cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22354789
title: Left ventricular global longitudinal strain as a parameter of mild myocardial dysfunction in athletes after COVID-19

description: Left ventricular function in athletes after COVID-19 was assessed by strain measurement. GLS was significantly lower and diastolic function significantly reduced in athletes who did not have a history of LV dysfunction but had a positive COVID-19 test compared with healthy control athletes. We found no association between GLS and acute symptoms during COVID-19. GLS determination could be used as a screening element for early detection of myocardial dysfunction during return-to-sport examinations.
DOI: 10.6084/m9.figshare.22354789.v1
title: Left ventricular global longitudinal strain as a parameter of mild myocardial dysfunction in athletes after COVID-19

description: Left ventricular function in athletes after COVID-19 was assessed by strain measurement. GLS was significantly lower and diastolic function significantly reduced in athletes who did not have a history of LV dysfunction but had a positive COVID-19 test compared with healthy control athletes. We found no association between GLS and acute symptoms during COVID-19. GLS determination could be used as a screening element for early detection of myocardial dysfunction during return-to-sport examinations.
DOI: 10.5281/zenodo.7782292
title: All analysis steps of SLR1 & SLR2

description: All analysis steps of SLR_1 and SLR_2 related to teachers´digital competence in the context of China.
DOI: 10.5281/zenodo.7782291
title: All analysis steps of SLR1 & SLR2

description: All analysis steps of SLR_1 and SLR_2 related to teachers´digital competence in the context of China.
DOI: 10.6084/m9.figshare.21890712.v1
title: Data and R Code from the Tassie forest degradation study

description: Data and R code from the forest degradation study across Tasmania, Australia. Data includes soil properties as well as microbial community data.
DOI: 10.5066/p9p7k0av
title: The Contaminant Exposure and Effects-Terrestrial Vertebrates database (CEE-TV)

description: The information provided in the Contaminant Exposure and Effects-Terrestrial Vertebrates (CEE-TV) Database profiles available geo-referenced information on contaminant exposure and effects in terrestrial vertebrates along the U. S. Atlantic, Gulf, and Pacific coasts (including Hawaii and Alaska) and the Great Lakes.
DOI: 10.6084/m9.figshare.22354738
title: PONE-D-22-23672

description: Data set to go with PLOS ONE Manuscript PONE-D-22-23672
DOI: 10.6084/m9.figshare.22354738.v1
title: PONE-D-22-23672

description: Data set to go with PLOS ONE Manuscript PONE-D-22-23672
DOI: 10.6084/m9.figshare.22009429
title: Long-term trends in inflowing chlorophyll <i>a</i> and nutrients and their relation to dissolved oxygen in a large western reservoir

description: Naymik J, Larsen CA, Myers R, Hoovestol C, Gastelecutto N, Bates D. 2023. Long-term trends in inflowing chlorophyll <i>a</i> and nutrients and their relation to dissolved oxygen in a large western reservoir. Lake Reserv Manage. 39:53–71. Anoxia in Brownlee Reservoir is one of the numerous water quality issues associated with eutrophic conditions in the Snake River as it flows through southern Idaho and parts of eastern Oregon. The states of Idaho and Oregon have developed total maximum daily loads (TMDLs) for multiple reaches of the Snake River and its tributaries upstream of Brownlee Reservoir intended to address poor water quality. Despite the emphasis on developing TMDLs throughout the Snake River and its tributaries, published long-term trend monitoring to evaluate the results of the TMDLs is lacking. Trends in Snake River concentrations and loads summarized using weighted regressions on time, discharge, and season show that combined efforts to improve water quality upstream of Brownlee Reservoir have realized decreasing trends in concentrations of chlorophyll <i>a</i>, total phosphorus, and suspended solids (80%, 46%, and 61% reductions, respectively) from 1995 to 2021. Brownlee Reservoir, a large mainstem reservoir with short residence time, has responded quickly to inflowing reductions of chlorophyll <i>a</i> and total phosphorus. Since 2005, dissolved oxygen (DO) has improved in the reservoir, with a 33% reduction in the volume of the reservoir having DO less than 1 mg/L. This supports the primary premise of upstream TMDLs and demonstrates that inflowing water quality improvements are effective at improving in-reservoir dissolved oxygen.
DOI: 10.6084/m9.figshare.22009429.v2
title: Long-term trends in inflowing chlorophyll <i>a</i> and nutrients and their relation to dissolved oxygen in a large western reservoir

