cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17263439.v1
title: Supplementary Material for: Endurance exercise training-attenuated diabetic kidney disease with muscle weakness in spontaneously diabetic Torii fatty rats.

description: Abstract Background The aim of this study was to evaluate protective effects of endurance exercise training against diabetic kidney disease (DKD) with muscle weakness by using male spontaneously diabetic Torii (SDT) fatty rats as type 2 diabetic animal models with obesity, hypertension, and hyperlipidemia. Methods Eight-week-old SDT fatty rats (n = 12) and Sprague–Dawley (SD) rats (n = 10) were randomly divided into exercise (Ex; SDT-Ex: n = 6, SD-Ex: n = 5) and sedentary groups (SDT-Cont: n = 6, SD-Cont: n = 5), respectively. Each group underwent regular treadmill exercise four times a week from ages 8 to 16 weeks. Results The exercise attenuated hypertension and hyperlipidemia and prevented increases in renal parameter levels without affecting blood glucose levels. In the SDT fatty rats, it prevented induction of renal morphological abnormalities in the interstitium of the superficial and intermediate layers of the cortex. Downregulated expression of endothelial nitric oxide synthase in the glomerulus of the SDT fatty rats was significantly upregulated by the exercise. The exercise upregulated the renal expressions of both medium-chain acyl-CoA dehydrogenase and peroxisome proliferator-activated receptor γ coactivator-1α related to fatty acid metabolism. It increased muscle strength and both muscle weight and cross-sectional area of type IIb muscle fibers in the extensor digitorum longus muscle in the SDT fatty rats. Conclusion Endurance exercise training in type 2 diabetes ameliorates DKD by improving endothelial abnormality and enhancing fatty acid metabolism in addition to attenuated hypertension, hyperlipidemia, and muscle weakness independently of blood glucose levels.
DOI: 10.6084/m9.figshare.17263439
title: Supplementary Material for: Endurance exercise training-attenuated diabetic kidney disease with muscle weakness in spontaneously diabetic Torii fatty rats.

description: Abstract Background The aim of this study was to evaluate protective effects of endurance exercise training against diabetic kidney disease (DKD) with muscle weakness by using male spontaneously diabetic Torii (SDT) fatty rats as type 2 diabetic animal models with obesity, hypertension, and hyperlipidemia. Methods Eight-week-old SDT fatty rats (n = 12) and Sprague–Dawley (SD) rats (n = 10) were randomly divided into exercise (Ex; SDT-Ex: n = 6, SD-Ex: n = 5) and sedentary groups (SDT-Cont: n = 6, SD-Cont: n = 5), respectively. Each group underwent regular treadmill exercise four times a week from ages 8 to 16 weeks. Results The exercise attenuated hypertension and hyperlipidemia and prevented increases in renal parameter levels without affecting blood glucose levels. In the SDT fatty rats, it prevented induction of renal morphological abnormalities in the interstitium of the superficial and intermediate layers of the cortex. Downregulated expression of endothelial nitric oxide synthase in the glomerulus of the SDT fatty rats was significantly upregulated by the exercise. The exercise upregulated the renal expressions of both medium-chain acyl-CoA dehydrogenase and peroxisome proliferator-activated receptor γ coactivator-1α related to fatty acid metabolism. It increased muscle strength and both muscle weight and cross-sectional area of type IIb muscle fibers in the extensor digitorum longus muscle in the SDT fatty rats. Conclusion Endurance exercise training in type 2 diabetes ameliorates DKD by improving endothelial abnormality and enhancing fatty acid metabolism in addition to attenuated hypertension, hyperlipidemia, and muscle weakness independently of blood glucose levels.
DOI: 10.5517/ccdc.csd.cc23rrd1
title: CCDC 1959243: Experimental Crystal Structure Determination

description: Related Article: Manzoor Ahmad Malik, Md Kausar Raza, Arif Mohammed, Mohmmad Younus Wani, Abdullah Saad Al-Bogami, Athar Adil Hashmi|2021|RSC Advances|11|39349|doi:10.1039/D1RA06227A
DOI: 10.6084/m9.figshare.17263433
title: Soil Moisture.xlsx

