cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2022-12.2688894/2247802
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2689227/2247803
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695115/2247804
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695461/2247805
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695846/2247806
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2687794/2247807
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2689133/2247808
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2688247/2247809
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695364/2247810
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695807/2247811
title: Data for EMSL Project 51241 from December 2022
DOI: 10.6084/m9.figshare.22124219
title: The Urbanity Global Network Dataset

description: The Global Urban Network (GUN) dataset provides pre-computed node and edge attribute features for various cities. Each layer is available in .geojson format and can easily be converted into NetworkX, igraph, PyG, and DGL graph formats.
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For node attributes, we adopt a uniform Euclidean approach, as it provides a consistent, straightforward, and extensible basis for integrating heterogeneous data sources across different network locations. Accordingly, we construct 100 metres euclidean buffers for each network node and compute the spatial intersection with spatial targets (e.g., street view imagery points, points of interest, and building footprints). To ensure spatial consistency and accurate distance computation, we project spatial entities into local coordinate reference systems (CRS). Users can employ the Urbanity package to generate Euclidean buffers of arbitrary distance. 
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For edge attributes, we adopt a two-step approach: 1) compute the distance between each spatial point of interest and its proximate edges in the network, and 2) assign entities to the corresponding edge with lowest distance. To account for remote edges (e.g., peripheral routes that are not located close to any amenities), we specify a distance threshold of 50 metres. For buildings, we compute the distance between building centroids and their respective network edge.  Accordingly, we compute spatial indicators based on the set of elements assigned to each network edge.
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We also release aggregated subzone statistics for each city. Similarly, users can employ the Urbanity package to generate aggregate statistics for any arbitrary geographic boundary. 
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Urbanity Python package: https://github.com/winstonyym/urbanity.
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DOI: 10.25582/data.2022-12.2695884/2247812
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2689158/2247813
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2689478/2247814
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695524/2247815
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2687761/2247816
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695132/2247817
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2688506/2247818
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695338/2247819
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2694826/2247820
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2689410/2247821
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2694821/2247822
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2688242/2247823
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2695347/2247824
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2688120/2247825
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2689146/2247826
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2689111/2247827
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2694975/2247828
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2696286/2247829
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2696419/2247830
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2696152/2247831
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25582/data.2022-12.2688802/2247832
title: Data for EMSL Project 51241 from December 2022
DOI: 10.25934/pr00009001.0
title: Available datapackage for study 'A Phase 2 Randomized, Double-Blind, Controlled Trial of the Safety and Immunogenicity of Typhoid Conjugate Vaccine (Vi-TCV) Among Children Younger Than 2 Years in Ouagadougou, Burkina Faso'

description: Typhoid fever is an illness that may cause mild effects in children, such as fever and feeling tired, or it may cause serious effects-- even death. A new typhoid vaccine has recently been recommended by the World Health Organization (WHO) to prevent typhoid in children. But this new typhoid vaccine has not been tested with all of the vaccines given to children in Burkina Faso. The investigators want to look at this new vaccine, and study how safe it is in children in Burkina Faso and how their immune systems respond to the vaccine when given with other vaccines, such as yellow fever and meningitis A vaccines. The investigators plan to vaccinate 100 children between the ages of 9-11 months, and 150 children between the ages of 15 months and 2 years, in Ouagadougou, Burkina Faso, with either the typhoid vaccine or a vaccine against another illness called polio. Children will have follow-up visits on days 3, 7, 28 and 180. One teaspoon of blood will be collected on days 0 and 28.
DOI: 10.6084/m9.figshare.24892059
title: SupplementaryData2.txt

description: Researchers described the first crown bird from the Late Cretaceous (Maastrichtian) Hell Creek Formation. They identified a well-preserved synsacrum, with traits linking it to Neornithes. Phylogenetic study places it as a presbyornithid, an extinct anseriform group previously hinted at but not confirmed in the Hell Creek Formation. This study provides new insights into the origins, radiation, and paleoecology of early neornithines before the K-Pg mass extinction event.
DOI: 10.6084/m9.figshare.24892059.v1
title: SupplementaryData2.txt

description: Researchers described the first crown bird from the Late Cretaceous (Maastrichtian) Hell Creek Formation. They identified a well-preserved synsacrum, with traits linking it to Neornithes. Phylogenetic study places it as a presbyornithid, an extinct anseriform group previously hinted at but not confirmed in the Hell Creek Formation. This study provides new insights into the origins, radiation, and paleoecology of early neornithines before the K-Pg mass extinction event.
DOI: 10.57931/2248064
title: MERIT-Plus Dataset: Delineation of endorheic basins in 5 and 15 min upscaled river networks