description: Naymik J, Larsen CA, Myers R, Hoovestol C, Gastelecutto N, Bates D. 2023. Long-term trends in inflowing chlorophyll <i>a</i> and nutrients and their relation to dissolved oxygen in a large western reservoir. Lake Reserv Manage. 39:53–71. Anoxia in Brownlee Reservoir is one of the numerous water quality issues associated with eutrophic conditions in the Snake River as it flows through southern Idaho and parts of eastern Oregon. The states of Idaho and Oregon have developed total maximum daily loads (TMDLs) for multiple reaches of the Snake River and its tributaries upstream of Brownlee Reservoir intended to address poor water quality. Despite the emphasis on developing TMDLs throughout the Snake River and its tributaries, published long-term trend monitoring to evaluate the results of the TMDLs is lacking. Trends in Snake River concentrations and loads summarized using weighted regressions on time, discharge, and season show that combined efforts to improve water quality upstream of Brownlee Reservoir have realized decreasing trends in concentrations of chlorophyll <i>a</i>, total phosphorus, and suspended solids (80%, 46%, and 61% reductions, respectively) from 1995 to 2021. Brownlee Reservoir, a large mainstem reservoir with short residence time, has responded quickly to inflowing reductions of chlorophyll <i>a</i> and total phosphorus. Since 2005, dissolved oxygen (DO) has improved in the reservoir, with a 33% reduction in the volume of the reservoir having DO less than 1 mg/L. This supports the primary premise of upstream TMDLs and demonstrates that inflowing water quality improvements are effective at improving in-reservoir dissolved oxygen.
DOI: 10.7294/22354063
title: Idaho Results of the Wildlife Viewer Survey

description: Through a 2021 AFWA MultiState Conservation Grant, Virginia Tech and the AFWA Wildlife Viewing and Nature Tourism Working Group conducted national and state level surveys to gather more data on wildlife viewers. This dataset is from the survey conducted in Idaho. It contains: 1. Idaho Wildlife Viewer Survey.pdf: a pdf version of the survey instrument 2. Idaho_WildlifeViewerSurvey.csv: a csv (comma-separated values) file of the dataset 3. Idaho_WildlifeViewerSurvey.sav: a sav (compatible with SPSS, the Statistical Package for Social Science) file of the dataset 4. WildlifeViewerSurveyData_VariableGuide: a guide to each variable name in the datasets
DOI: 10.7294/22354063.v1
title: Idaho Results of the Wildlife Viewer Survey

description: Through a 2021 AFWA MultiState Conservation Grant, Virginia Tech and the AFWA Wildlife Viewing and Nature Tourism Working Group conducted national and state level surveys to gather more data on wildlife viewers. This dataset is from the survey conducted in Idaho. It contains: 1. Idaho Wildlife Viewer Survey.pdf: a pdf version of the survey instrument 2. Idaho_WildlifeViewerSurvey.csv: a csv (comma-separated values) file of the dataset 3. Idaho_WildlifeViewerSurvey.sav: a sav (compatible with SPSS, the Statistical Package for Social Science) file of the dataset 4. WildlifeViewerSurveyData_VariableGuide: a guide to each variable name in the datasets
DOI: 10.5281/zenodo.7299516
title: 4i dataset for "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps"

description: 4i dataset described and analysed in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps" (biorxiv). Analysis of this dataset was done with CAMPA. The data is in CAMPA-readable format; for more information on the data format, see the CAMPA documentation . The data contains preprocessed wells of 184A1 and HeLa cells with different chemical treatments and 43 channels. For more information on the data, see the CAMPA reproducibility repository.
DOI: 10.5281/zenodo.7299515
title: 4i dataset for "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps"

