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Conf-het Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
Zenodo
Jeon, Minkyu; Raghu, Rishwanth; Astore, Miro; Woollard, Geoffrey; Feathers, Ryan; Kaz, Alkin; Hanson, Sonya; Cossio, Pilar; Zhong, Ellen — 2024-07-02 Synthetic cryo-EM datasets with simulated conformational heterogeneity and their ground truth atomic models, density maps, poses, labels, mask, and consensus volume: IgG-1D: 100k particle images (128x128, 6A/pix) of IgG with conformations uniformly sampled from a simple one-dimensional continuous circular motion IgG-1D-noisier: The IgG-1D dataset with noise increased from SNR 0.01 to 0.005 IgG-1D-noisiest: The IgG-1D dataset with noise increased from SNR 0.01 to 0.001 IgG-RL: 100k particle images (128x128, 6A/pix) of IgG with conformations of its flexible linker generated by sampling backbone dihedral angles according to the Ramachandran distributions of disordered peptides https://creativecommons.org/licenses/by/4.0/legalcode
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Q175 Behaviour - Rotarod Videos
Zenodo
Koch, Ellen; Cheng, Judy; Ramandi, Daniel; Sepers, Marja; Hsu, Alex; Fong, Tony; Murphy, Timothy; Yttri, Eric; Raymond, Lynn — 2024-05-13 Raw rotarod videos (compressed) https://creativecommons.org/licenses/by/4.0/legalcode
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Q175 Behaviour - T-Maze Data
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Koch, Ellen; Cheng, Judy; Ramandi, Daniel; Sepers, Marja; Hsu, Alex; Fong, Tony; Murphy, Timothy; Yttri, Eric; Raymond, Lynn — 2024-05-13 Raw water T-maze videos (compressed) https://creativecommons.org/licenses/by/4.0/legalcode
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Q175 Behaviour - Open Field Videos
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Koch, Ellen; Cheng, Judy; Ramandi, Daniel; Sepers, Marja; Hsu, Alex; Fong, Tony; Murphy, Timothy; Yttri, Eric; Raymond, Lynn — 2024-05-13 Raw open field videos (compressed) https://creativecommons.org/licenses/by/4.0/legalcode
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Database for the manuscript "Songbirds adjust oxygen carrying capacity through modulation of the number rather than the size of erythrocytes"
Zenodo
Kovalov, Viktor; Niedojadło, Jowita; Bury, Agata; Bryla, Amadeusz; Dzialo, Maciej; DeMoranville, Kristen J.; Carbeck, Katherine M.; Pierce, Barbara J.; Trost, Lisa; McWilliams, Scott R.; Rutkowska, Joanna; Cichoń, Mariusz; Bauchinger, Ulf; Sadowska, Edyta T. — 2024-04-05 This database is used for the manuscript "Songbirds adjust oxygen carrying capacity through modulation of the number rather than the size of erythrocytes". The database includes hematological parameters of 697 individuals from three species. One sheet describes all the columns in the database. For more information on the methodology, please see the Main Text or the Supplementary Materials. The database has restricted access (upon request) for the entire duration of the revision process. If a paper is accepted, the database will become public. https://creativecommons.org/licenses/by/4.0/legalcode
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Griffith Creek stream temperature and stream discharge
Zenodo
Moore, R.D. — 2023-11-04 This repository contains data sets and scripts used in the analysis for the following article: Moore RD, Guenther SM, Gomi T, Leach JA. Headwater stream temperature response to forest harvesting: Do lower flows cause greater warming? Hydrological Processes, doi 10.10002/hyp.15025   The research was supported by funds from the Natural Sciences and Engineering Research Council of Canada and Forest Renewal British Columbia.   File descriptions griff_mike_tmmm.csv Time series of daily minimum, mean and maximum stream temperatures for Mike Creek (control stream) and Griffith Creek (treatment stream). There are three monitoring sites on Griffith Creek: just above a weir (0 m), and 100 m and 200 m upstream of the weir. The variable names include the stream name (Mike or Griff), the summary value (Min, Mean or Max) and, for Griffith Creek, the location (0, 100, 200). q_dly_rdm.csv Time series of daily mean streamflow at a weir in L/s, as computed from stage and a rating curve. See Moore et al. (2023) for details of the rating curve derivation. mkrf_open_met_dly.csv Time series of daily mean solar radiation (W/m^2), mean daily and maximum air temperature (degrees C), and total rainfall (mm) measured at an open site southeast of Griffith Creek. 01_paired-catchment-analysis_tmax_20230929.r 02_figures_tmax_20230929.r 03_test_ushape_peakfinder_20230927.r Scripts used to conduct the analyses and generate figures and tabular output. The leading number indicates the order in which the scripts should be run. https://creativecommons.org/licenses/by/4.0/legalcode