description: Soil moisture measurements recorded by TDR 150 soil moisture meter.
DOI: 10.6084/m9.figshare.17263436
title: Stem Height and Bud Number per Stem

description: Stem height (cm) and number of buds per stem by treatment
DOI: 10.6084/m9.figshare.17263436.v1
title: Stem Height and Bud Number per Stem

description: Stem height (cm) and number of buds per stem by treatment
DOI: 10.6084/m9.figshare.17263430
title: Flower and Fruit Counts per Stem

description: Number of flowers and fruit (green, red, and blue) per stem over time by treatment.
DOI: 10.6084/m9.figshare.17263430.v1
title: Flower and Fruit Counts per Stem

description: Number of flowers and fruit (green, red, and blue) per stem over time by treatment.
DOI: 10.6084/m9.figshare.17263427.v1
title: Wild Blueberry Harvest

description: Total yields by plot as well as measures of berry size and sugar content indicated by Brix analysis.
DOI: 10.6084/m9.figshare.17263427
title: Wild Blueberry Harvest

description: Total yields by plot as well as measures of berry size and sugar content indicated by Brix analysis.
DOI: 10.5281/zenodo.5789166
title: Thermal emission spectra of terrestrial alkaline rocks

description: These thermal emission spectra were collected at Arizona State University. The samples and spectral collection methods are described in: Dunn, T. L., McSween, H. Y., & Christensen, P. R. (2007). Thermal emission spectra of terrestrial alkaline volcanic rocks: Applications to Martian remote sensing. Journal of Geophysical Research E: Planets, 112(5), 1–18. https://doi.org/10.1029/2006JE002766 This repository includes the original spectra as well as spectra that have had minor corrections for a negative spectral slope. The negative spectral slope can arise if sample temperatures change during spectral acquisition. Only two samples (49012, 49098) were slope-corrected. The plot shows the spectra in the *spectra.txt files. The first column in the data files (not the label files) is the wavenumber (cm-1) x-axis. The remaining columns are the emissivity spectra. The label files give the spectrum labels.
description: uploaded by Deanne Rogers with permission of Tasha Dunn
DOI: 10.5281/zenodo.5789165
title: Thermal emission spectra of terrestrial alkaline rocks

description: These thermal emission spectra were collected at Arizona State University. The samples and spectral collection methods are described in: Dunn, T. L., McSween, H. Y., & Christensen, P. R. (2007). Thermal emission spectra of terrestrial alkaline volcanic rocks: Applications to Martian remote sensing. Journal of Geophysical Research E: Planets, 112(5), 1–18. https://doi.org/10.1029/2006JE002766 This repository includes the original spectra as well as spectra that have had minor corrections for a negative spectral slope. The negative spectral slope can arise if sample temperatures change during spectral acquisition. Only two samples (49012, 49098) were slope-corrected. The plot shows the spectra in the *spectra.txt files. The first column in the data files (not the label files) is the wavenumber (cm-1) x-axis. The remaining columns are the emissivity spectra. The label files give the spectrum labels.
description: uploaded by Deanne Rogers with permission of Tasha Dunn
DOI: 10.6084/m9.figshare.17263466
title: Mulch % Moisture Content.xlsx

description: Moisture content of mulch materials before and after oven drying.
DOI: 10.6084/m9.figshare.17263466.v1
title: Mulch % Moisture Content.xlsx

description: Moisture content of mulch materials before and after oven drying.
DOI: 10.5281/zenodo.5789174
title: 12222

description: s
DOI: 10.5281/zenodo.5789173
title: 12222

description: s
DOI: 10.6084/m9.figshare.17263472
title: Mulch Particle Size Analysis.xlsx

description: Mulch particle size distribution as a result of soil sieve analysis
DOI: 10.6084/m9.figshare.17263472.v1
title: Mulch Particle Size Analysis.xlsx