description: Endorheic drainage basins, those inland basins not connected directly to ocean, are essential for hydrological modeling of global and regional water balances, land surface water storage, gravity anomalies, sea level rise, etc. Within many hydrological model frameworks, river basins are defined by digital river networks through their flow direction and connectivity datasets. Here we present an improvement to gridded flow direction data and its derivatives produced from upscaled global 5 and 15 arc minute MERIT networks. We explicitly label endorheic and exorheic drainage basins and alter the delineation of endorheic basins by merging small inland watersheds to the adjacent host basins. The resulting datasets have a significantly reduced number of endorheic basins while preserving the total land portion of those basins since most of the merged catchments were inside other larger endorheic areas. We developed and present here the endorheic basin delineation method. This method performs an analysis of the contributing river and basin geometry relative to the location of the flow end point (i.e. potential endorheic lake), proximity of the latter to the drainage basin boundary and the elevation difference between the basin's lowest point and potential spillover location at the basin boundary. The new digital river network was validated using the University of New Hampshire Water Balance Model by comparing the water balance of endorheic inland depressions with modeled accumulation of water in their inland lakes based on the observed historical climate drivers used by WBM.
DOI: 10.5281/zenodo.10035735
title: Historical Adoption of Technologies (HATCH) Dataset

description: About the Dataset 
The Historical Adoption of TeCHnologies (HATCH) dataset provides yearly data of technology adoption levels at both the global and country levels. The dataset includes a heterogeneous set of technologies that differ in their size, use, and spatial diffusion. The following files are available.
Citation
The paper can be cited as: Nemet, G., Greene, J., Müller-Hansen, F. et al. Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures. Commun Earth Environ 4, 397 (2023). https://doi.org/10.1038/s43247-023-01056-1 
Paper describing the data is available here: https://www.nature.com/articles/s43247-023-01056-1
 
File Descriptions
HATCH1.0.csv
Description: Dataset used in the analysis for "Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures," which includes a limited number of country-level datapoints.
 
HATCH_v1.5_Clean.csv
 
Description: Expanded dataset that includes country-level adoption of many technologies used in v1.0, with the addition of more global technology time series.
 
Tech_Growth_V1.5_variabledescriptions_Clean.yaml
Description: Description of each technology, including description, metric, and technology category.
 
HATCH_v1.5_DataSources.csv
Description: Lists the data sources and corresponding citations for each technology in the HATCH dataset.
 
Code for calculating growth rates available at: DOI: 10.5281/zenodo.8327347
 
Table of growth rate calculations for each technology available at: https://zenodo.org/records/10056128
 
Link to Scenario Explorer hosted by IIASA: https://cdr.apps.ece.iiasa.ac.at/story/hatch/
DOI: 10.5281/zenodo.10231865
title: Historical Adoption of Technologies (HATCH) Dataset

description: About the Dataset 
The Historical Adoption of TeCHnologies (HATCH) dataset provides yearly data of technology adoption levels at both the global and country levels. The dataset includes a heterogeneous set of technologies that differ in their size, use, and spatial diffusion. The following files are available.
Citation
The paper can be cited as: Nemet, G., Greene, J., Müller-Hansen, F. et al. Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures. Commun Earth Environ 4, 397 (2023). https://doi.org/10.1038/s43247-023-01056-1 
Paper describing the data is available here: https://www.nature.com/articles/s43247-023-01056-1
 
File Descriptions
HATCH1.0.csv
Description: Dataset used in the analysis for "Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures," which includes a limited number of country-level datapoints.
 
HATCH_v1.5_Clean.csv
 
Description: Expanded dataset that includes country-level adoption of many technologies used in v1.0, with the addition of more global technology time series.
 
Tech_Growth_V1.5_variabledescriptions_Clean.yaml
Description: Description of each technology, including description, metric, and technology category.
 
HATCH_v1.5_DataSources.csv
Description: Lists the data sources and corresponding citations for each technology in the HATCH dataset.
 