description: 4i dataset described and analysed in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps" (biorxiv). Analysis of this dataset was done with CAMPA. The data is in CAMPA-readable format; for more information on the data format, see the CAMPA documentation . The data contains preprocessed wells of 184A1 and HeLa cells with different chemical treatments and 43 channels. For more information on the data, see the CAMPA reproducibility repository.
DOI: 10.5281/zenodo.7299750
title: Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps"

description: Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps" (biorxiv) of 4i data. Analysis of this dataset was done with CAMPA, and all scripts to reproduce these results are available in the CAMPA reproducibility repository.
DOI: 10.5281/zenodo.7299749
title: Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps"

description: Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps" (biorxiv) of 4i data. Analysis of this dataset was done with CAMPA, and all scripts to reproduce these results are available in the CAMPA reproducibility repository.
DOI: 10.5281/zenodo.7697373
title: DeliCS Training+Validation Data - SPI-TGAS-MRF+GRE

description: This data set consists of raw MRI k-space data from 12 healthy volunteers. The data were acquired on a 3T Premier MRI scanner (GE Healthcare, Waukesha, WI) with a 48-channel head receiver-coil. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>): The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats) GRE (files named <strong>raw_gre.npy</strong>): A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64)) Noise estimation (files named <strong>noise.npy</strong>): Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points) To run the processing pipeline presented in [1], please follow the instructions on https://github.com/SetsompopLab/deli-cs and download Zenodo datasets 10.5281/zenodo.7734431 and 10.5281/zenodo.7703200 with testing data and meta data needed for the reconstruction pipeline. [1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em>https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1 [2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:10.1002/mrm.29194
description: {"references": ["Cao, X,\u00a0\u00a0Liao, C,\u00a0\u00a0Iyer, SS, et al.\u00a0\u00a0Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging.\u00a0Magn Reson Med.\u00a02022;\u00a088:\u00a0133-\u00a0150. doi:10.1002/mrm.29194", "Iyer S, Schauman S, Sandino C, et al.\u00a0Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.\u00a0BioRxiv:\u00a0https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1"]}
DOI: 10.5281/zenodo.7697372
title: DeliCS Training+Validation Data - SPI-TGAS-MRF+GRE

description: This data set consists of raw MRI k-space data from 12 healthy volunteers. The data were acquired on a 3T Premier MRI scanner (GE Healthcare, Waukesha, WI) with a 48-channel head receiver-coil. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>): The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats) GRE (files named <strong>raw_gre.npy</strong>): A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64)) Noise estimation (files named <strong>noise.npy</strong>): Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points) To run the processing pipeline presented in [1], please follow the instructions on https://github.com/SetsompopLab/deli-cs and download Zenodo datasets 10.5281/zenodo.7734431 and 10.5281/zenodo.7703200 with testing data and meta data needed for the reconstruction pipeline. [1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em>https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1 [2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:10.1002/mrm.29194
description: {"references": ["Cao, X,\u00a0\u00a0Liao, C,\u00a0\u00a0Iyer, SS, et al.\u00a0\u00a0Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging.\u00a0Magn Reson Med.\u00a02022;\u00a088:\u00a0133-\u00a0150. doi:10.1002/mrm.29194", "Iyer S, Schauman S, Sandino C, et al.\u00a0Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.\u00a0BioRxiv:\u00a0https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1"]}
DOI: 10.5281/zenodo.7703200
title: DeliCS Testing Data + DL Checkpoints - SPI-TGAS-MRF+GRE

description: This data set consists of raw MRI k-space data from 3 healthy volunteers (train_case000, test_case000, and test_case001) and 3 patients (test_case002, test_case003, and test_case004). The data were acquired on a 3T Premier MRI scanners (GE Healthcare, Waukesha, WI) with 48-channel head receiver-coils. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. Each case tarball contains three files: <strong>raw_mrf.npy, gre_mrf.npy, noise.npy</strong> SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>): The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats) GRE (files named <strong>raw_gre.npy</strong>): A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64)) Noise estimation (files named <strong>noise.npy</strong>): Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points) Finally, <strong>checkpoints.tar.gz</strong> contains the pre-trained weights used for the deliCS network. [1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em>https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1 [2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:10.1002/mrm.29194
description: {"references": ["Cao, X,\u00a0\u00a0Liao, C,\u00a0\u00a0Iyer, SS, et al.\u00a0\u00a0Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging.\u00a0Magn Reson Med.\u00a02022;\u00a088:\u00a0133-\u00a0150. doi:10.1002/mrm.29194", "Iyer S, Schauman S, Sandino C, et al.\u00a0Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.\u00a0BioRxiv:\u00a0https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1"]}
DOI: 10.5281/zenodo.7703199
title: DeliCS Testing Data + DL Checkpoints - SPI-TGAS-MRF+GRE