description: Mulch particle size distribution as a result of soil sieve analysis
DOI: 10.6084/m9.figshare.17263469
title: Wild Blueberry Stem Density

description: Number of stems per quadrat.
DOI: 10.6084/m9.figshare.17263469.v1
title: Wild Blueberry Stem Density

description: Number of stems per quadrat.
DOI: 10.6084/m9.figshare.17263523
title: Soil Test Results After Harvest

description: Standard soil test analysis results of soil nutrients by plot after harvest at completion of study.
DOI: 10.6084/m9.figshare.17263523.v1
title: Soil Test Results After Harvest

description: Standard soil test analysis results of soil nutrients by plot after harvest at completion of study.
DOI: 10.5519/qd.69ybwtp7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (376992 records)
DOI: 10.5281/zenodo.5789268
title: Erythroxylum daphnites SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5789267
title: Erythroxylum daphnites SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5789333
title: Raw data used for the preparation of the article Sci. Rep. 9 (2019), 11328. DOI: 10.1038/s41598-019-47547-0.

description: Raw data (SEM, XRD, magnetotransport measurements) used for the preparation of the article G. Kunakova, R. Meija, J. Andzane, U. Malinovskis, G. Petersons, M. Baitimirova, M. Bechelany, T. Bauch, F. Lombardi, D. Erts ‘Surface structure promoted high-yield growth and magnetotransport properties of Bi2Se3 nanoribbons’, Sci. Rep. 9 (2019), 11328. DOI: 10.1038/s41598-019-47547-0.
DOI: 10.5281/zenodo.5789332
title: Raw data used for the preparation of the article Sci. Rep. 9 (2019), 11328. DOI: 10.1038/s41598-019-47547-0.

description: Raw data (SEM, XRD, magnetotransport measurements) used for the preparation of the article G. Kunakova, R. Meija, J. Andzane, U. Malinovskis, G. Petersons, M. Baitimirova, M. Bechelany, T. Bauch, F. Lombardi, D. Erts ‘Surface structure promoted high-yield growth and magnetotransport properties of Bi2Se3 nanoribbons’, Sci. Rep. 9 (2019), 11328. DOI: 10.1038/s41598-019-47547-0.
DOI: 10.6084/m9.figshare.17263556
title: Soil Test Reports Before Treatment Application

description: Standard soil test analysis results for each treatment area. All soil test reports included.
DOI: 10.6084/m9.figshare.17263556.v1
title: Soil Test Reports Before Treatment Application

description: Standard soil test analysis results for each treatment area. All soil test reports included.
DOI: 10.6084/m9.figshare.17263559
title: Chemical Analysis of Mulch Treatments

description: Analysis of each mulch treatment material and its composition.
DOI: 10.6084/m9.figshare.17263559.v1
title: Chemical Analysis of Mulch Treatments

description: Analysis of each mulch treatment material and its composition.
DOI: 10.5281/zenodo.5789318
title: Existing land uses constrain climate change mitigation potential of forest restoration in India

description: The datasets were developed as part of the publication "Existing land uses constrain climate change mitigation potential of forest restoration in India". Please refer to the manuscript for processing details.<br> <br> ForestBioclimaticEnvelop_ProjectionUTM is the bioclimatic envelope of forests developed. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system. FinalOpportunity_AfterExclusions_ProjectionUTM is the feasible area of opportunity, after all exclusions of land uses and covers that cannot naturally regenerate to forests. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.
DOI: 10.5281/zenodo.5780192
title: Existing land uses constrain climate change mitigation potential of forest restoration in India

description: The datasets were developed as part of the publication "Existing land uses constrain climate change mitigation potential of forest restoration in India". Please refer to the manuscript for processing details.<br> <br> ForestBioclimaticEnvelop_ProjectionUTM is the bioclimatic envelope of forests developed. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system. FinalOpportunity_AfterExclusions_ProjectionUTM is the feasible area of opportunity, after all exclusions of land uses and covers that cannot naturally regenerate to forests. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.
DOI: 10.3886/e147523
title: Code for: Consumption Response to Credit Expansions: Evidence from Experimental Assignment of 45,307 Credit Lines

