cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21413/iasi-ft_metopa_cld_l2_ulb-latmos
title: A CO2-free cloud mask from IASI radiances for climate applications (from IASI/Metop-A)

description: The IASI Cloud Detection product (IASI CLD) is a cloud mask available at the IASI pixel level (L2) that was developed for climate applications purposes. The algorithm is detailed in Whitburn et al. (2022, Atmospheric Measurement Techniques). It combines (1) a high sensitivity to cloud detection, (2) a very good consistency over time and between the three IASI instruments and (3) simplicity in its parametrization. The method is based on the use of a supervised neural network using the version 6.5 of the operational IASI L2 cloud product as a reference dataset. As input parameters, it relies on IASI radiance information only. The consistency over time is ensured by careful selection of the IASI input channels, avoiding especially the spectral regions affected by long-lived absorbers and water vapor. The version number is 1.0. IASI/Metop CLD data are available daily from 2008 to 2020 for Metop-A, Metop-B and Metop-C in hdf format. For Metop-A before 2016, the algorithm was applied to reprocessed IASI L1C data for more homogeneity in the data set. Each file includes the latitude, the longitude, the neural network’s retrieval, and the IASI overpass time. Note that the retrieval of the neural network provides a value comprised between 0 and 1. This value is converted in a cloud mask using an appropriate threshold. We recommend using the thresholds provided in Whitburn et al. (2022) (i.e. 0.175 for land and 0.275 for oceans) but these can be adjusted depending on the application.
description: The algorithm is detailed in Whitburn et al. (2022, Atmospheric Measurement Techniques). It combines (1) a high sensitivity to cloud detection, (2) a very good consistency over time and between the three IASI instruments and (3) simplicity in its parametrization. The method is based on the use of a supervised neural network using the version 6.5 of the operational IASI L2 cloud product as a reference dataset. As input parameters, it relies on IASI radiance information only. The consistency over time is ensured by careful selection of the IASI input channels, avoiding especially the spectral regions affected by long-lived absorbers and water vapor.
description: Product data level : "L2"
description: https://iasi.cnes.fr/
description: Publication: Whitburn, S., Clarisse, L., Crapeau, M., August, T., Hultberg, T., Coheur, P. F., and Clerbaux, C.: A CO2-independant cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-127, accepted, 2022
DOI: 10.6084/m9.figshare.14294492
title: CO fire emissions Naus et al. (2022)

description: Fire emissions of carbon monoxide (CO) in the Amazon basin over the 2003 to 2018 time period, optimized with CO column data from the Thermal InfraRed (TIR) product of the Measurements of Pollution In The Troposphere (MOPITT) satellite in the TM5-4DVAR inverse system. These emissions were produced in the reference inversions described in Chapter 4 of Stijn Naus' PhD thesis (available on https://edepot.wur.nl/536720), as well as in Naus et al. (2022). The inversions start from prior emissions of the Global Fire Assimilation System (GFAS), also available online (https://atmosphere.copernicus.eu/global-fire-emissions). <br> The optimized emissions are given for the two zoom domains over which satellite data have been assimilated. Advised is to only use emissions from the 1 by 1 degree inner zoom domain. Emissions only cover the April to December inversion window.
DOI: 10.6084/m9.figshare.20539398.v1
title: Figure 4 B data set Measurement of antioxidant capacity in 661W and BKO cells

description: The spectrometry counts obtained from antioxidant assay in 661W and BKO cells.
DOI: 10.14278/rodare.1862
title: Data and code for: Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting

description: Data and code that can be used to reproduce the analyses underlying 'Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting' by Alston, Fleming, et al. (Preprint: https://doi.org/10.1101/2022.04.21.489059) For more detailed information, please visit the README file.
DOI: 10.15482/usda.adc/1527977
title: Data from: Not just crop or forest: building an integrated land cover map for agricultural and natural areas (tabular files)

description: Introduction and Rationale:
Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product.
Contents:
Spatial data
1. Attribute table for merged rasters
Technical validation data
1. Number and proportion of mismatched pixels
2. Number and proportion of unresolved pixels
3. Producer's and User's accuracy values and coverage of reference data
DOI: 10.17632/4y5mzppzmb.1
title: Ancient origin and constrained evolution of the division and cell wall (dcw) gene cluster across Bacteria

description: SUPPORTING DATA:
 