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Inclusive Green Growth Dataset for African Countries
Zenodo
Ofori, Isaac K.; Gbolonyo, Emmanuel Y.; Ojong, Nathanael — 2024-06-24 Tracking the progress of countries in inclusive green growth (IGG) is crucial for shaping effective sustainable development policies. However, comprehensive IGG data is often inaccessible. Accordingly, rigorous empirical contributions in this direction in the context of Africa remain sparse. To address this, we computed IGG scores for 22 African countries from 2000-2020. Our data reveal that only nine of these countries are achieving green and inclusive growth. This dataset equips researchers and institutions to assess IGG progress and identify pathways that African governments can leverage to promote sustainable development. https://creativecommons.org/licenses/by/4.0/legalcode
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Dataset: Tracking the south polar seasonal cap retreat of Mars using computer vision
Zenodo
Acharya, Pruthviraj — 2024-07-15 These CSV files contain the ellipse fit parameters used for the analysis in Acharya, P. J., Smith, I. B., & Calvin, W. M. (2024). “Tracking the South Polar Seasonal Cap Retreat of Mars Using Computer Vision.” Icarus. DOI: 10.1016/j.icarus.2024.116104.  The SPSC ellipse is derived using a mosaic of MARCI images developed by Calvin, W. M., Cantor, B. A., & James, P. B. (2017). Interannual and seasonal changes in the south seasonal polar cap of Mars: Observations from MY 28-31 using MARCI. Icarus, 292, 144-153.. Each mosaic is 1000x1000 pixels with a spatial resolution of 0.072246423 Latitude ° per pixel.  The file names follow the convention ##.csv, where ## represents the Mars Year (MY). The following table shows what each variable presents.  Variable Name Description [Units] Ls  Solar Lonigude [°] Major Axis Semi-major axis value [Latitude °] Minor Axis Semi-minor axis value [Latitude °] Average Axis Average axis (See publication for more information) [Laitutde °] Major_Angle Rotation of the fitted ellipse from 0E (°) Dis_Center Distance between the center of the ellipse and the geographical center [Latitude °] Center X X coordinate of the center of the ellipse [Pixels] Center Y Y coordinate of the center of the ellipse [Pixels] Area Area of the ellipse [Squared kilometers] Circle_Radius The radius of the circle of best fit [Latitude °] Contour_Area Area of the SPSC [Squared kilometers] https://creativecommons.org/licenses/by/4.0/legalcode
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UV Reflectance of Spacecraft Materials and Analog Soils: Implications for Bioburden Reductions on the Undersides of Mars Rovers
Zenodo
Schuerger, Andrew; Moores, John — 2023-10-30 Abstract The Mars Sample Return mission architecture will utilize three spacecraft to collect, cache, recover, launch, and return to Earth a diversity of regolith and rock samples. However, no comprehensive Mars Microbial Survival (MMS) model currently exists.  As an initial effort in building a MMS model, we examined the UV reflectance of 15 spacecraft materials and seven Mars analog soils within the context of the Perseverance mission. Data were used to predict the times required to achieve one lethal dose (syn., Sterility Assurance Level [SAL]; def. as a bioburden reduction of ‒12 logs).  Results suggest that a single SAL dosage of UVC was achieved on exposed surfaces on the upper deck of Perseverance within a few hours to a few sols post-landing at Jezero Crater. The overall average for UVC reflectance from spacecraft materials was approx. 10%.  The overall UVC reflectance from Mars analog soils was measured at 1.3%.  The Adaptive Caching Assembly (ACA) on Perseverance is located on the forward edge of the underbelly of the spacecraft.  Modeling of the accumulated UVC dosage for the ACA yielded a prediction of reaching one SAL for downward facing surfaces at 93 sols that receive 'single bounce' UVC photons from the local terrain.  The SAL increases to 930 sols, if an additional 'bounce' of the solar UV irradiation is required to reach a partially protected site in the ACA hardware.  The current study is the first to report on the UVC reflectance from a diversity of spacecraft materials. The manuscript is in review (as of 30-Oct-2023) at Journal of Geophysical Research, Planets. https://creativecommons.org/licenses/by/4.0/legalcode
