Find research datasets worth reusing

illumina_resp_viruses_masking

Kraken2 database containing the illumina resvpiratory viruses with 83 accession numbers (34 unique taxons). Database used in Pipoli da Fonseca et al  (https://pubmed.ncbi.nlm.nih.gov/36369470/)

bacteria_masking:v24.4.4

Kraken2 Database for bacteria built with masking option, in april 2024. Contains 102,758 accession numbers (14,312 unique taxons).

opencc-by-4.0Mar 2018View details →

The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility

Contains all the data: Bentsen, T., T.May, A. A. Kresnner, and T. Dau. The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility. PLOS ONE., in review. There are two folders: <ol> <li>WRSs: the Word Recognition Scores (WRSs) from the listener study. The matrix has dimensions 9 conditions x 20 subjects. Data is ordered corresponding to the following condition order: 'UP', 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)', 'DNN (IBM)'; 'DNN (IBM, 40 ms)'; 'DNN (IRM)'; 'DNN (IRM, 40 ms)'</li> <li>Masks: <ul> <li>GMM-IBMs: IBMs and estimated IBMs for the models 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)'</li> <li>DNN-IBMs: IBMs and estimated IBMs for the models 'DNN (IBM)'; 'DNN (IBM, 40 ms)'</li> <li>DNN-IRMs: IRMs and estimated IRMs for the models  'DNN (IRM)'; 'DNN (IRM, 40 ms)'</li> </ul> </li> </ol>

opencc-by-4.0Oct 2018View details →

Sentinel-2 reference cloud masks generated by an active learning method

Reference classifications generated with Active Learning for Cloud Detection (ALCD) This data set provides a reference cloud mask data set for 38 Sentinel-2 scenes. These reference masks have been created with the ALCD tool, developed by Louis Baetens, under the direction of Olivier Hagolle at CESBIO/CNES[1]. They were created to validate the cloud masks generated by the MAJA software [2]. - The `Reference_dataset` directory contains 31 scenes selected in 2017 or 2018. - The `Hollstein` directory contains 7 scenes that were used to validate the ALCD tool by comparison to manually generated reference images kindlyprovided by Hollstein et al[3] One of these scenes is present in both directories. For the validation of MAJA, the "Hollstein" scenes were not used because of their acquisition at a time period when Sentinel-2 was not yet operational, with a degraded repetitivity of observations. # Description of the data structure The name of each scene directory is the name of the corresponding Sentinel-2 L1C product. In the scene directory, three sub-directories can be found. - `Classification` - `Samples` - `Statistics` # Description of the files - `Classification/classification_map.tif` --- the main product, which is the classified scene. 7 classes are available. Each one is represented with a different integer. 0: no_data. 1: not used. 2: low clouds. 3: high clouds. 4: clouds shadows. 5: land. 6: water. 7: snow. - `Classification/confidence_enhanced.tif` --- enhanced confidence map of the classification. The values are between 0 and 255 (coded on 1 bit). The original confidence map is, for each pixel, the proportion of votes for the majority class as the classification map has been created via a Random Forest algorithm. A median filter has been applied to this confidence map. Finally, the value was saved on 1 bit, leading to the value being between 0 and 255. - `Classification/contours.png` --- the contours of the classes from the classification map, overlayed on the scene. The color code depends on each class. Green: low and high clouds. Yellow: cloud shadows. Blue: water. Purple: snow. - `Classification/used_parameters.json` --- the parameters that were used to classify the scene. It includes the tile code, the cloudy and clear dates, along with their product reference. - `Samples/` --- this directory contains all the shapefiles, one per class. - `Statistics/k_fold_summary.json` --- results of the 10-fold cross-validation on the scene. 5 metrics are computed, in the order given in the "metrics_names". "all_metrics" is a list of the 10 folds, with the 5 metrics in the correct order for each fold. "means" and "stds" are the means and standard deviations of the 10 folds. # References [1] Baetens, L.; Desjardins, C.; Hagolle, O. Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens. 2019, 11, 433. [2] A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images, O Hagolle, M Huc, D. Villa Pascual, G Dedieu, Remote Sensing of Environment 114 (8), 1747-1755, 2010 [3] Hollstein, A.; Segl, K.; Guanter, L.; Brell, M.; Enesco, M. Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images. Remote Sens. 2016, 8, 666

opencc-by-4.0Nov 2018View details →

Masked datasets from an fMRI experiment on the impact of semantic priming on the perception of ambivalent (male versus female) faces