Data from the manuscript of Megrian et al., "Ancient origin and constrained evolution of the division and cell wall (dcw) gene cluster across Bacteria”

- CONCAT/
	- concat_12DCW.treefile
	Phylogeny presented in Figure 6 and Supplementary Figure 14, in newick format.
	- concat_12DCW_collapsed.treefile
	Same phylogeny as concat_12DCW.treefile, but with phyla collapsed into a single branch.
	- concat_12DCCW.aln
	Concatenation alignment used to reconstruct the phylogeny. Corresponds to the concatenation of 12 dcw cluster proteins.
	- RENAME_CONCAT.txt
	Annotation file to rename the labels of the newick file on iTol (https://itol.embl.de)
	- cleaned_trimmed_single_alignments/
	Directory containing cleaned and trimmed single alignments used for the concatenation.

- PARALOGS/
	- *.treefile
	Phylogenies presented in Figure 4 and Supplementary Figures 2, 3 and 4, in newick format.
	- *.trim
	Trimmed alignments used to reconstruct the phylogenies.
	- *.aln
	Alignments used to reconstruct the phylogenies (before trimming).
	- *.fasta
	Sequences aligned to reconstruct the phylogenies.

- PastML/
	- pastml_raw_output.tab
	Output of PastML inferences. Columns correspond to contiguous pairs of dcw cluster genes. Rows correspond to node names in the reference phylogeny. 0 refers to absence of the pair in the correspoding node, 1 refers to presence.
	- pastml_ref_tree.treefile
	Reference phylogeny used for the inference. Node names are indicated.

- SGT_before_cleaning
	- *_raw.treefile
	Single gene phylogenies obtained after the homology searches, before cleaning.
	- *_raw.trim
	Trimmed alignments used to reconstruct the phylogenies.
	- *_raw.aln
	Alignments used to reconstruct the phylogenies (before trimming).
	- *_raw.fasta
	Sequences aligned to reconstruct the phylogenies.
	- RENAME_SGT.txt
	Annotation file to rename the labels of the newick files on iTol (https://itol.embl.de)

- OTHER_REF_TREES
	- CORE
	Contains CONCAT and SGT_before_cleaning data, based on 63 core genome markers.
	- RNAPOL_IF2
	Contains CONCAT and SGT_before_cleaning data, based on RNApol+IF2 markers.
	- RPROTS
	Contains CONCAT and SGT_before_cleaning data, based on 16rprot markers.

- CPR
	- CPR.treefile
	Phylogeny presented in Figure 5. Obtained from a supermatrix that contains 302 sequences (27 chloroflexi + 275 CPRs), and 2126 amino acid positons. The concatenated proteins are: MurG, MurF, MurE, MurD, MurC, MraZ, MraY, MraW, FtsZ, FtsW, FtsI, FtsA. IQ-TREE v2.3.1 was used to infer the ML tree. Best-fit model: LG+F+R10 chosen according to BIC. Ultrafast bootstrap 1000.