description: STATA .do files used to produce the tables, figures, and statistics.
DOI: 10.3886/e147523v1
title: Code for: Consumption Response to Credit Expansions: Evidence from Experimental Assignment of 45,307 Credit Lines

description: STATA .do files used to produce the tables, figures, and statistics.
DOI: 10.25318/3210043901-eng
title: Paid agricultural work in the year prior to the census

description: Census of Agriculture, 2011 and 2016. Paid agricultural work in the year prior to the census.
DOI: 10.25318/3210043901-fra
title: Travail agricole rémunéré dans l'année précédant le recensement

description: Recensement de l'agriculture, 2011 et 2016. Travail agricole rémunéré dans l'année précédant le recensement.
DOI: 10.3886/e151521
title: Replication code for "College Tuitions and Income Inequality"

description: This is a data replication repository for "College Tuition and Income Inequality".
DOI: 10.3886/e151521v1
title: Replication code for "College Tuitions and Income Inequality"

description: This is a data replication repository for "College Tuition and Income Inequality".
DOI: 10.25405/data.ncl.17192867
title: Agrisys Tanzania Household Survey Dataset

description: <b>Milheiras_etal_Well-being_Dataset.csv</b><em>Household survey data collected by the Agrisys Tanzania research team in November and December 2019. Data collected in the northern region of Kilombero Valley, Tanzania.</em><em><br></em><b>Milheiras_etal_Well-being_Metadata.html</b><br><i>Description of household survey variables.</i><br><i><br></i><b>Agrisys HH Survey Consent Form.docx</b><br><i>English version of the consent form signed by household survey </i><i>participants</i><i>.</i><b><br></b><b>Agrisys HH Survey Participant Information Sheet.docx</b><br><i>English version of the information sheet given to household survey </i><i>participants</i><i>.</i>
DOI: 10.5281/zenodo.5789368
title: Incidence trends of childhood central nervous system tumors in Finland 1990–2017

description: Frequency tables of the dataset provided by the Finnish cancer registry and used for the submitted manuscript Incidence trends of childhood central nervous system tumors in Finland 1990–2017 by the above-mentioned authors.<br>
DOI: 10.5281/zenodo.5789367
title: Incidence trends of childhood central nervous system tumors in Finland 1990–2017

description: Frequency tables of the dataset provided by the Finnish cancer registry and used for the submitted manuscript Incidence trends of childhood central nervous system tumors in Finland 1990–2017 by the above-mentioned authors.<br>
DOI: 10.5281/zenodo.4468690
title: ABSOLUT input data for an example application on the districts of Germany

description: These are data for running the ABSOLUT R programs published separately on Zenodo: 10.5281/zenodo.4468608. The data published here consist of: <strong>absolutcontrol.dat</strong> – text file (UTF-8) with case-specific settings for program execution, may be edited by the user <strong>crop-areas.csv</strong> – CSV table of crop areas in hectares for different crops in German administrative areas. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2020) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0 <strong>districtweather.zip</strong> – zipped directories DistrictWeather and DistrictFeatures. DistrictWeather contains 401 ASCII DAT files with monthly weather variables, one per district. These have been generated using rasterized weather data from the German meteorological service (Deutscher Wetterdienst, DWD), an official digital map of administrative boundaries provided by the German Federal Agency for Cartography and Geodesy, and CLC-2012 land use data provided by Copernicus. DistrictFeatures is an empty directory to be used by the corresponding program. <strong>climatescenarios.zip</strong> - zipped directories ClimateScenarios and ClimateScenarioFeatures. ClimateScenarios contains three subdirectories with example climate scenario realisations in the same format as DistrictWeather. ClimateScenarioFeatures is an empty directory to be used by the corresponding program. <strong>yield-indat.csv </strong>– CSV table of crop yields in dt/ha for different crops in German administrative areas, annual values for the years 1999–2020. Modified from data originally provided by the statistical offices of Germany (© Statistische Ämter des Bundes und der Länder, Deutschland, 2021) and re-distributed here in this modified form also under the terms of the Data licence Germany – attribution – version 2.0, see https://www.govdata.de/dl-de/by-2-0
DOI: 10.5281/zenodo.5777028
title: Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest

description: Dataset was used for the article "Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest". The related work proposes to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Abstract of the paper: A better knowledge of tree vegetative growth patterns and their relationship to environmental variables is crucial in understanding forest growth dynamics and how climate change may affect them. Generally less studied than reproductive structures, the phenology of tree vegetative growth mainly focuses on the analysis of growing shoots, from vegetative buds development to leaf fall. This growth process usually strongly differs between temperate and tropical regions. In temperate regions, this pattern is quite well known. Low winter temperatures impose a stop of the vegetative growth shoots and lead to the typical expression of an annual growth cycle for the vast majority of tree species. In moist tropical regions, on the other hand, the seasonality is much less marked. In addition, these regions contain a much wider variety of tree species. These two aspects lead to a tremendous diversity of phenological patterns that are still poorly known and understood. In particular, not much is known on the periodicity and timing of growth at individual trees, population, or community levels. The work carried out in this study aims to advance knowledge in this area, focusing more particularly on herbarium scans, as herbarium collections offer the promise of monitoring plant phenology over long time periods. However, such a study requires the ability to detect a sufficiently large number of growing shoots in herbarium collections to draw statistically relevant conclusions, which can be very costly if the work is done manually. Furthermore, herbarium collections traditionally focus on reproductive organs, and herbarium specimens showing growing shoots are pretty rare. We propose in this paper to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect these relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Our results show the relevance of using herbarium data for vegetative phenology research, as well as the potential of deep learning approaches for growth shoot detection.
DOI: 10.5281/zenodo.5777027
title: Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest

description: Dataset was used for the article "Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest". The related work proposes to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Abstract of the paper: A better knowledge of tree vegetative growth patterns and their relationship to environmental variables is crucial in understanding forest growth dynamics and how climate change may affect them. Generally less studied than reproductive structures, the phenology of tree vegetative growth mainly focuses on the analysis of growing shoots, from vegetative buds development to leaf fall. This growth process usually strongly differs between temperate and tropical regions. In temperate regions, this pattern is quite well known. Low winter temperatures impose a stop of the vegetative growth shoots and lead to the typical expression of an annual growth cycle for the vast majority of tree species. In moist tropical regions, on the other hand, the seasonality is much less marked. In addition, these regions contain a much wider variety of tree species. These two aspects lead to a tremendous diversity of phenological patterns that are still poorly known and understood. In particular, not much is known on the periodicity and timing of growth at individual trees, population, or community levels. The work carried out in this study aims to advance knowledge in this area, focusing more particularly on herbarium scans, as herbarium collections offer the promise of monitoring plant phenology over long time periods. However, such a study requires the ability to detect a sufficiently large number of growing shoots in herbarium collections to draw statistically relevant conclusions, which can be very costly if the work is done manually. Furthermore, herbarium collections traditionally focus on reproductive organs, and herbarium specimens showing growing shoots are pretty rare. We propose in this paper to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect these relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Our results show the relevance of using herbarium data for vegetative phenology research, as well as the potential of deep learning approaches for growth shoot detection.
DOI: 10.6084/m9.figshare.17263601
title: 11_Microglia.bw

description: microglia
DOI: 10.6084/m9.figshare.17263601.v1
title: 11_Microglia.bw

description: microglia
DOI: 10.5281/zenodo.5789394
title: Data Set of Publication - Outcome of Scleral Rupture Primary Management Without Vitrectomy Jakarta Eye Trauma Study

description: This dataset was collected for research purposes. The dataset was about the scleral rupture we found in our teritories