cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2gjfs7
title: CCDC 2279919: Experimental Crystal Structure Determination

description: Related Article: Georgia G. Sands, Grace Ertle, Jimmy Mugemana, Richard J. Staples, John E. Bender, Shannon M. Biros|2023|Polyhedron|245|116659|doi:10.1016/j.poly.2023.116659
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gjfv9
title: CCDC 2279921: Experimental Crystal Structure Determination

description: Related Article: Georgia G. Sands, Grace Ertle, Jimmy Mugemana, Richard J. Staples, John E. Bender, Shannon M. Biros|2023|Polyhedron|245|116659|doi:10.1016/j.poly.2023.116659
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.10022417
title: EQUIPMENT TECHNOLOGIES MATERIALS

description: EQUIPMENT TECHNOLOGIES MATERIALS
DOI: 10.5281/zenodo.10022418
title: EQUIPMENT TECHNOLOGIES MATERIALS

description: EQUIPMENT TECHNOLOGIES MATERIALS
DOI: 10.6084/m9.figshare.24376237
title: Water color along the Kansas River

description: Kansas River subset of national dataset of river color, RiverSR, published here:Gardner et al. 2021. “The Color of Rivers.” Geophysical Research Letters 48 (1): 1–12. https://doi.org/10.1029/2020GL088946.And available here: https://zenodo.org/record/4304567<br>
DOI: 10.6084/m9.figshare.24376237.v1
title: Water color along the Kansas River

description: Kansas River subset of national dataset of river color, RiverSR, published here:Gardner et al. 2021. “The Color of Rivers.” Geophysical Research Letters 48 (1): 1–12. https://doi.org/10.1029/2020GL088946.And available here: https://zenodo.org/record/4304567<br>
DOI: 10.57745/2vepza
title: maize.csv

description: :unav
DOI: 10.57745/6i9fkf
title: table_description.tab

description: :unav
DOI: 10.57745/diwlqz
title: sugarbeet.csv

description: :unav
DOI: 10.57745/eru4lm
title: potatoes.csv

description: :unav
DOI: 10.57745/qvfzzs
title: wheat.csv

description: :unav
DOI: 10.57745/vs6plg
title: nb_crop_tfi.csv

description: :unav
DOI: 10.57745/44htqn
title: barley.csv

description: :unav
DOI: 10.57745/bikl7a
title: pea.csv

description: :unav
DOI: 10.57745/hollqu
title: oilseed.csv

description: :unav
DOI: 10.57745/kzscxc
title: sunflower.csv

description: :unav
DOI: 10.57745/xgt25w
title: scrip_div_TFI.R

description: :unav
DOI: 10.57745/3vmqd3
title: table_description.tab

description: :unav
DOI: 10.57745/ftvfyy
title: ROTATION.csv

description: :unav
DOI: 10.57745/gaktpo
title: CROP_ROTATION_TFI.R

description: :unav
DOI: 10.57745/oa0jti
title: table_description.tab

description: :unav
DOI: 10.57745/tix9c2
title: CROP.csv

description: :unav
DOI: 10.57745/jvmz8r
title: CROP_ROTATION_TFI.R

description: :unav
DOI: 10.57745/l2n7yj
title: CROP_ROTATION_TFI.R

description: :unav
DOI: 10.57745/mvxh54
title: CROP_ROTATION_TFI.R

description: :unav
DOI: 10.6084/m9.figshare.23779821.v2
title: <strong>Extraction Amazonian Manatee</strong> (<em>Trichechus inunguis</em>), 1533-1898

description: The West Indian (<em>Trichechus manatus</em>) and the Amazonian (<em>T. inunguis</em>) species of manatees have been exploited by different societies from ancient times to the present. Deploying the matrix of Indigenous knowledge and uses, the hunting of manatees was at the core of European dominion strategies of the Americas within a framework of, appropriation, use and consumption of marine and aquatic mammals which spanned the post-colonial period. Both species are currently listed by the IUCN as Vulnerable and population trends are expected to decrease in future. The aim of this paper is to identify and employ quantitative information about manatee extractions, primarily for the Amazonian species, in pre-industrial times. We undertook a four-step approach that included a systematic literature review, identification and review of documentary sources, selection and extraction of quantitative data, and contextual analysis through cross-analysis of written and cartographic sources. Our quantitative estimates resulted in a total of 14.030 individuals captured and a biomass removal of 6.248,9 tonnes in a period of 52 separate years between the 16th and the 19th century. From those, 12.818 manatees were hunted between 1843 and 1898, comprising 91% of all captures prior to the 20th century when another moment of accelerated extractions occurred. We obtained a trend of increasing captures from the 17th to the 20th century, with a lacuna of data for the 16th century. Estimating past extractions over the centuries, enabled us to realise a decrease of 94% of the overall population size. This informs current and future conservation measures for the species while also including traditional and indigenous views of addressing and using aquatic resources.
DOI: 10.5281/zenodo.10022358
title: X-Ray diffraction images from a crystal of D. radiodurans DdrC - PDB 7UDI - Crystal form xMJ7121

