or MitoTrackerTM Green FM. In vitro matured oocytes were inseminated with equal numbers of red and green labeled sperm. Eighteen hours after fertilization, fluorescence microscopy was used to determine the origin of the fertilizing spermatozoon.

opencc-by-4.0Jun 2023View details →

zenodo44/100

Machine Translation Evaluation Dataset for Amharic

Machine Translation Evaluation Dataset for Amharic  The dataset contains source sentences in Amharic and English and their corresponding reference translations that were collected using crowd sourcing. These ground-truth sentences are from across different domains such as news headlines, social media, Wikipedia and everyday conversation. Metadata of files in the dataset amen.tsv   - Domain: news | wiki | twitter | convo   - Source Sentence: Amharic sentence   - Reference Translation: English translation   - Google Translate: output of Google Translate   - Yandex Translate: output of Yandex Translate enam.tsv   - Domain: news | wiki | twitter | convo   - Source Sentence: English sentence   - Reference Translation: Amharic translation   - Google Translate: output of Google Translate   - Yandex Translate: output of Yandex Translate Amharic source and reference translations across domains: News: These are news headlines from Ethiopian news websites. Wikipedia: A random sample of sentences from the Amharic Wikipedia. Twitter: Amharic Twitter posts on consumer products. Conversational: Everyday conversational expressions from Amharic native speakers.   English source and reference translations across domains: News: These are news headlines from <a href="https://en.wikipedia.org/wiki/Portal:Current_events">Wikipedia current events portal</a>. Wikipedia: A random sample of sentences from the English Wikipedia. Twitter: English Twitter posts on global events from Wikipedia current events portal. Conversational: Everyday conversational expressions from English native speakers.   Evaluation of two systems that provide Amharic translation The dataset also contains evaluation of two commercial systems: [Google Translate](https://translate.google.com/) and [Yandex Translate](https://translate.yandex.com/). Both systems provide free APIs that users can sign up and get access keys to. The translations for Amharic to English were generated on 14th February 2020. The translations for English to Amharic were generated on 30th March 2020.

opencc-by-4.0Feb 2020View details →

zenodo44/100

Associated dataset for "Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"

The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at: <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP</a> Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way you should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication: <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_ICASSP_freeze</code></pre> Directory "SNR": <ul> <li>Tools for instrumental evaluation (Section 4)</li> <li>Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations</li> <li>Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations)</li> </ul> Directory "Relative Output Levels": <ul> <li>Tools for preparation of perceptual evaluation (Section 5)</li> <li>Shell script to capture rendered uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4)</li> </ul> Directory "Absolute Output Levels": <ul> <li>Tools for specification of perceptual evaluation (Section 5)</li> <li>Shell script to capture reproduced uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1)</li> </ul> Files in base directory and directory "Study Results": <ul> <li>Tools for perceptual evaluation / user study (Section 5)</li> <li>Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory)</li> <li>Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)</li> </ul>

opencc-by-4.0May 2020View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record