description: This data set consists of raw MRI k-space data from 3 healthy volunteers (train_case000, test_case000, and test_case001) and 3 patients (test_case002, test_case003, and test_case004). The data were acquired on a 3T Premier MRI scanners (GE Healthcare, Waukesha, WI) with 48-channel head receiver-coils. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. Each case tarball contains three files: <strong>raw_mrf.npy, gre_mrf.npy, noise.npy</strong> SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>): The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats) GRE (files named <strong>raw_gre.npy</strong>): A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64)) Noise estimation (files named <strong>noise.npy</strong>): Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points) Finally, <strong>checkpoints.tar.gz</strong> contains the pre-trained weights used for the deliCS network. [1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em>https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1 [2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:10.1002/mrm.29194
description: {"references": ["Cao, X,\u00a0\u00a0Liao, C,\u00a0\u00a0Iyer, SS, et al.\u00a0\u00a0Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging.\u00a0Magn Reson Med.\u00a02022;\u00a088:\u00a0133-\u00a0150. doi:10.1002/mrm.29194", "Iyer S, Schauman S, Sandino C, et al.\u00a0Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.\u00a0BioRxiv:\u00a0https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1"]}
DOI: 10.5281/zenodo.7734431
title: DeliCS Preprocessed Data

description: This data set consists of pre-processed MRI data as presented in <em>Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction </em>[1]. By downloading this dataset you will be able to re-create the figures presented in the paper using the code available on: https://github.com/SetsompopLab/deli-cs . Each tarball named <strong>caseXXX_preprocessed.tar.gz</strong> contains data related to that subject: <strong>deli_2min.npy</strong> is the DL genrated initial reconstruction. <strong>init_adj_2min.npy</strong> is the inital gridding reconstructions. <strong>ref_2min.npy</strong> is the reference LLR reconstruction (not initialized with deliCS). <strong>ref_6min.npy</strong> is the reference LLR reconstruction using 6 min of MRF acquisition. This is considered gold standard - NOT AVAILABLE FOR TEST CASES 002-004, which are acquired in the clinic. <strong>refine_2min_iters_20.npy </strong>is the reconstruction from the full proposed deliCS pipeline. <strong>T1... .npy </strong>are T1 maps from various matching reconstructions <strong>T2... .npy </strong>are T2 maps from various matching reconstructions Additionally, the tarball named <strong>bartcompare.tar.gz </strong>contains the <strong>ref_2min.npy </strong>density compensated Sigpy reconstruction along with <strong>bartrecon_2min.cfl </strong>and <strong>bartrecon_2min.hdr</strong>, which are the non-density compensated Bart reconstructions shown in figure 3 in [1]. Furthermore, meta-data needed to process the data as presented in [1] are included. Some of the figure generation code requires the subspace basis and dictionary to perform dictionary matching on the fly. The tarball <strong>shared.tar.gz</strong> contains: the k-space trajectory for 2 min data (<strong>traj_grp16_inacc2.mat</strong>) the k-space trajectory for 6 min data (<strong>traj_grp48_inacc1.mat</strong>) the density compensation function for each trajectory (<strong>dcf_2min.npy</strong> and <strong>dcf_6min.npy</strong>) the subspace basis (<strong>phi.mat</strong>) the dictionary (<strong>dictionary.mat</strong>) a scaling factor for the deli reconstruction (<strong>deli_scaling_2min.npy</strong>) [1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em>https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1
description: {"references": ["Iyer S, Schauman S, Sandino C, et al.\u00a0Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.\u00a0BioRxiv:\u00a0https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1"]}