cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20217755
title: Protecting biodiversity to support ecosystem services: An analysis of tradeoffs and synergies in southwestern China

description: 1. Biodiversity plays a key role in supporting ecosystem services. Understanding this relationship sheds light on the linkages between ecological structure and functioning, thereby increasing the capacity for effective strategies of ecosystem conservation and restoration. 2. Focusing on southwestern China, a global biodiversity hotspot, we evaluated the spatial correlations between biodiversity and four regulating services and one cultural service. We calculated critical points of change in the relationship between biodiversity and each of these ecosystem services and categorized the region into “threshold”, “sensitive”, “saturation”, “recession”, and “low-value” areas. 3. The results show that the spatial correlation between biodiversity and regulating services was mainly positive in the Nujiang River and Lancang River basins and the Dayao Mountains, while biodiversity was negatively correlated with provisioning and cultural services in the Qionglai and Hengduan Mountains. The slope value of the curve fitted for the biodiversity-ecosystem services relationship was approximately 1 when species richness was approximately 50; the corresponding areas were termed threshold areas and were pivotal zones of change in the biodiversity-ecosystem services relationship. These areas were mainly distributed in the Qionglai Mountains, Hengduan Mountains, and the Nujinag River Basin, and should be prioritized for monitoring and biodiversity protection. 4. Synthesis and applications: our findings can help improve understanding of the spatially complex interactions between biodiversity and multiple ecosystem services, as well as identifying areas that should be prioritized for protection and restoration; they also contribute towards a scientific framework for integrated management and policy in general.
DOI: 10.6084/m9.figshare.20218715
title: Effect of Mass Sports Activity on Prosocial Behavior A Sequential Mediation Model of Flow Trait and Subjective Well-Being

description: <strong>Original data</strong>
DOI: 10.23728/b2share.33bc26f6544b40909a7a44b824229415
title: Underlying data_Lab-scale and on-field industrial composting of biodegradable plastic blends for packaging_Chong et al. 2022

description: Underlying data_Lab-scale and on-field industrial composting of biodegradable plastic blends for packaging_Chong et al. 2022 (in submission)
DOI: 10.5281/zenodo.6791874
title: The pan-genome unearths gene content and transposable element variations in modern pigs

description: Genes, gene annotations, proteins, and sequences identified in the non-reference genome of the pig pan-genome. Transposable insertion polymorphisms (TIP) indentified in the pig mobolome.
DOI: 10.5281/zenodo.6791735
title: The pan-genome unearths gene content and transposable element variations in modern pigs

description: Genes, gene annotations, proteins, and sequences identified in the non-reference genome of the pig pan-genome. Transposable insertion polymorphisms (TIP) indentified in the pig mobolome.
DOI: 10.5281/zenodo.6791966
title: Compositional Data Analysis (CoDA) of Clinopyroxene from Abyssal Peridotites