Code for calculating growth rates available at: DOI: 10.5281/zenodo.8327347
 
Table of growth rate calculations for each technology available at: https://zenodo.org/records/10056128
 
Link to Scenario Explorer hosted by IIASA: https://cdr.apps.ece.iiasa.ac.at/story/hatch/
DOI: 10.5281/zenodo.8427841
title: Historical Adoption of Technologies (HATCH) Dataset

description: About the Dataset 
The Historical Adoption of TeCHnologies (HATCH) dataset provides yearly data of technology adoption levels at both the global and country levels. The dataset includes a heterogeneous set of technologies that differ in their size, use, and spatial diffusion. The following files are available.
Citation
The paper can be cited as: Nemet, G., Greene, J., Müller-Hansen, F. et al. Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures. Commun Earth Environ 4, 397 (2023). https://doi.org/10.1038/s43247-023-01056-1 
Paper describing the data is available here: https://www.nature.com/articles/s43247-023-01056-1
 
File Descriptions
HATCH1.0.csv
Description: Dataset used in the analysis for "Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures," which includes a limited number of country-level datapoints.
 
HATCH_v1.5_Clean.csv
 
Description: Expanded dataset that includes country-level adoption of many technologies used in v1.0, with the addition of more global technology time series.
 
Tech_Growth_V1.5_variabledescriptions_Clean.yaml
Description: Description of each technology, including description, metric, and technology category.
 
HATCH_v1.5_DataSources.csv
Description: Lists the data sources and corresponding citations for each technology in the HATCH dataset.
 
Code for calculating growth rates available at: DOI: 10.5281/zenodo.8327347
 
Table of growth rate calculations for each technology available at: https://zenodo.org/records/10056128
 
Link to Scenario Explorer hosted by IIASA: https://cdr.apps.ece.iiasa.ac.at/story/hatch/
DOI: 10.5281/zenodo.8427842
title: Historical Adoption of Technologies (HATCH) Dataset

description: The Historical Adoption of TeCHnologies (HATCH) dataset provides yearly data of technology adoption levels at both the global and country levels. The dataset includes a heterogeneous set of technologies that differ in their size, use, and spatial diffusion. 
 
HATCH1.0.csv
Description: Dataset used in the analysis in "Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures," which includes a limited number of country-level datapoints.
 
HATCH1.5.csv
 
Description: Expanded dataset that includes country-level adoption of many technologies used in v1.0, with the addition of more global technology time series as well.
 
variable_descriptions.yaml
 
Description: Description of each technology, including more details about the metric.
 
"Data sources"
 
Description: Lists the data sources and corresponding citations for each technology in the HATCH dataset. (available in Version 2)
 
Code for calculating growth rates available at: 10.5281/zenodo.8327347
 
Table of growth rate calculations for each technology available at: 10.5281/zenodo.8356924
 
Link to Scenario Explorer hosted by IIASA:  (available in Version 2)
 
Citation:  (available in Version 2)
DOI: 10.5517/ccdc.csd.cc28cv8s
title: CCDC 2096755: Experimental Crystal Structure Determination

description: Related Article: Gerzon E. Delgado, José L. Fonseca, Asiloé J. Mora, Julia Bruno-Colmenárez, Cecilia Chacón, Gustavo Marroquin, Jonathan Cisterna, Iván Brito|2024|J.Mol.Struct.|1299|137205|doi:10.1016/j.molstruc.2023.137205
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.cc2clxtq
title: CCDC 2192934: Experimental Crystal Structure Determination

description: Related Article: Xin-Wei Li, Shu-Jun Fang, Ying-Ze Li, Li-Qing Qin, Nan-Ying Chen, Bin Zheng, Dong-Liang Mo, Gui-Fa Su, Jun-Cheng Su, Cheng-Xue Pan|2023|Bioorg.Chem.|143|107015|doi:10.1016/j.bioorg.2023.107015
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.cc2d7xn7
title: CCDC 2212149: Experimental Crystal Structure Determination

description: Related Article: Yixin Wang, Xin Wang, Bo Zhao, Zhixin Ren, Zishuo Yao, Wei Wei, Jie Wang, Jinwen Qin, Jing Xie, Minhua Cao|2023|Nano Energy|120|109163|doi:10.1016/j.nanoen.2023.109163
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.10421090
title: LRTS: Dataset of Long-Running Test Suites (Part 2)

description: LRTS
DOI: 10.5281/zenodo.10421091
title: LRTS: Dataset of Long-Running Test Suites (Part 2)

description: LRTS
DOI: 10.5281/zenodo.10421042
title: LRTS: Dataset of Long-Running Test Suites (Part 1)
DOI: 10.5281/zenodo.10421043
title: LRTS: Dataset of Long-Running Test Suites (Part 1)
DOI: 10.5281/zenodo.10421170
title: LRTS: Dataset of Long-Running Test Suites (Part 1)
DOI: 10.5281/zenodo.10421200
title: LRTS: Dataset of Long-Running Test Suites (Part 1)
DOI: 10.6084/m9.figshare.24847263
title: ccle_exp.csv