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PMW Sea Ice Thickness - from SSM/I and SSMIS CDR
Zenodo
Soriot, Clément; Prigent, Catherine; Jimenez, Carlos — 2024-10-02 Sea Ice Thickness is retrieved from brightness temperatures acquired by the SSM/I and SSMIS passive microwave radiometers from 1992 to 2020 for the months Oct-Mar. The algorithm uses the statistical relationships observed between passive microwave observations and SIT to train a Multi-Layered Perceptron (MLP) to reproduce ICESat-2 SIT from brightness temperatures at 19 and 37 GHz. The MLP has been trained on the polar winter 2018-2019 where it showed good performance when compared to the CryoSat-2 satellite retrieval and the Operation Ice Bridge airborne measurements.More details in: Soriot, C., Prigent, C., Jimenez, C., & Frappart, F. (2023). Arctic sea ice thickness estimation from passive microwave satellite observations between 1.4 and 36 GHz. Earth and Space Science, 10(2), e2022EA002542 Soriot, C., Vancoppenolle, M., Prigent, C., Jimenez, C., & Frappart, F. (2024). Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness. Scientific Reports, 14(1), 21000. https://creativecommons.org/licenses/by/4.0/legalcode
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Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks
Zenodo
Love, Ryan; Milne, Glenn A.; Ajourlou, Parviz; Parang, Soran; Tarasov, Lev; Latychev, Konstantin — 2023-10-25 Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively. Filenames denote the structure of the SS profile:  ???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}  LT = elastic lithosphere thickness (km) UMV = upper mantle viscosity (1E21 Pa s) LMV = lower mantle viscosity (1E21 Pa s) i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile 96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity   The columns of the input files are as follows: LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0) units for the above are as follows: km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m,  mm/year where 'days since 2000' assumes exactly 365.25 days per year. https://creativecommons.org/licenses/by/4.0/legalcode
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Data file for turbulence driving pattern in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
Zenodo
Price, Daniel; Federrath, Christoph; Tricco, Terrence — 2016-01-21 ** this file is automatically downloaded by phantom on running the code ** This is the default driving pattern file used in the "turbdrive" setup of the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code, which was originally written for the set of simulations shown in Price & Federrath (2010). The driving pattern file was originally created by Christoph Federrath. For details and references on the Ornstein-Uhlenbeck stochastic turbulent driving algorithm itself, see section 2.5 of the Phantom code paper (Price et al. 2018). The same pattern file has been used in several subsequent studies, including Tricco, Price & Federrath (2016) and Tricco, Price & Laibe (2017). For details on how to read this file see the Phantom source code (src/main/forcing.f90) https://creativecommons.org/licenses/by/4.0/legalcode
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Audio Data from: Jazz Trio Database
Zenodo
Cheston, Huw; Schlichting, Joshua Lukas; Cross, Ian; Harrison, Peter — 2024-09-23 The Jazz Trio Database is a dataset composed of about 45 hours of improvised jazz performances annotated by an automated signal processing pipeline. See our publication in Transactions of the International Society for Music Information Retrieval (DOI: 10.5334/tismir.186). This repository contains the audio files used to create the annotations (both mixed, "raw" files, and unmixed, "stem" files processed using audio source separation models). Its purpose is to serve as a reference database for the design, evaluation, and implementation of various music information retrieval systems, including (but not limited to) onset detection, beat tracking, automatic music transcription, and automatic performer identification. No annotations or metadata are provided with this archive; these can instead be found on the GitHub repository. Track names are consistent between audio, metadata, and annotations for a single track. All files are encoded as stereo 16-bit .WAV audio with a sample rate of 44.1 kHz. Downloading: We provide audio for JTD as two multi-part .zip files, one containing the unmixed audio and another containing the separated stems. After you've been granted access to the Zenodo record, the simplest way to download the entire database is to press the "Download all" button.   