description: Dimer of the DNA-Damage Response Protein C (DdrC) from Deinococcus radiodurans. 
Crystal Form xMJ7121. 
FL protein with L131M/184M mutation and derivatized with SeMet. 
Collected at 0.9795 Å
Rotation about omega. 1120 frames at a width of 0.25°/frame.
Data collected in two 140° wedges.
Raw data integrated and scaled in autoPROC.
DOI: 10.5281/zenodo.10022357
title: X-Ray diffraction images from a crystal of D. radiodurans DdrC - PDB 7UDI - Crystal form xMJ7121

description: Dimer of the DNA-Damage Response Protein C (DdrC) from Deinococcus radiodurans. 
Crystal Form xMJ7121. 
FL protein with L131M/184M mutation and derivatized with SeMet. 
Collected at 0.9795 Å
Rotation about omega. 1120 frames at a width of 0.25°/frame.
Data collected in two 140° wedges.
Raw data integrated and scaled in autoPROC.
DOI: 10.34848/t4wsma/dhl9ej
title: 2021 11 11 dataset of Colombian military and guerrillas.dta

description: Respuestas a cuestionarios sobre procesos de reintegración a la vida civil; Survey responses about reintegration processes
DOI: 10.34848/t4wsma
title: 2021 11 11 dataset of Colombian military and guerrillas

description: Dataset de encuestas con exmiembros de las fuerzas militares y exguerrilleros de Farc.
DOI: 10.5519/qd.tmnpsmtn
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4 records)
DOI: 10.25387/g3.23624268
title: Supplemental Material for Corut and Wallace, 2023

description: <strong>Supplementary Data S1:</strong> Supplemental information contains Tables S1-S8. <strong>Supplementary Data S2:</strong> Supplemental information contains Figures S1-S5. <strong>Supplementary Data S3-5:</strong> Example default configuration files of kGWASflow. <strong>Supplementary Data S6-10:</strong> The configuration files, phenotype information and HTML summary report of kGWASflow run with the E.coli dataset. <strong>Supplementary Data S11-15:</strong> The configuration files, phenotype information and HTML summary report of kGWASflow run with the maize dataset.
DOI: 10.25387/g3.23624268.v1
title: Supplemental Material for Corut and Wallace, 2023

description: <strong>Supplementary Data S1:</strong> Supplemental information contains Tables S1-S8. <strong>Supplementary Data S2:</strong> Supplemental information contains Figures S1-S5. <strong>Supplementary Data S3-5:</strong> Example default configuration files of kGWASflow. <strong>Supplementary Data S6-10:</strong> The configuration files, phenotype information and HTML summary report of kGWASflow run with the E.coli dataset. <strong>Supplementary Data S11-15:</strong> The configuration files, phenotype information and HTML summary report of kGWASflow run with the maize dataset.
DOI: 10.5281/zenodo.10019803
title: NREL 5MW wind turbine blade and tower degradation

description: Problem
➔Track the degradation of one rotor blade and tower base over over as 10 months period under wind inflow and operational uncertainties
Scenario
➔Continuous wind turbine blade and tower stiffness degradation, emulating blade root delamination resulting in dynamic instability, leading to the tower base excess fatigue.
Simulation environment and setup in FAST v8 :
➔In the blade input file: modify flap and edge stiffnesses in damage regions, blade root (only 1 in 3 blades is affected). Assumed equal degradation in both edgewise and flapwise directions
➔In the blade input file: modify mode shapes coefficients (1st flap, 2nd flap and 1st edge modes)
➔In the tower input file: modify FA and SS stiffnesses in damage regions, tower base
➔In the tower input file: modify mode shapes coefficients (1st & 2nd FA and 1st & 2nd SS modes)
➔Note that the tower degradation only "shows up" in the last 2 monitoring periods
➔In the turbulence input file: modify wind speed, tuburlence intensity, shear exponent, horizontal and vertical inflow angles.
➔Further complication by assuming that the average input environmental conditions are not stationary over the monitoring period, emulating seasonal variations.
➔This means 120 environmental samples (and consequenly input wind field time series) are sampled from different distributions each period during the degradation process
DOI: 10.5281/zenodo.10019802
title: NREL 5MW wind turbine blade and tower degradation