- TREES_CLEANING
	- cleanTrees.R
	R script used for cleaning phylogenies.
	- SAMPLE_FILES
	Files needed to run the script.
DOI: 10.17632/z8yhd3sj23.4
title: Isochromatic-art: a computational dataset for evaluating the stress distribution of loaded bodies by digital photoelasticity

description: Importance of evaluating the stress field of loaded structures lies in the need of identifying the forces which make them fail, redesigning their geometry to increase the mechanical resistance, or characterize unstressed regions to remove material. In such work line, digital photoelasticity highlights by the possibility of reveal the stress information through isochromatic color fringes, and quantifying it through inverse problem strategies. However, the absence of public data with a high variety of spatial fringe distribution have limited developing new proposals which generalize the stress evaluation in a wider variety of industrial applications. This dataset shares a variated collection of stress maps and their respective representation in color fringe patterns. In this case, the data were generated following the computational strategy in [1], but assuming stress surfaces and patches derived from analytical stress models, 3D reconstructions, saliency maps, and superpositions of Gasussian surfaces. At total, two sets of ‘101430’ raw images were separatelly generated for stress maps and isochromatic color fringes, respectively. This dataset can be valuable for researchers interested in characterizing the mechanical response in loaded models, engineers in computer science interested in modeling inverse problems, and scientist who work in physical phenomena such as 3D reconstruction in visible light, bubble analysis, oil surfaces, and film thickness.
DOI: 10.21413/iasi-ft_metopb_cld_l2_ulb-latmos
title: A CO2-free cloud mask from IASI radiances for climate applications (from IASI/Metop-B)

description: The IASI Cloud Detection product (IASI CLD) is a cloud mask available at the IASI pixel level (L2) that was developed for climate applications purposes. The algorithm is detailed in Whitburn et al. (2022, Atmospheric Measurement Techniques). It combines (1) a high sensitivity to cloud detection, (2) a very good consistency over time and between the three IASI instruments and (3) simplicity in its parametrization. The method is based on the use of a supervised neural network using the version 6.5 of the operational IASI L2 cloud product as a reference dataset. As input parameters, it relies on IASI radiance information only. The consistency over time is ensured by careful selection of the IASI input channels, avoiding especially the spectral regions affected by long-lived absorbers and water vapor. The version number is 1.0. IASI/Metop CLD data are available daily from 2008 to 2020 for Metop-A, Metop-B and Metop-C in hdf format. For Metop-A before 2016, the algorithm was applied to reprocessed IASI L1C data for more homogeneity in the data set. Each file includes the latitude, the longitude, the neural network’s retrieval, and the IASI overpass time. Note that the retrieval of the neural network provides a value comprised between 0 and 1. This value is converted in a cloud mask using an appropriate threshold. We recommend using the thresholds provided in Whitburn et al. (2022) (i.e. 0.175 for land and 0.275 for oceans) but these can be adjusted depending on the application.
description: The algorithm is detailed in Whitburn et al. (2022, Atmospheric Measurement Techniques). It combines (1) a high sensitivity to cloud detection, (2) a very good consistency over time and between the three IASI instruments and (3) simplicity in its parametrization. The method is based on the use of a supervised neural network using the version 6.5 of the operational IASI L2 cloud product as a reference dataset. As input parameters, it relies on IASI radiance information only. The consistency over time is ensured by careful selection of the IASI input channels, avoiding especially the spectral regions affected by long-lived absorbers and water vapor.
description: Product data level : "L2"
description: https://iasi.cnes.fr/
description: Publication: Whitburn, S., Clarisse, L., Crapeau, M., August, T., Hultberg, T., Coheur, P. F., and Clerbaux, C.: A CO2-independant cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-127, accepted, 2022
DOI: 10.21413/iasi-ft_metopc_cld_l2_ulb-latmos
title: A CO2-free cloud mask from IASI radiances for climate applications (from IASI/Metop-C)