description: Additional supporting information includes data, R script, and QGIS file supporting the main text: <strong>CSV (Data Set)</strong> residual_abyssal_peridotites.csv: Compilations of residual abyssal peridotites (n = 1162) and depleted MORB-mantle (n = 1) residual_abyssal_peridotites_coda_results.csv: Filtered data and results of PCA and k-means clustering (n = 267) model_cpx.csv: Clinopyroxene compositions obtained by open-system melting model test.csv: csv file for testing new data<br> <br> <strong>R</strong> abyssal_cpx_pca.Rproj coda.R: R script implemented in this study test_your_data.R: R script to test new data comparing to abyssal clinopyroxenes and modeled clinopyroxenes <strong>QGIS</strong> residual_abyssal_peridotites.qgz: QGIS using residual_abyssal_peridotites.csv and residual_abyssal_peridotites_coda_results.csv for Figure 1 and Figure S7 color_etopo1_ice_low_modified.tiff: ETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry from NOAA 2. Instruction We prepared an R script to compare new (your) clinopyroxene data with clinopyroxene from abyssal peridotites. New data will be plotted using the principal components derived from the natural clinopyroxene database presented in this paper. The procedure is as follows: 1. add data below the second row in test.csv<br> * Do not change the file name<br> * Do not change the first row<br> * Add clinopyroxene data (10 elements) and its label replacing under 2nd row * Label of data can be sample name, lithology, locality etc. 2. Open abyssal_cpx_pca.Rproj by R studio (double click) 3. Open test_your_data.R (double click)<br> 4. Implement test_your_data.R. To use test_your_data.R, first press cmd+A (ctrl+A) and press Run/cmd+enter (ctrl+enter). 5. Results files<br> 5-1. abyssalcpx_vs_test.csv: PC1&PC2 values using abyssal clinopyroxene PC coordinates<br> 5-2. plot1.pdf: abyssal clinopyroxene (cluster) vs. test data plot<br> 5-3. plot2.pdf: modeled clinopyroxene vs. test data plot<br> 5-4. spider_cl1.pdf: PM-normalized trace elements patterns of cluster 1 from abyssal peridotites 5-5. spider_cl2.pdf: PM-normalized trace elements patterns of cluster 2 from abyssal peridotites 5-6. spider_cl3.pdf: PM-normalized trace elements patterns of cluster 3 from abyssal peridotites 5-7. spider_cl4.pdf: PM-normalized trace elements patterns of cluster 4 from abyssal peridotites 5-8. spider_test.pdf: PM-normalized trace elements patterns of new data (your data)<br> 5-9. plot3.pdf: Discrimination diagram for clinopyroxene trace elements compositions. PM normalized Sr/Nd ratio vs. Ce/Yb ratio of clinopyroxenes from abyssal peridotites vs. test data
DOI: 10.5281/zenodo.6791965
title: Compositional Data Analysis (CoDA) of Clinopyroxene from Abyssal Peridotites

description: Additional supporting information includes data, R script, and QGIS file supporting the main text: <strong>CSV (Data Set)</strong> residual_abyssal_peridotites.csv: Compilations of residual abyssal peridotites (n = 1162) and depleted MORB-mantle (n = 1) residual_abyssal_peridotites_coda_results.csv: Filtered data and results of PCA and k-means clustering (n = 267) model_cpx.csv: Clinopyroxene compositions obtained by open-system melting model test.csv: csv file for testing new data<br> <br> <strong>R</strong> abyssal_cpx_pca.Rproj coda.R: R script implemented in this study test_your_data.R: R script to test new data comparing to abyssal clinopyroxenes and modeled clinopyroxenes <strong>QGIS</strong> residual_abyssal_peridotites.qgz: QGIS using residual_abyssal_peridotites.csv and residual_abyssal_peridotites_coda_results.csv for Figure 1 and Figure S7 color_etopo1_ice_low_modified.tiff: ETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry from NOAA 2. Instruction We prepared an R script to compare new (your) clinopyroxene data with clinopyroxene from abyssal peridotites. New data will be plotted using the principal components derived from the natural clinopyroxene database presented in this paper. The procedure is as follows: 1. add data below the second row in test.csv<br> * Do not change the file name<br> * Do not change the first row<br> * Add clinopyroxene data (10 elements) and its label replacing under 2nd row * Label of data can be sample name, lithology, locality etc. 2. Open abyssal_cpx_pca.Rproj by R studio (double click) 3. Open test_your_data.R (double click)<br> 4. Implement test_your_data.R. To use test_your_data.R, first press cmd+A (ctrl+A) and press Run/cmd+enter (ctrl+enter). 5. Results files<br> 5-1. abyssalcpx_vs_test.csv: PC1&PC2 values using abyssal clinopyroxene PC coordinates<br> 5-2. plot1.pdf: abyssal clinopyroxene (cluster) vs. test data plot<br> 5-3. plot2.pdf: modeled clinopyroxene vs. test data plot<br> 5-4. spider_cl1.pdf: PM-normalized trace elements patterns of cluster 1 from abyssal peridotites 5-5. spider_cl2.pdf: PM-normalized trace elements patterns of cluster 2 from abyssal peridotites 5-6. spider_cl3.pdf: PM-normalized trace elements patterns of cluster 3 from abyssal peridotites 5-7. spider_cl4.pdf: PM-normalized trace elements patterns of cluster 4 from abyssal peridotites 5-8. spider_test.pdf: PM-normalized trace elements patterns of new data (your data)<br> 5-9. plot3.pdf: Discrimination diagram for clinopyroxene trace elements compositions. PM normalized Sr/Nd ratio vs. Ce/Yb ratio of clinopyroxenes from abyssal peridotites vs. test data
DOI: 10.5519/qd.dexaeuf2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5165775 records)
DOI: 10.5281/zenodo.6791978
title: Compositional and Thermal State of the Lower Mantle from the inversion of GLAD-M25 model