description: ....
DOI: 10.6084/m9.figshare.24847263.v1
title: ccle_exp.csv

description: ....
DOI: 10.6084/m9.figshare.24847860
title: drug_target.csv

description: ....
DOI: 10.6084/m9.figshare.24847860.v1
title: drug_target.csv

description: ....
DOI: 10.6084/m9.figshare.24884001
title: Benchmark data

description: Benchmark dataset for COF, Zeolite, and PPN (graphdata and griddata for porous materials)COF : 1) In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications, <i>Chemistry of Materials</i> <i>(</i><i>2018</i><i>)</i> 2) In Silico Discovery of Covalent Organic Frameworks for Carbon Capture, <i>ACS Applied Materials & Interfaces</i> <i>(</i><i>2020</i><i>)</i>Zeolite : Inverse design of porous materials using artificial neural networks, <i>Sci. Adv.</i> (2020)PPN : In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials<i>Journal of the American Chemical Society</i> <i>(</i><i>2014)</i>
DOI: 10.6084/m9.figshare.24847866
title: Untitled Item

description: ....
DOI: 10.6084/m9.figshare.24847866.v1
title: Untitled Item

description: ....
DOI: 10.5281/zenodo.10421098
title: LRTS: Dataset of Long-Running Test Suites (Part 3)

description: LRTS
DOI: 10.5281/zenodo.10421099
title: LRTS: Dataset of Long-Running Test Suites (Part 3)

description: LRTS
DOI: 10.6084/m9.figshare.21546138
title: Gene pathway analysis of the endometrium at the start of the window of implantation in women with unexplained infertility and unexplained recurrent pregnancy loss: is unexplained recurrent pregnancy loss a subset of unexplained infertility?

description: This study aims to understand differences/similarities in the genetic profile of the endometrium at the start of window of implantation (WOI) in women with unexplained infertility (UI) and unexplained recurrent pregnancy loss (uRPL). Differentially expressed genes (DEGs) from the endometrium were evaluated using gene expression array and pathway enrichment analysis was performed to analyse gene expression pathways involved in both conditions. We found 2,171 genes arranged in 117 pathways and 730 genes arranged in 33 pathways differentially expressed in endometrium of patients in UI and uRPL, respectively. Complement-coagulation cascades, morphine addiction pathway, and PI3K-Akt signalling pathway were predominantly differentially expressed in UI. Cancer pathways, NF-κB signalling pathway, and actin cytoskeleton regulation pathway showed significant changes in uRPL. Forty-eight percent of DEGs and 84% of differentially expressed pathways in uRPL were found in the endometrium of UI patients. Unexpected close association in gene expression pathways between UI and uRPL is observed supporting the hypothesis ‘uRPL is a clinical subset of UI’. Yet 100% DEGs overlap wasn’t found suggesting the endometrium has still some different gene expression patterns at start of WOI in UI and uRPL. Lastly, diagnostic tools may be developed for uRPL because more specific genes-pathways are involved compared with UI, which shows broader genetic expression profile.
DOI: 10.6084/m9.figshare.21546138.v2
title: Gene pathway analysis of the endometrium at the start of the window of implantation in women with unexplained infertility and unexplained recurrent pregnancy loss: is unexplained recurrent pregnancy loss a subset of unexplained infertility?

description: This study aims to understand differences/similarities in the genetic profile of the endometrium at the start of window of implantation (WOI) in women with unexplained infertility (UI) and unexplained recurrent pregnancy loss (uRPL). Differentially expressed genes (DEGs) from the endometrium were evaluated using gene expression array and pathway enrichment analysis was performed to analyse gene expression pathways involved in both conditions. We found 2,171 genes arranged in 117 pathways and 730 genes arranged in 33 pathways differentially expressed in endometrium of patients in UI and uRPL, respectively. Complement-coagulation cascades, morphine addiction pathway, and PI3K-Akt signalling pathway were predominantly differentially expressed in UI. Cancer pathways, NF-κB signalling pathway, and actin cytoskeleton regulation pathway showed significant changes in uRPL. Forty-eight percent of DEGs and 84% of differentially expressed pathways in uRPL were found in the endometrium of UI patients. Unexpected close association in gene expression pathways between UI and uRPL is observed supporting the hypothesis ‘uRPL is a clinical subset of UI’. Yet 100% DEGs overlap wasn’t found suggesting the endometrium has still some different gene expression patterns at start of WOI in UI and uRPL. Lastly, diagnostic tools may be developed for uRPL because more specific genes-pathways are involved compared with UI, which shows broader genetic expression profile.