description: The IASI Cloud Detection product (IASI CLD) is a cloud mask available at the IASI pixel level (L2) that was developed for climate applications purposes. The algorithm is detailed in Whitburn et al. (2022, Atmospheric Measurement Techniques). It combines (1) a high sensitivity to cloud detection, (2) a very good consistency over time and between the three IASI instruments and (3) simplicity in its parametrization. The method is based on the use of a supervised neural network using the version 6.5 of the operational IASI L2 cloud product as a reference dataset. As input parameters, it relies on IASI radiance information only. The consistency over time is ensured by careful selection of the IASI input channels, avoiding especially the spectral regions affected by long-lived absorbers and water vapor. The version number is 1.0. IASI/Metop CLD data are available daily from 2008 to 2020 for Metop-A, Metop-B and Metop-C in hdf format. For Metop-A before 2016, the algorithm was applied to reprocessed IASI L1C data for more homogeneity in the data set. Each file includes the latitude, the longitude, the neural network’s retrieval, and the IASI overpass time. Note that the retrieval of the neural network provides a value comprised between 0 and 1. This value is converted in a cloud mask using an appropriate threshold. We recommend using the thresholds provided in Whitburn et al. (2022) (i.e. 0.175 for land and 0.275 for oceans) but these can be adjusted depending on the application.
description: The algorithm is detailed in Whitburn et al. (2022, Atmospheric Measurement Techniques). It combines (1) a high sensitivity to cloud detection, (2) a very good consistency over time and between the three IASI instruments and (3) simplicity in its parametrization. The method is based on the use of a supervised neural network using the version 6.5 of the operational IASI L2 cloud product as a reference dataset. As input parameters, it relies on IASI radiance information only. The consistency over time is ensured by careful selection of the IASI input channels, avoiding especially the spectral regions affected by long-lived absorbers and water vapor.
description: Product data level : "L2"
description: https://iasi.cnes.fr/
description: Publication: Whitburn, S., Clarisse, L., Crapeau, M., August, T., Hultberg, T., Coheur, P. F., and Clerbaux, C.: A CO2-independant cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-127, accepted, 2022
DOI: 10.5067/tz6i8e3odiqb
title: Aqua/AIRS L2 Support Retrieval (AIRS+AMSU) V7.0
DOI: 10.5281/zenodo.4032577
title: Data used for figures and tables in "Increase in ocean acidity variability and extremes under increasing atmospheric CO2"

description: The data used to create the figures and tables in the paper: Burger, Friedrich A., John, Jasmin G., and Frölicher, Thomas L., "Increase in ocean acidity variability and extremes under increasing atmospheric CO2", Biogeosciences, in press, 2020.
DOI: 10.5281/zenodo.4135325
title: Data used in the Living Planet Index analysis in Pacoureau et al. 2020 - Half a century of global decline in oceanic sharks and rays

description: Data used in the Living Planet Index analysis in Pacoureau et al. 2020 - Half a century of global decline in oceanic sharks and rays. Available at www.sharkipedia.org.
DOI: 10.5281/zenodo.4288759
title: Skin care interventions for preventing eczema and food allergy: RoB 2 assessments

description: This excel file contains the detailed risk of bias assessment data, with consensus responses to the signalling questions, using the Cochrane Risk of bias 2 tool for Kelleher MM, Cro S, Cornelius V, Axon E, Lodrup Carlsen KC, Skjerven HOve, Rehbinder EMaria, Lowe A, Dissanayake E, Shimojo N, Yonezawa K, Ohya Y, Yamamoto-Hanada K, Morita K, Surber C, Cork M, Cooke A, Tran L, Askie LM, Duley L, Van Vogt E, Chalmers JR, Williams HC, Boyle RJ. Skincare interventions in infants for preventing eczema and food allergy. Cochrane Database of Systematic Reviews 2020, Issue 2. Art. No.: CD013534. DOI: 10.1002/14651858.CD013534.
DOI: 10.5281/zenodo.6325611
title: Dataset for manuscript Internet-administered, guided, low-intensity cognitive behavioural therapy for depression and/or generalized anxiety in parents of children treated for cancer: A single arm feasibility trial (ENGAGE)

description: Dataset supporting findings in the manuscript <strong>Internet-administered, guided, low-intensity cognitive behavioural therapy for depression and/or generalized anxiety in parents of children treated for cancer: A single arm feasibility trial (ENGAGE). </strong>The study was prospectively registered in the ISRCTN registry (ISRCTN 57233429).
DOI: 10.5281/zenodo.6601173
title: Processed ground observation and WRF-CAMQ data for Greater Bay Area, 2015-2021