description: The 3D chemical composition and temperature structure of the lower mantle from the inversion of GLAD-M25 model is included
DOI: 10.5281/zenodo.6791977
title: Compositional and Thermal State of the Lower Mantle from the inversion of GLAD-M25 model

description: The 3D chemical composition and temperature structure of the lower mantle from the inversion of GLAD-M25 model is included
DOI: 10.6084/m9.figshare.19959812
title: Host-treeselectionbytheantgarden-initiatingarborealponerineNeoponeragoeldi

description: We show that the ant garden ant Neoponera goeldii had the hability to select its host tree
DOI: 10.6084/m9.figshare.19959812.v4
title: Host-treeselectionbytheantgarden-initiatingarborealponerineNeoponeragoeldi

description: We show that the ant garden ant Neoponera goeldii had the hability to select its host tree
DOI: 10.6084/m9.figshare.19959812.v3
title: Host-treeselectionbytheantgarden-initiatingarborealponerineNeoponeragoeldi

description: We show that the ant garden ant Neoponera goeldii had the hability to select its host tree
DOI: 10.34740/kaggle/dsv/3896270
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.34740/kaggle/dsv/3896286
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.20219450
title: Polyp-canal reconstruction reveals evolution towards complexity in corals (Dataset Part 85)

description: There are 17 files in this dataset. This is the 85th part of this dataset. The <em>A. millepora</em>, <em>A. muricata</em>,<em> C. vaughani</em>,<em> M. capricornis</em>, <em>M. foliosa</em>,<em> P. damicornis</em>, <em>P. meandrina</em>,<em> P. verrucosa</em>, and<em> S. pistillata</em> samples were collected from the Xisha Islands (2018); the <em>A. digitifera</em>, <em>F. speciosa </em>(2008), and<em> M. turgescens</em> (2017) samples were collected from the Nansha Islands; the <em>P. lutea</em> samples were collected from the Weizhou Islands (2017); the <em>G. fascicularis</em>, and<em> M. stellata </em>samples were collected from the Gaven Reef (2017); the <em>A. myriophthalma</em> samples were collected from the Reed Tablemount (2017); the <em>P. sinensis</em> samples were collected from the Subi Reef (2017). All samples, which occurred in large arborescent colonies forming thickets, were found in tropical shallow reefs of the marine neritic zone, from depths of about 5 to 10 m. The daily mean temperature was between 23.2 and 29.2°C. The coral samples were kept whole and housed in our laboratory coral tank, where all conditions were simulated to reflect those of their habitat in the South China Sea. These samples were kept in the tank for about one to three months before HRCT analysis.
DOI: 10.6084/m9.figshare.20219450.v1
title: Polyp-canal reconstruction reveals evolution towards complexity in corals (Dataset Part 85)

description: There are 17 files in this dataset. This is the 85th part of this dataset. The <em>A. millepora</em>, <em>A. muricata</em>,<em> C. vaughani</em>,<em> M. capricornis</em>, <em>M. foliosa</em>,<em> P. damicornis</em>, <em>P. meandrina</em>,<em> P. verrucosa</em>, and<em> S. pistillata</em> samples were collected from the Xisha Islands (2018); the <em>A. digitifera</em>, <em>F. speciosa </em>(2008), and<em> M. turgescens</em> (2017) samples were collected from the Nansha Islands; the <em>P. lutea</em> samples were collected from the Weizhou Islands (2017); the <em>G. fascicularis</em>, and<em> M. stellata </em>samples were collected from the Gaven Reef (2017); the <em>A. myriophthalma</em> samples were collected from the Reed Tablemount (2017); the <em>P. sinensis</em> samples were collected from the Subi Reef (2017). All samples, which occurred in large arborescent colonies forming thickets, were found in tropical shallow reefs of the marine neritic zone, from depths of about 5 to 10 m. The daily mean temperature was between 23.2 and 29.2°C. The coral samples were kept whole and housed in our laboratory coral tank, where all conditions were simulated to reflect those of their habitat in the South China Sea. These samples were kept in the tank for about one to three months before HRCT analysis.
DOI: 10.6084/m9.figshare.14301276
title: Cue responsiveness as a measure of emerging language ability in aphasia