description: Problem
➔Track the degradation of one rotor blade and tower base over over as 10 months period under wind inflow and operational uncertainties
Scenario
➔Continuous wind turbine blade and tower stiffness degradation, emulating blade root delamination resulting in dynamic instability, leading to the tower base excess fatigue.
Simulation environment and setup in FAST v8 :
➔In the blade input file: modify flap and edge stiffnesses in damage regions, blade root (only 1 in 3 blades is affected). Assumed equal degradation in both edgewise and flapwise directions
➔In the blade input file: modify mode shapes coefficients (1st flap, 2nd flap and 1st edge modes)
➔In the tower input file: modify FA and SS stiffnesses in damage regions, tower base
➔In the tower input file: modify mode shapes coefficients (1st & 2nd FA and 1st & 2nd SS modes)
➔Note that the tower degradation only "shows up" in the last 2 monitoring periods
➔In the turbulence input file: modify wind speed, tuburlence intensity, shear exponent, horizontal and vertical inflow angles.
➔Further complication by assuming that the average input environmental conditions are not stationary over the monitoring period, emulating seasonal variations.
➔This means 120 environmental samples (and consequenly input wind field time series) are sampled from different distributions each period during the degradation process
DOI: 10.5281/zenodo.8084387
title: Gap-free 1km PM2.5 dataset in China (2000-present)

description: This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to 10.5281/zenodo.7229348. More datasets can be found on the right navigation panel of 10.5281/zenodo.4569557. 


We also estimate other atmospheric data:


For full-coverage, 1-km, AOD data in China, please go to harvard dataverse. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.


We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method. Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The monthly average data during 2000-2022 are released here in CSV format (if you want GEOTIFF files, please go to 10.5281/zenodo.8347128) and free for non-commercial use. If you want use our dataset, please cite the following publication. 


The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. 


-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. Journal of Environmental Management, 342, 118145.[url]


-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. Atmospheric Research, 281, 106481.[url]
DOI: 10.5281/zenodo.8084388
title: Gap-free 1km PM2.5 dataset in China (2000-present)

description: This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to 10.5281/zenodo.7229348. More datasets can be found on the right navigation panel of 10.5281/zenodo.4569557. 


We also estimate other atmospheric data:


For full-coverage, 1-km, AOD data in China, please go to harvard dataverse. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.


We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method. Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The monthly average data during 2000-2022 are released here in CSV format (if you want GEOTIFF files, please go to 10.5281/zenodo.8347128) and free for non-commercial use. If you want use our dataset, please cite the following publication. 


The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. 


-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. Journal of Environmental Management, 342, 118145.[url]


-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. Atmospheric Research, 281, 106481.[url]
DOI: 10.6084/m9.figshare.24377899
title: 20231020_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24377899.v1
title: 20231020_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.17608/k6.auckland.24376909
title: AQC-left-heart-segmentation

description: You can download the test segmentations and example area-time curve population priors on this site.In this study, we developed a workflow to automatically perform a single-frame quality control (QC) assessment, followed by post-processing procedures and multi-frame QC analysis. Subsequently, this workflow is utilised for the calculation of routine clinical cardiac indices. This data can be used to (partially) run the workflow. <b>GitHub:</b><br>https://github.com/bgeven/AQC-left-heart-segmentation
DOI: 10.17608/k6.auckland.24376909.v2
title: AQC-left-heart-segmentation

description: You can download the test segmentations and example area-time curve population priors on this site.In this study, we developed a workflow to automatically perform a single-frame quality control (QC) assessment, followed by post-processing procedures and multi-frame QC analysis. Subsequently, this workflow is utilised for the calculation of routine clinical cardiac indices. This data can be used to (partially) run the workflow. <b>GitHub:</b><br>https://github.com/bgeven/AQC-left-heart-segmentation
DOI: 10.17608/k6.auckland.24376909.v1
title: AQC-left-heart-segmentation.zip

description: Zip file containing test segmentations and population priors of left ventricular (LV) and left atrial (LA) area-time curves.
DOI: 10.6084/m9.figshare.24377272
title: Study explores the significant impacts of environmental factors and researchers’ needs on behavior and output.

description: The survey was conducted in the form of an online questionnaire, which includes the following three aspects of content:(1) Basic information about the participants, including the size of their team (the number of people), the duration of their engagement in research and teaching, and whether they have interdisciplinary research experience. This information will have an impact on the results during the analysis.(2) Participants are asked to rate the importance of the 30 factors mentioned earlier from the perspective of facilitating research output, scoring them on a scale of 1 to 5, where 1 is "no impact" and 5 is "very important."(3) Participants are required to classify the 30 factors involved in the questionnaire according to their understanding. They can create any number of groups, with no more than 10 variables per group, and give each group a keyword label.A total of 10 researchers from science, engineering, humanities, and history disciplines participated in this survey.
DOI: 10.6084/m9.figshare.24377272.v1
title: Study explores the significant impacts of environmental factors and researchers’ needs on behavior and output.