Twenty-four female native Dutch speakers participated in the fMRI experiment and gained monetary compensation for their participation. Only female participants were recruited for the study, in order to avoid gender-related confounding factors. The study was approved by the local ethics committee (CMO Arnhem-Nijmegen, Radboud University Medical Center, ethical approval for studies on healthy human subjects at the Donders Centre for Cognitive Neuroimaging, no ECG 2012-0910-058) and conducted in accordance with their guidelines. All participants signed informed consent forms before the experiment. The data from seven subjects were excluded from the analysis: 3 subjects failed to finish the task and 4 subjects exhibited head motion that exceeded the maximum acceptance rate of 2 [mm]. The remaining 17 subjects (females, age 18-29 years) reported no neurological diseases, and had normal or corrected-to-normal vision.  A set of realistic 3D faces was morphed across gender (from extremely female to extremely male) using FaceGen Modeller 3.5 (Singular Inversions, www.facegen.com). The morphing procedure started from 40 distinct faces. For each face, we gradually modulated gender features in 5 steps with the same amount of feature transformation in each step. The face stimuli were presented frontally and cropped around the oval of the face. We controlled for luminance using SHINE toolbox for MATLAB. The perceptual boundary within gender continuum of faces was established in a separate behavioral experiment. Each trial started with priming: presentation of a gender-related word 'man' or 'vrouw' for 0.2 [s]. Then, after the fixation cross 0.25 [s]), a face was presented (0.5 [s]), followed by an inter-trial period of a randomized length of 5-7 [s]. Participants were asked to perform a matching task: respond 'yes' if a word and subsequent picture corresponded in gender, and 'no' otherwise. The experiment was carried out in Dutch. The buttons were counterbalanced across subjects. The experiment was divided into 6 blocks in order to avoid fatigue. Each block consisted of 50 trials. The order of stimuli was randomized across blocks and participants. We used Presentation software (version 17.1, www.neurobs.com) in order to screen the stimuli during the experiment. Functional images were acquired using 3T Skyra MRI system (Siemens Magnetom), T2* weighted echo-planar images (gradient-echo, repetition-time TR = 1760 [ms], echo-time TE = 32 [ms],  0.7 [ms] echo spacing, 1626 hz/Px bandwidth, generalized auto-calibrating partially parallel acquisition (GRAPPA), acceleration factor 3, 32 channel brain receiver coil). In total, 78 axial slices were acquired (2.0 [mm] thickness, 2.0*2.0 [mm] in plane resolution,  212 [mm] field of view (FOV) whole brain, anterior-to-posterior phase-encoding direction). The data reprocessing was performed using SPM12 (Welcome Trust Center for Neuroimaging, University College London, UK). Functional scans were realigned to the first scan of the first run with further realignment to the mean scan. We performed slice-time correction on realigned images to account for differences in image acquisition between slices. Motion-related components were removed from the data using a data-driven ICA-AROMA. Denoised functional scans were spatially normalized to the Montreal Neurological Institute (MNI) space without changing the voxel size. Normalized data were smoothed spatially with a Gaussian kernel of 6 [mm] full-width at half-maximum. We extracted region-of-interest (ROI) mask using Anatomical Automatic Labeling atlas (AAL). According to our a priori hypothesis, we preselected the bilateral SPL (4288 voxels) and the bilateral IPL (3792 voxels).

Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - CALIBRATED MOVEMENT DATA AND VARIABLES SUPPORTING ANALYSES

Description of the data and file structure This record contains data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:  Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. Global Change Biology. In press. The data include the following: (1) calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13328931 - all 2011 & 2014 audio data. These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.   Description of the movement data files The movement files have been calibrated from the raw data and are ready to use. The files contain the .mat extension, and need to be opened using Matlab and the tagtools tool kit available at https://github.com/animaltags . Tutorials for working with the toolkit are available at animaltags.org .  These files contain several vector and matrix variables. We define those used in our analyses below. For questions about how to work with these files, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu. Aw: calibrated triaxial accelerometer data (converted from tag frame to whale frame) fs: sample rate (50 Hz) head: animal's circular heading (rotation about the dorsal-ventral axis, in radians) Mw: calibrated triaxial magnetometer data (converted from tag frame to whale frame) p: depth (in meters) pitch: animal's pitch (rotation about the left-right axis, in radians) roll: animal's roll (rotation about the anterior-posterior axis, in radians) tempr: temperature recorded on tag (in Celsius) TT: time cues for the start and end of every analyzed dive within a deployment. This matrix contains 6 columns: -col 1: start cue (in sec) -col 2: end cue (in sec) -col 3: maximum depth of dive (m) -col 4: time cue at max depth (in sec) -col 5: mean depth (m) -col 6: mean compression   Description of the analyzed variables The data are provided column-wise in a spreadsheet, whereby each column contains one of several variables used to build the corresponding models listed in Table 2 in the above article. Model details are provided in the above article, including the statistical packages needed to run the models.  The following is a list of variable names (column headers) and their corresponding definitions: bzsounds: binary presence (1)/absence (0) of buzz bouts within a dive. Buzzing is defined as the occurrence of echolocation clicks with an inter-click interval < 11 ms code: categorical identifier of the numerical week of year in which the tag was deployed (e.g., week 33 of 2009 is different than week 33 of 2011) deployment: the event whereby a tag was affixed to an individual killer whale and data were collected via tag sensors; each deployment was assigned a unique deployment ID, consisting of the first letter of the Genus and species names (“oo” for Orcinus orca), followed by two digits corresponding to the year (“09” = 2009), followed by the Julian day of the year (e.g. “234”), followed by a letter indicating the deployment order of the day. NRKW deployments were assigned a through l, and SRKW deployments were assigned m through z (e.g. “a” = first deployment of the day for NRKW, “m” = first deployment of the day for SRKW) durwho: duration of a whole dive, in seconds. Dives were defined as all departures from the surface, to at least 1 m or deeper, followed by a return to within 0.5 m of the surface divenum: chronological identifier for dive position within a deployment (e.g., for the 10th dive within a deployment, divenum = 10) kindet: binary presence (1)/absence (0) of a prey capture event within a dive. Prey capture was informed by the occurrence of stereotyped movement signatures in sensor data indicative of prey capture, following an established method validated with visual and acoustic confirmation of predation events. Prey capture is defined as the occurrence of three movement variables indicative of prey capture (peak jerk, roll and heading variance) each exceeding pre-determined thresholds (see Tennessen et al. 2019b in above article for details) maxdep: maximum depth of a dive, in meters NLmax: the maximum noise level received during a dive, measured as the root-mean-square sound pressure level (dB re 1 mPa) within one second bins over the 15-45 kHz frequency band population: population to which the tagged whale belongs (NRKW = Northern Resident killer whale; SRKW = Southern Resident killer whale) sex: sex of tagged whale (F = female, M = male, NA = unknown) sc: binary presence (1)/absence (0) of slow-click sounds within a dive. Slow-clicking is defined as the occurrence of echolocation clicks with an inter-click interval >100 ms tagID: identifier for the individual tag used for each deployment year: year of deployment

protozoa_masking:v21.1.1

kraken2 DB for protozoa built with masking option. Contains 11393 accession numbers corresponding to 41 unique taxon.

univec_core_masking:v24.8.1

kraken2 DB for univec built with masking option. Contains 3157 accession number corresponding to 1 unique taxon (28384).

univec_masking:v21.1.1

kraken2 DB for univec built with masking option. Contains 6093 accession number corresponding to 1 unique taxon (28384)

univec_core_masking:v21.1.1

kraken2 DB for univec built with masking option. Contains 3137 accession number corresponding to 1 unique taxon (28384).

univec_masking:v24.8.1

kraken2 DB for univec built with masking option. Contains 6113 accession number corresponding to 1 unique taxon (28384)

fungi_masking:v21.1.1

kraken2 DB for fungi built with masking option. Contains 1472 accession number corresponding to 58 unique taxons Built in Jan. 2021   v2 adds the added_nomasking sequences. for book-keeping

archaea_masking:v21.1.1

Kraken2 DB for archaea built with masking option in Jan 2021. Contains 567 accession numbers (331 unique taxons).  Built with kraken2 in Jan. 2021 <ul> <li>v2 just adds the added.fna.gz files for book keeping(unmasked sequences)</li> </ul>

plasmid_masking:v24.8.20

kraken2 DB for plasmid built with masking option in Aug 2024. Contains 93587 accession numbers corresponding to 6804 taxons.

bacteria_masking:v21.8.1

Kraken2 DB for bacteria with masking option. DB built 6months after v21.1.1 with 10355 unique taxons and 59483 accession numbers

human_mouse_masking:v21.1.1

This is a kraken2 DB with both human and mouse together. hg38 and mm38

archaea_masking:v24.8.1

kraken2 DB for archaea built with masking option. Contains 126170 accession numbers corresponding to 1305 unique taxons. When building this DB, ftp links were broken, Archaea genomes were downloaded manually using download.sh script (provided here)

drosophila_masking:v24.8.1

kraken2 DB for drosophila built with masking option. This DB includes 41 species with  (48657 accessions) See download.sh for details about the sequences included.

chiroptera_masking:v24.8.1

Kraken2 DB for chiroptera order with masking option. 20 different genomes from NCBI (see download.sh)

Float+SOCAT sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed

Here we provide sampling masks used in the study "The importance of adding unbiased Argo observations to the ocean carbon observing system" (Heimdal & McKinley, 2024, Scientific Reports). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 2 different sampling masks used in the experiments presented in Heimdal & McKinley (2024). These masks represent two different float sampling schemes (+SOCAT) including 500 floats, corresponding to historical Argo float observations (https://fleetmonitoring.euro-argo.eu/dashboardpatterns) and potential optimized float sampling (following Chamberlain et al., 2023, <a href="https://doi.org/10.1175/JTECH-D-22-0093.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-22-0093.1</a>).