description: Prior research suggests that initial aphasia severity, lesion size, and lesion location are the most salient factors in predicting recovery outcomes. While these factors provide important prognostic information, information that is individualized and readily available to clinicians is limited. Deficits in naming are common to all aphasia types and are routinely targeted in aphasia assessment and treatment, with cues provided to facilitate lexical retrieval. In this study, we examine aphasia recovery factors that are readily available to clinicians, examining whether a person’s ability to improve naming with cues, indicating “stimulability,” will be predictive of future word retrieval. Ten participants with aphasia following a left-hemisphere stroke participated in initial assessment, seven of whom met criteria for longitudinal assessment. Stroke and early clinical recovery data were collected for all participants. At four timepoints over one year we evaluated longitudinal participants’ naming ability and measured the proportion of successful lexical retrieval with the presentation of phonemic, feature, and sentence cues. For all participants, multiple descriptive factors regarding recovery, including lesion information, information from the acute inpatient timeframe, and communication opportunities, were examined. For individuals followed longitudinally, naming stimulability did not consistently predict naming accuracy at the subsequent assessment timepoint. Individuals’ attempts at naming emerged as a metric related to future naming performance warranting further evaluation. Multiple factors related to recovery must be considered when providing prognostic information. Naming stimulability and attempts at naming provide some information regarding future performance, but are not consistently reliable across timepoints.
DOI: 10.6084/m9.figshare.14301276.v1
title: Cue responsiveness as a measure of emerging language ability in aphasia

description: Prior research suggests that initial aphasia severity, lesion size, and lesion location are the most salient factors in predicting recovery outcomes. While these factors provide important prognostic information, information that is individualized and readily available to clinicians is limited. Deficits in naming are common to all aphasia types and are routinely targeted in aphasia assessment and treatment, with cues provided to facilitate lexical retrieval. In this study, we examine aphasia recovery factors that are readily available to clinicians, examining whether a person’s ability to improve naming with cues, indicating “stimulability,” will be predictive of future word retrieval. Ten participants with aphasia following a left-hemisphere stroke participated in initial assessment, seven of whom met criteria for longitudinal assessment. Stroke and early clinical recovery data were collected for all participants. At four timepoints over one year we evaluated longitudinal participants’ naming ability and measured the proportion of successful lexical retrieval with the presentation of phonemic, feature, and sentence cues. For all participants, multiple descriptive factors regarding recovery, including lesion information, information from the acute inpatient timeframe, and communication opportunities, were examined. For individuals followed longitudinally, naming stimulability did not consistently predict naming accuracy at the subsequent assessment timepoint. Individuals’ attempts at naming emerged as a metric related to future naming performance warranting further evaluation. Multiple factors related to recovery must be considered when providing prognostic information. Naming stimulability and attempts at naming provide some information regarding future performance, but are not consistently reliable across timepoints.
DOI: 10.34740/kaggle/dsv/3896445
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.6792072
title: Zenodo package for Mao et al. AAS journal submitted

description: The supplementary materials here contain data and python3 notebooks to reproduce figures in the paper submitted by Mao et al. to the AAS journal. To run the notebooks via Anaconda 3<br> Step 1: Download and install Anaconda 3 on your computer. Once you are under the Anaconda 3 environment, you can continue with this tutorial. Step 2: Download and unpack the tarball from Zenodo. <pre><code>tar -xvf handhelike-zenodo.tar.gz</code></pre> Step 3: Install the dependencies (incl. numpy, scipy, etc.) via pip or conda if required <pre><code>cd zenodo pip install -r requirements.txt conda install --yes --file requirements.txt</code></pre> Step 4: Run the python notebooks. <pre><code>cd pynb python run_plot_cfcsd.py</code></pre>
DOI: 10.5281/zenodo.6792071
title: Zenodo package for Mao et al. AAS journal submitted