description: The pre-processed training and testing dataset for the paper <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement. </em>The format of data is compatible with the official implementation.
DOI: 10.5281/zenodo.6833673
title: Prediction of the broadcasting model and various baselines

description: The ground truth values, and the predictions from the broadcasting model and the baselines (SC and CMAQ) in the submission <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>. The namelists of the WRF and CMAQ models are also included.
DOI: 10.5281/zenodo.6975857
title: Data used for developing a parameterization for spatial distribution of solar irradiance over rugged terrain

description: This dataset includes data and results produced while developing a parameterization for the spatial distribution of solar radiation over mountainous terrain. The method is designed for applications in Earth System Models. The four items included in this dataset are the following: <strong>GFDL_preproc_dems</strong> includes digital elevations maps derived from the Shuttle Radar Topography Mission (SRTM) for three sample domains. <strong>single-time-step </strong>Includes atmospheric optical properties used as input for Monte Carlo simulations. <strong>gmd_2021_grids_light_*</strong> Include maps of terrain parameters derived from SRTM elevation data, and the partition of the domains in homogeneous tiles (sub-grid units) for different number and types of of land units. <strong>rmc_simul_res</strong> results of the Monte Carlo simulations, consisting of maps of simulated solar radiation for different domains and solar angles.
DOI: 10.5281/zenodo.7221291
title: IrrigationMapV0, initial files and scripts to reproduce the simulation of Druel et al, 2022

description: DATA supplementary to the corresponding article published in Geosci. Model Dev.: Druel, A., Munier, S., Mucia, A., Albergel, C., and Calvet, J.-C.: Implementation of a new crop phenology and irrigation scheme in<br> the ISBA land surface model using SURFEX_v8.1, 2022. ==&gt; Start by read the "README.txt" file, it explains how to use all files and be able to reproduce the simulations describe in the article. ==&gt; The map of irrigation can be downloaded and used for other applications. In this case, please refer to the article above. <strong>Liste of files:</strong> irrigcover_v0.tar.gz: Irrigation map 300 m x 300 m used for irrigation in ECOCLIMAP-SG. From a spatial resampling from the 1 km x 1km map of Meier et al, 2017. PGD.nc contains the initial characteristics of the simulation, such as the fraction of irrigation. It's computed at the beginning of the simulation and ensures the accuracy of the initial configuration. PREP.nc contains the result of the 20 year spinup (by repeating year 1979). It is used as initial condition of the published simulation. forcingScript.tar.gz contains scripts to help to produce forcing files (code made for LDAS launch is used, https://www.umr-cnrm.fr/spip.php?article1022) runLauncherFiles.tar.gz contains scripts and specific config files to be able to launch the simulations of the article with the exact same configurations.
DOI: 10.6084/m9.figshare.20538885.v1
title: Figure 3 Cell viability from oxidative stress challenge

description: The cell survival rate following exposure to H2O2 or vehicle control treatment in 661W and BKO cells.
DOI: 10.5281/zenodo.6598377
title: Ground obeservation data (meteorological factors and air pollution) in Greater Bay Area, 2015 - 2021

description: Ground observation data used in the paper <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>.
DOI: 10.15482/usda.adc/1527978
title: Data from: Not just crop or forest: building an integrated land cover map for agricultural and natural areas (spatial files)

description: Introduction and Rationale:
Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product.
Contents:
Spatial data
1. National rasters of land cover in the conterminous United States: 2012-2021
DOI: 10.17632/d4hf2hjjxr.2
title: Therapeutic Exercise & Vitamin D for CLBP

description: the dataset contains the extended data for the protocol titled "Comparison of the effects of Therapeutic Exercise with either an Educational Booklet or Vitamin-D3 Supplement in the Management of Chronic Low Back Pain: Study Protocol for an Assessor Blinded Multicenter Randomized Clinical Trial"