This will create a new file named files-archive (with no extension). Rename the file to files-archive.zip and extract using any unzipping tool (7zip, WinRAR, the unarchiver) or the command line. This will give you a list of multi-part zip files in the form [processed.zip.001, processed.zip.002, ...] and [raw.zip.001, raw.zip.002, ...].  To extract these, use 7zip from the command line with the following code: 7z x processed.zip.001 7z x raw.zip.001 Note that the default `unzip` command on Linux can't handle these files, so you'll need to use 7zip. You may also be able to use a GUI tool like WinRAR, which was used to create the archive in the first place. Also, be aware that each of those commands will extract all the wavs to the current folder, so you’ll likely need to move them afterwards. Thanks to Xavier Riley for providing these instructions! License JTD audio is provided for academic research purposes only and the material contained within it should not be used for any commercial purpose without the express permission of the copyright holders. Access will only be granted for research projects, and potential users of the audio data must apply for access to the data. These requests are checked manually: please do not fill in the form multiple times, we will aim to grant you access as soon as possible. Note that the annotations and metadata are provided on an open access basis and do not require permission to be granted.  Citation If you use the Jazz Trio Database in your work, please cite the paper where it was first introduced: @article{jazz-trio-database title = {Jazz Trio Database: Automated Annotation of Jazz Piano Trio Recordings Processed Using Audio Source Separation}, url = {https://doi.org/10.5334/tismir.186}, doi = {10.5334/tismir.186}, publisher = {Transactions of the International Society for Music Information Retrieval}, author = {Cheston, Huw and Schlichting, Joshua L and Cross, Ian and Harrison, Peter M C}, year = {2024}, }
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Dataset and software for manuscript 2023RS007900
Zenodo
Rohel, Remington; Ponomarenko, Pasha; McWilliams, Kathryn — 2024-03-11 This is a collection of software and data for the analysis included in the Radio Science manuscript 2023RS007900. https://creativecommons.org/licenses/by/4.0/legalcode
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AAPM TG Report 195: Monte Carlo Reference Data Sets for Imaging Research
Zenodo
Sechopoulos, Ioannis; Ali, Elsayed S. M.; Badal, Andreu; Badano, Aldo; Boone, John; Kyprianou, Iacovos; Mainegra-Hing, Ernesto; McNitt-Gray, Michael F.; McMillan, Kyle; Rogers, D. W. O.; Samei, Ehsan; Turner, Adam C. — 2015-09 Electronic Resources of the AAPM TG 195 Report "Monte Carlo Reference Data Sets for Imaging Research" (available at https://www.aapm.org/pubs/reports/detail.asp?docid=162 and with Executive Summary published by Medical Physics at https://doi.org/10.1118/1.4928676) https://creativecommons.org/licenses/by/4.0/legalcode
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Urban Air Pollution and Children's Brain Development: A Systematic Bibliometric Review
Zenodo
Rosalyn, Kefas; Roy, Raymond; D'Angiulli, Amedeo — 2024-10-05 The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period. Keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 450 publications, filtering based on co-occurrence of relevant keywords reduced this to approximately 50 papers. VOSviewer was employed to analyze co-occurrences and generate a visual map of relationships between air pollution and child neurodevelopment. The thesaurus was applied to standardize terminology, refining the final network for detailed analysis of keyword clusters and their interactions. https://creativecommons.org/licenses/by/4.0/legalcode

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