description: The supplementary materials here contain data and python3 notebooks to reproduce figures in the paper submitted by Mao et al. to the AAS journal. To run the notebooks via Anaconda 3<br> Step 1: Download and install Anaconda 3 on your computer. Once you are under the Anaconda 3 environment, you can continue with this tutorial. Step 2: Download and unpack the tarball from Zenodo. <pre><code>tar -xvf handhelike-zenodo.tar.gz</code></pre> Step 3: Install the dependencies (incl. numpy, scipy, etc.) via pip or conda if required <pre><code>cd zenodo pip install -r requirements.txt conda install --yes --file requirements.txt</code></pre> Step 4: Run the python notebooks. <pre><code>cd pynb python run_plot_cfcsd.py</code></pre>
DOI: 10.6084/m9.figshare.20219474
title: Polyp-canal reconstruction reveals evolution towards complexity in corals (Dataset Part 93)

description: There are 17 files in this dataset. This is the 93rd part of this dataset. The <em>A. millepora</em>, <em>A. muricata</em>,<em> C. vaughani</em>,<em> M. capricornis</em>, <em>M. foliosa</em>,<em> P. damicornis</em>, <em>P. meandrina</em>,<em> P. verrucosa</em>, and<em> S. pistillata</em> samples were collected from the Xisha Islands (2018); the <em>A. digitifera</em>, <em>F. speciosa </em>(2008), and<em> M. turgescens</em> (2017) samples were collected from the Nansha Islands; the <em>P. lutea</em> samples were collected from the Weizhou Islands (2017); the <em>G. fascicularis</em>, and<em> M. stellata </em>samples were collected from the Gaven Reef (2017); the <em>A. myriophthalma</em> samples were collected from the Reed Tablemount (2017); the <em>P. sinensis</em> samples were collected from the Subi Reef (2017). All samples, which occurred in large arborescent colonies forming thickets, were found in tropical shallow reefs of the marine neritic zone, from depths of about 5 to 10 m. The daily mean temperature was between 23.2 and 29.2°C. The coral samples were kept whole and housed in our laboratory coral tank, where all conditions were simulated to reflect those of their habitat in the South China Sea. These samples were kept in the tank for about one to three months before HRCT analysis.
DOI: 10.6084/m9.figshare.20219474.v1
title: Polyp-canal reconstruction reveals evolution towards complexity in corals (Dataset Part 93)

description: There are 17 files in this dataset. This is the 93rd part of this dataset. The <em>A. millepora</em>, <em>A. muricata</em>,<em> C. vaughani</em>,<em> M. capricornis</em>, <em>M. foliosa</em>,<em> P. damicornis</em>, <em>P. meandrina</em>,<em> P. verrucosa</em>, and<em> S. pistillata</em> samples were collected from the Xisha Islands (2018); the <em>A. digitifera</em>, <em>F. speciosa </em>(2008), and<em> M. turgescens</em> (2017) samples were collected from the Nansha Islands; the <em>P. lutea</em> samples were collected from the Weizhou Islands (2017); the <em>G. fascicularis</em>, and<em> M. stellata </em>samples were collected from the Gaven Reef (2017); the <em>A. myriophthalma</em> samples were collected from the Reed Tablemount (2017); the <em>P. sinensis</em> samples were collected from the Subi Reef (2017). All samples, which occurred in large arborescent colonies forming thickets, were found in tropical shallow reefs of the marine neritic zone, from depths of about 5 to 10 m. The daily mean temperature was between 23.2 and 29.2°C. The coral samples were kept whole and housed in our laboratory coral tank, where all conditions were simulated to reflect those of their habitat in the South China Sea. These samples were kept in the tank for about one to three months before HRCT analysis.
DOI: 10.5281/zenodo.6792125
title: Buteo lineatus decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6792124
title: Buteo lineatus decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6792137
title: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks

description: <pre>The data are part of the doctoral work entitled: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks.</pre>
DOI: 10.5281/zenodo.6792136
title: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks

description: <pre>The data are part of the doctoral work entitled: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks.</pre>
DOI: 10.15151/esrf-es-818497596
title: BAG Montpellier

description: The BAG of Montpellier ...
DOI: 10.5281/zenodo.6789910
title: Closed

description: a) Glymphatic_testSET – Brain MRI T-SLIP test data (DICOMs) for the glymphatic flow quantification. b) DataAnonymized – per subject fit results from the study conducted @ UC San Diego.
DOI: 10.5281/zenodo.6789863
title: Closed

description: a) Glymphatic_testSET – Brain MRI T-SLIP test data (DICOMs) for the glymphatic flow quantification. b) DataAnonymized – per subject fit results from the study conducted @ UC San Diego.