description: The survey was conducted in the form of an online questionnaire, which includes the following three aspects of content:(1) Basic information about the participants, including the size of their team (the number of people), the duration of their engagement in research and teaching, and whether they have interdisciplinary research experience. This information will have an impact on the results during the analysis.(2) Participants are asked to rate the importance of the 30 factors mentioned earlier from the perspective of facilitating research output, scoring them on a scale of 1 to 5, where 1 is "no impact" and 5 is "very important."(3) Participants are required to classify the 30 factors involved in the questionnaire according to their understanding. They can create any number of groups, with no more than 10 variables per group, and give each group a keyword label.A total of 10 researchers from science, engineering, humanities, and history disciplines participated in this survey.
DOI: 10.17882/96677
title: CTD casts of H2020 Eurofleets+ OASIS Cruise, SE Alboran Sea

description: CTD casts have been collected durig the OASIS Cruise, funded by the EU H2020 Eurofleets+ Project. Casts have been performed in a area surrounding the Catifas and Cabliers Cold-Water Coral Mounds, in the southeastern Alboran Sea.
DOI: 10.6084/m9.figshare.22762076.v4
title: SpikeSeq Supplemental Material

description: The SARS-CoV-2 spike protein is a highly immunogenic and mutable protein that is the target of vaccine prevention and antibody therapeutics. This makes the encoding S-gene an important sequencing target. The SARS-CoV-2 sequencing community overwhelmingly adopted tiling amplicon-based strategies for sequencing the entire genome. As the virus evolved, primer mismatches inevitably led to amplicon drop-out. Given the exposure of the spike protein to host antibodies, mutation occurred here most rapidly, leading to amplicon failure over the most insightful region of the genome. To mitigate this, we developed SpikeSeq, a targeted method to amplify and sequence the S-gene. We evaluated 20 distinct primer designs through iterative <i>in silico</i> and <i>in vitro</i> testing to select the optimal primer pairs and run conditions. Once selected, periodic <i>in silico</i> analysis monitor primer conservation as SARS-CoV-2 evolves. Despite being designed during the Beta wave, the selected primers remain &gt; 99% conserved through Omicron as of 2023-04-14. To validate the final design, we compared SpikeSeq data and National SARS-CoV-2 Strain Surveillance whole-genome data for 321 matching samples. Consensus sequences for the two methods were highly identical (99.998%) across the S-gene. SpikeSeq can serve as a complement to whole-genome surveillance or be leveraged where only S-gene sequencing is of interest. While SpikeSeq is adaptable to other sequencing platforms, the Nanopore platform validated here is compatible with low to moderate throughputs, and its simplicity better enables users to achieve accurate results, even in low resource settings.
DOI: 10.34740/kaggle/dsv/6740926
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/6740927
title: Mahanagar Yatayat Transport Dataset
DOI: 10.5281/zenodo.8381437
title: Data --- "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions"

description: Data to reproduce the results of the manuscript entitled "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions" submitted to Geophysical Research Letters. The companion jupyter notebook can be found in DOI: 10.5281/zenodo.8387558
DOI: 10.5281/zenodo.10015973
title: Relief delivery simulations using the transport network of Visayas, Philippines

description: In this study, we constructed a transportation network connecting the cities and municipalities of the Visayas island group in the Philippines with their regional relief hubs and set up a relief delivery simulation to affected town centers, defined to be those within a 100 km radius of the path of Typhoon Haiyan. We measured the relief delivery efficiency of the transport network over a range of damage scenarios relative to the undamaged baseline case. 
This contains the files and codebase necessary to construct the Visayas transportation network and perform relief simulations.
DOI: 10.5281/zenodo.7585519
title: Relief delivery simulations using the transport network of Visayas, Philippines

description: In this study, we constructed a transportation network connecting the cities and municipalities of the Visayas island group in the Philippines with their regional relief hubs and set up a relief delivery simulation to affected town centers, defined to be those within a 100 km radius of the path of Typhoon Haiyan. We measured the relief delivery efficiency of the transport network over a range of damage scenarios relative to the undamaged baseline case. 
This contains the files and codebase necessary to construct the Visayas transportation network and perform relief simulations.
DOI: 10.5281/zenodo.10020958
title: PepNet, PointNovo, and DeepNovo running results on PXD014877 and PXD019483 and DIA dataset

description: PepNet, PointNovo, and DeepNovo running results on PXD014877 and PXD019483 and DIA dataset
For publication: Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks
DOI: 10.5281/zenodo.7869926
title: PepNet, PointNovo, and DeepNovo running results on PXD014877 and PXD019483 and DIA dataset

description: PepNet, PointNovo, and DeepNovo running results on PXD014877 and PXD019483 and DIA dataset
For publication: Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks