cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6774750
title: High elevation forest age structure across an elevational gradient in the Greater Yellowstone Ecosystem

description: Dataset for Blomdahl et al. 2022. Drivers of forest change in the Greater Yellowstone Ecosystem. Journal of Vegetation Science. See publication for site description and methods. Descriptions for variables in “trees_seedlings.csv”: <strong>Plot_ID: </strong>Plot identifier. Nomeclature follows transect name and plot number. ECO="Ecotone" transect, SBM="South Bird Mountain" transect. <strong>Year_Sampled: </strong>Samples collected 2017-2019. <strong>Tree_ID: </strong>Identifier for unique trees and seedlings. <strong>Core: </strong>Tree core sample identifier. Applies only to trees (cores not taken from seedlings). Generally, 2 cores were taken per Tree &gt;5 cm DCH, though sometimes up to 4 were collected if a sample was rotten. <strong>Sample_ID: </strong>Identifier for unique samples, some of which come from the same tree (for unique individuals: "Tree_ID"). Applies to trees and seedlings. <strong>Form: </strong>Stems &gt;5 cm diameter at coring height (DCH), coring height=30 cm; Seedlings &gt;30: Stems &lt;5 cm DCH and &gt;30 cm in height (sometimes referred to as "saplings"); Seedlings &lt;30: Stems &lt;30 cm in height <strong>Species: </strong>ABLA=<em>Abies</em> <em>lasiocarpa</em>, PIAL=Pinus <em>albicaulis</em>, PICO=<em>Pinus</em> <em>contorta</em>, PIEN=<em>Picea</em> <em>engelmannii</em>, PSME=<em>Pseudotsuga</em> <em>menziesii</em> <strong>Diam_30_cm: </strong>Diameter (cm) at 30 cm sample height. <strong>Diam_0_cm: </strong>Diameter (cm) at 0 cm sample height (i.e., the base). Only seedlings were measured at base, not trees. <strong>Seedling_Ht_cm: </strong>Length of seedling stem (cm). <strong>Bark_Thick_cm: </strong> Bark thickness (cm). Not recorded in 2018. Bark thickness assumed to be &lt;0.1 cm for seedlings. <strong>Live_Dead: </strong>Live/Dead status when sampled. L=Live, D=Dead. <strong>Canopy: </strong>Canopy position. D=Dominant, C=Codominant. S=Suppressed. Not recorded in 2017. All seedlings assumed suppressed. <strong>Outer_Ring: </strong>Last complete year of growth, generally one year prior to Year_Sampled for live trees. Mortality year for dead trees. <strong>Inner_Ring:</strong> Year of innermost ring measured in tree core sample measured at 30 cm sample height. Does not apply to seedlings, which were sampled as cross sections, and therefore the pith was always measureable. <strong>Pith_30: </strong>Year of the first ring of the tree or sapling, measured at 30 cm sampling height. <strong>Pith_0: </strong>Year of the first ring of the seedling, measuring at 0 cm sampling height (i.e., the base). Applies only to seedlings, which were destructively sampled at the base. <strong>Estab_Year: </strong>Estimated year of establishment for trees and saplings, same as Pith_0 for seedlings. See methods of Blomdahl et al., 2022, for how establishment year was estimated. <strong>Age:</strong> Estimated age of the tree.
DOI: 10.6084/m9.figshare.20205617.v2
title: Longitudinal clinical simulation cohorts

description: This dataset includes three longitudinal simulation cohorts; body mass index (BMI), glucose, and systolic blood pressure (SBP). In each cohort, the trajectories were randomly selected from a pool of patients in the Cleveland Clinic EHR seen between 2000-2018. <br> Please cite the following paper and see the paper for more details: <br> H. Javidi, et al., Identification of Robust Deep Neural Network Models of Longitudinal Clinical Measurements, Nature Digital Medicine, 2022.
DOI: 10.6084/m9.figshare.20205617.v3
title: Longitudinal clinical simulation cohorts

description: <strong>Rotroff Lab</strong> <strong>2022-07-01</strong> This repository contains simulated datasets related to <code>Identification of Robust Deep Neural Network Models of Longitudinal Clinical Measurements</code> article published in <em>npj Digital Medicine</em> (2022). These datasets were simulated by manipulating shapes and magnitudes of clinical longitudinal measurements of random blood glucose tests, systolic blood pressure and BMI. Per clinical measurement type, 3168 datasets were generated. In half of these datasets, classes were defined by differences in shape (50%, N=1584) and in the other half, classes were defined by differences in magnitudes (50%, N=1584). The complete list of parameters manipulated is as follows: Effect Size: 0.25-2.75 Dispersion: 0.25-2.00 Noise: 0%, 10%, 25%, 50% Irregularity: Moderate (0-3 missing measurements) or High (2-5 missing measurements) For more information on the simulations, please refer to the Methods section of the paper. Please note that with the inclusion of class imbalance, the total number of cohorts increased to 12,672 in the paper. <strong>Parameter annotations</strong> The parameters are annotated in the file names as follows: _Simulated_Cohort_EffectSize_Dispersion_Noise_Irregularity_OverlapBetweenClasses.csv When <code>IrrNMP</code> is used for Irregularity, no irregularity is applied. <code>OverlapBetweenClasses</code> specifies the overlap between the two classes. <strong>Directory Structure</strong> The directory structure for each clinical measurement type is as follows: bmi_simulation (N=1056)mag_cohorts shape_cohorts <code>Simulation_Cohort_IDRandomNumber</code>: Random trajectory base 4 irregularity_cohorts other_cohorts <code>Simulation_Cohort_ID_RandomNumber</code>: Random trajectory base 5 irregularity_cohorts other_cohorts <code>Simulation_Cohort_ID_RandomNumber</code>: Random trajectory base 6 irregularity_cohorts other_cohorts glucose_simulation (1056)mag_cohorts shape_cohorts sbp_simulation (N=1056)mag_cohorts shape_cohorts <strong>Note:</strong> <code>irregularity_cohorts</code>: Effect sizes, dispersions and irregularity were manipulated in these files. <code>other_cohorts</code>: Effect sizes, dispersions and noise were manipulated in these files. <br> <br> Please cite the following paper and this dataset in your work: <br> H. Javidi, et al., Identification of Robust Deep Neural Network Models of Longitudinal Clinical Measurements, Nature Digital Medicine, 2022.
DOI: 10.5281/zenodo.6787378
title: SMARTDEST DATASET WP3 v1.0

description: The SMARTDEST DATASET WP3 v1.0 includes data at sub-city level for 7 cities: Amsterdam, Barcelona, Edinburgh, Lisbon, Ljubljana, Turin, and Venice. It is made up of information extracted from public sources at the local level (mostly, city council open data portals) or volunteered geographic information, that is, geospatial content generated by non-professionals using mapping systems available on the Internet (e.g., Geofabrik). Details on data sources and variables are included in a ‘metadata’ spreadsheet in the excel file. The same excel file contains 5 additional spreadsheets. The first one, labelled #1, was used to perform the analysis on the determinants of the geographical spread of tourism supply in SMARTDEST case study’s cities (in the main document D3.3, section 4.1), The second one (labelled #2) offers information that would allow to replicate the analysis on tourism-led population decline reported in section 4.3. As for spreadsheets named #3-AMS, #4-BCN, and #5-EDI, they refer to data sources and variables used to run follow-up analyses discussed in section 5.1, with the objective of digging into the causes of depopulation in Amsterdam, Barcelona, and Edinburgh, respectively. The column ‘row’ can be used to merge the excel file with the shapefile ‘db_task3.3_SmartDest’. Data are available at the <em>buurt</em> level in Amsterdam (an administrative unit roughly corresponding to a neighbourhood), census tract level in Barcelona and Ljubljana, for data zones in Edinburgh, statistical zones in Turin, and <em>località</em> in Venice.
DOI: 10.5281/zenodo.6787377
title: SMARTDEST DATASET WP3 v1.0

description: The SMARTDEST DATASET WP3 v1.0 includes data at sub-city level for 7 cities: Amsterdam, Barcelona, Edinburgh, Lisbon, Ljubljana, Turin, and Venice. It is made up of information extracted from public sources at the local level (mostly, city council open data portals) or volunteered geographic information, that is, geospatial content generated by non-professionals using mapping systems available on the Internet (e.g., Geofabrik). Details on data sources and variables are included in a ‘metadata’ spreadsheet in the excel file. The same excel file contains 5 additional spreadsheets. The first one, labelled #1, was used to perform the analysis on the determinants of the geographical spread of tourism supply in SMARTDEST case study’s cities (in the main document D3.3, section 4.1), The second one (labelled #2) offers information that would allow to replicate the analysis on tourism-led population decline reported in section 4.3. As for spreadsheets named #3-AMS, #4-BCN, and #5-EDI, they refer to data sources and variables used to run follow-up analyses discussed in section 5.1, with the objective of digging into the causes of depopulation in Amsterdam, Barcelona, and Edinburgh, respectively. The column ‘row’ can be used to merge the excel file with the shapefile ‘db_task3.3_SmartDest’. Data are available at the <em>buurt</em> level in Amsterdam (an administrative unit roughly corresponding to a neighbourhood), census tract level in Barcelona and Ljubljana, for data zones in Edinburgh, statistical zones in Turin, and <em>località</em> in Venice.
DOI: 10.5281/zenodo.6788250
title: Machine learning techniques with code

description: This dataset contains data from Paperswithcode.com obtained on January of 2022. The first file, 'Papers_with_abstracts' includes information about different research papers like their title, abstract, authors, etc. 'Links_between_papers_and_code' includes each paper connection to their corresponding github repository. Finally, 'Methods' includes a categorization of the aforementioned papers done by the community of Paperswithcode in different areas. These files were used in the Master Thesis Topic Modeling for Research Software done by María Ayuso in Universidad Politécnica de Madrid.
DOI: 10.5281/zenodo.6788249
title: Machine learning techniques with code

description: This dataset contains data from Paperswithcode.com obtained on January of 2022. The first file, 'Papers_with_abstracts' includes information about different research papers like their title, abstract, authors, etc. 'Links_between_papers_and_code' includes each paper connection to their corresponding github repository. Finally, 'Methods' includes a categorization of the aforementioned papers done by the community of Paperswithcode in different areas. These files were used in the Master Thesis Topic Modeling for Research Software done by María Ayuso in Universidad Politécnica de Madrid.
DOI: 10.5281/zenodo.6788040
title: SUM Reaction Data: The Chemoton 2.0 Data Set

description: This repository contains the data underlying the results presented in Unsleber, J. P.; Grimmel, S. A.; Reiher, M. <strong>2022</strong>, <em>arXiv:2202.13011 [physics.chem-ph]</em>.
DOI: 10.25390/caryinstitute.11801052.v46
title: Cary Institute Environmental Monitoring Program Current Year Data 2022

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: please click on the Cite button in the Figshare repository.<br>Those wishing to publish data from Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact data manager Vicky Kelly, kellyv@caryinstitute.org.<br>For complete data for previous years and for complete metadata, please see appropriate items for the Cary Institute Environmental Monitoring Program at https://caryinstitute.figshare.com/
DOI: 10.5517/ccdc.csd.cc295xfv
title: CCDC 2120847: Experimental Crystal Structure Determination

description: Related Article: Tomáš Chlupatý, Zdeňka Růžičková, Hana Kampová, Jan Merna, Aleš Růžička|2022|Inorg.Chem.|61|9392|doi:10.1021/acs.inorgchem.1c03850
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.cc297t0d
title: CCDC 2122663: Experimental Crystal Structure Determination

description: Related Article: Jianfei Bai|2021|CSD Communication|||
description: Related Article: Jian-Fei Bai, Jianbo Tang, Xiaolong Gao, Zhi-Jiang Jiang, Bencan Tang, Jia Chen, Zhanghua Gao|2022|Org.Lett.|24|4507|doi:10.1021/acs.orglett.2c01403
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.cc29yl84
title: CCDC 2143596: Experimental Crystal Structure Determination

description: Related Article: Daniel S. J. Miller, Sabine A. Voell, Izidor Sosič, Matic Proj, Olivia W. Rossanese, Gregor Schnakenburg, Michael Gütschow, Ian Collins, Christian Steinebach|2022|RSC Med. Chem.|13|731|doi:10.1039/D2MD00064D
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.cc2b9k18
title: CCDC 2154129: Experimental Crystal Structure Determination

description: Related Article: Tomáš Chlupatý, Zdeňka Růžičková, Hana Kampová, Jan Merna, Aleš Růžička|2022|Inorg.Chem.|61|9392|doi:10.1021/acs.inorgchem.1c03850
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.cc2bdnl0
title: CCDC 2157122: Experimental Crystal Structure Determination

description: Related Article: Palani Manikandan, Rudrasenan Agneswaran, Vinayagam Pavunkumar, Arasambattu K. Mohanakrishnan|2022|Org.Lett.|24|4502|doi:10.1021/acs.orglett.2c01234
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.cc2bjvvl
title: CCDC 2161160: Experimental Crystal Structure Determination

description: Related Article: Palani Manikandan, Rudrasenan Agneswaran, Vinayagam Pavunkumar, Arasambattu K. Mohanakrishnan|2022|Org.Lett.|24|4502|doi:10.1021/acs.orglett.2c01234
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.cc2blpxk
title: CCDC 2162929: Experimental Crystal Structure Determination

description: Related Article: Jian-Fei Bai, Jianbo Tang, Xiaolong Gao, Zhi-Jiang Jiang, Bencan Tang, Jia Chen, Zhanghua Gao|2022|Org.Lett.|24|4507|doi:10.1021/acs.orglett.2c01403
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.cc2bms70
title: CCDC 2163962: Experimental Crystal Structure Determination

description: Related Article: Alexander E. Sedykh, Dirk G. Kurth, Klaus Müller-Buschbaum|2022|CSD Communication|||
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.cc2bms81
title: CCDC 2163963: Experimental Crystal Structure Determination

description: Related Article: Alexander E. Sedykh, Dirk G. Kurth, Klaus Müller-Buschbaum|2022|CSD Communication|||
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.cc2bms92
title: CCDC 2163964: Experimental Crystal Structure Determination

description: Related Article: Alexander E. Sedykh, Dirk G. Kurth, Klaus Müller-Buschbaum|2022|CSD Communication|||
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.cc2bslnd
title: CCDC 2168594: Experimental Crystal Structure Determination

description: Related Article: Tsonko Kolev, Martina I. Peeva, Ivan P. Bogdanov, Iliyan V. Ognyanov, Atanas G. Atanasov, Nikolay T. Tzvetkov|2022|Z.Kristallogr.-New Cryst.Struct.|237|601|doi:10.1515/ncrs-2022-0181
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.cc2bt79q
title: CCDC 2169203: Experimental Crystal Structure Determination

description: Related Article: Aqilah Abdul Latiff, Yan Yi Chong, Wun Fui Mark-Lee, Mohammad B. Kassim|2022|Z.Kristallogr.-New Cryst.Struct.|237|689|doi:10.1515/ncrs-2022-0076
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: 10.15139/s3/x6byhs
title: Replication Data for: How the American Public Perceived Electoral Competition in the States During the Pre-Poll Era: A Prediction Market Data Analysis of the 1896 Presidential Election

description: This study uses prediction market data from the nation’s historical election betting markets to measure electoral competition in the American states during the era before the advent of scientific polling. Betting odds data capture ex ante expectations of electoral closeness in the aggregate, and as such improve upon existing measures of competition based on election returns data. Situated in an analysis of the1896 presidential election and its associated realignment, I argue that the market odds data show that people were able to anticipate the realignment and that expectations on the outcome in the states influenced voter turnout. Findings show that a month ahead of the election betting markets accurately forecast a McKinley victory in most states. This study further demonstrates that the market predictions identify those states where electoral competition would increase or decline that year and the consequences of these expected partisanship shifts on turnout. In places where the anticipation was for a close race voter expectations account for a turnout increase of as much as 6%. Participation dropped by 1% to 6% in states perceived as becoming electorally uncompetitive. The results support the conversion and dealignment theories from the realignment literature.
DOI: 10.25625/3lnri6/q5vdnp
title: paladin_balance_sheet.tab

description: :unav
DOI: 10.34740/kaggle/dsv/3889908
title: Data Science Subreddit Posts
DOI: 10.5281/zenodo.6760822
title: First study of the susceptibility of Culex quinquefasciatus from the Amazon Region to become infected and transmit the West Nile virus strain isolated in Brazil

description: <em>West Nile virus</em> is characterized as a neurotropic pathogen transmitted by <em>Culex</em> genus mosquitoes that cause West Nile fever. In 2018, the Evandro Chagas Institute performed the first isolation of a WNV strain in Brazil, from a horse brain sample. This study aimed to evaluate the susceptibility of <em>Cx. quinquefasciatus</em> from the Amazon region of Brazil to become infected and transmit the WNV strain isolated in 2018. Oral infection of naive F1 generation females was performed with sheep bloodmeal artificially infected with WNV, followed by analysis of infection, dissemination, and transmission rates, as well as viral titers of body, head, and saliva samples. The infection rate was 100% in all dpe evaluated. The dissemination rates varied in the three dpe, with a 40%, 37.5%, and 100% in the 7<sup>th</sup>, 14<sup>th</sup>, and 21<sup>st</sup> dpe, respectively. The transmission rate was 100% from the 21<sup>st</sup> dpe. These results indicate that that <em>Cx. quinquefasciatus</em> is susceptible to oral infection by the Brazilian strain of WNV