cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/15i1c5/bprupm
title: rpg_complete_2020_d08.zip

description: :unav
DOI: 10.57745/15i1c5/eb9azj
title: rpg_complete_2020_d52.zip

description: :unav
DOI: 10.57745/15i1c5/sxqh28
title: rpg_complete_2020_d10.zip

description: :unav
DOI: 10.57745/15i1c5/8zfpmg
title: rpg_complete_2020_d51.zip

description: :unav
DOI: 10.5517/ccdc.csd.cc2bs8v8
title: CCDC 2168290: Experimental Crystal Structure Determination

description: Related Article: Gregory C. Turpin, Baylei G. Boyer, Cooper R. Gangi, Thaylon J. Hernandez, Ryan T. VanderLinden, Richard D. Ernst|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.cc2byzcn
title: CCDC 2173762: Experimental Crystal Structure Determination

description: Related Article: Jeanette A. Krause, Stuart A. Willison, William B. Connick|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.cc2byzdp
title: CCDC 2173763: Experimental Crystal Structure Determination

description: Related Article: Jeanette A. Krause, Rick Simons, William B. Connick|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.cc2bz17m
title: CCDC 2173820: Experimental Crystal Structure Determination

description: Related Article: WANG CHENG|2022|CSD Communication|||
description: Related Article: Cheng Wang, Dan Wang, Zhisheng Fu, Yawei Qin, Qisheng Zhang, Zhiqiang Fan|2022|J.Catalysis|413|311|doi:10.1016/j.jcat.2022.06.044
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.cc2bzf70
title: CCDC 2174192: Experimental Crystal Structure Determination

description: Related Article: Jeffrey G. Kulsomphob, Ryan T. VanderLinden, and Richard D. Ernst|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.cc2c00v8
title: CCDC 2174769: Experimental Crystal Structure Determination

description: Related Article: Jeanette A. Krause, Stuart A. Willison, William B. Connick|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: 10.5281/zenodo.7067699
title: Limitations of Catchment Area Estimates and River Basin Boundaries from CWC and WRIS

description: The following files are included:<br> [Item 1 'share_stations.txt'] list of 100 stations used in this study and their attributes, pipe(|) delimited<br> [Item 2 'share_hydrosheds_rivers'] one shapefile of HydroSHEDS river network up to Farakka<br> [Item 3 'share_merit_rivers'] one shapefile of MERIT river network up to Farakka<br> [Item 4 'share_hydrosheds_reloc'] one shapefile of relocated stations, HydroSHEDS network<br> [Item 5 'share_merit_reloc'] one shapefile of relocated stations, MERIT network<br> [Item 6 folder 'share_bounds'] shapefiles of delineated catchment boundaries associated with each station (100 total)<br> [Item 7 'share_station_maps.pdf'] PDF file of maps showing location of each station and associated catchment boundary (100 pages)<br>
DOI: 10.5281/zenodo.7067698
title: Limitations of Catchment Area Estimates and River Basin Boundaries from CWC and WRIS

description: The following files are included:<br> [Item 1 'share_stations.txt'] list of 100 stations used in this study and their attributes, pipe(|) delimited<br> [Item 2 'share_hydrosheds_rivers'] one shapefile of HydroSHEDS river network up to Farakka<br> [Item 3 'share_merit_rivers'] one shapefile of MERIT river network up to Farakka<br> [Item 4 'share_hydrosheds_reloc'] one shapefile of relocated stations, HydroSHEDS network<br> [Item 5 'share_merit_reloc'] one shapefile of relocated stations, MERIT network<br> [Item 6 folder 'share_bounds'] shapefiles of delineated catchment boundaries associated with each station (100 total)<br> [Item 7 'share_station_maps.pdf'] PDF file of maps showing location of each station and associated catchment boundary (100 pages)<br>
DOI: 10.5281/zenodo.7082676
title: Climate data for Machine Learning based 100-year flood flow prediction model

description: This study evaluates the application of ML technique over northeast United States regions and compares its performance to the U.S. Geological Survey (USGS) Streamflow Statistics (StreamStats)
DOI: 10.5281/zenodo.7082675
title: Climate data for Machine Learning based 100-year flood flow prediction model

description: This study evaluates the application of ML technique over northeast United States regions and compares its performance to the U.S. Geological Survey (USGS) Streamflow Statistics (StreamStats)
DOI: 10.5066/p9gy1uf4
title: Preliminary model data for lake level gate operation and discharge at Reelfoot Lake - Tennessee and Kentucky

description: Reelfoot Lake, in northwestern Tennessee and southwestern Kentucky, is home to a Federal wildlife refuge, a State wildlife-management area, and to a tourism industry that is based on hunting, fishing, birding, and the area?s unique cultural history. In 2012, the U.S. Geological Survey, in cooperation with the Tennessee Wildlife Resources Agency, began a series of hydrologic investigations to support the U.S. Fish and Wildlife Service in managing lake level. The objective was to develop operational models that preserved the historic patterns of variability that had characterized the lake for the past 60 years while meeting seasonal water-level targets (Heal and others, 2022). Preliminary models for gate operations at the new outlet control structure for Reelfoot Lake were developed by the U.S. Geological Survey using calibrated ratings of the lift gates. In 2018, the old structure at the outlet of Reelfoot Lake was buried and lake level control was transferred to a new structure. The transition from lake-level management of the old control structure to the new control structure was documented using historical lake level and discharge measurements and records of stop-log management from March 7, 2013, to August 12, 2018. Discharge into Running Reelfoot Bayou was determined using a standard stage-discharge rating curve. Discharge measured using an acoustic Doppler current profiler was used to calibrate gate-discharge equations for free and submerged orifice flow at the new structure (Heal and others, 2022). Two lake operation models, one for the summer season and another for the winter season, are provided for the new structure based on data from this period. The summer operation model is based on operation of the gates once the lake level exceeds an elevation of 282.7 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Free flow begins when lake level reaches 282.3 ft above NAVD 88 and becomes transitional once the lake level exceeds 282.8 ft above NAVD 88. Submerged flow begins once the lake level reaches 283 ft above NAVD 88 and the tail-water depth is above critical flow depth. The winter operation model is based on operation of the gates once the lake level exceeds 283.2 ft above NAVD 88. Submerged flow begins when the lake rises to an elevation of 283.5 ft above NAVD 88 and the tail-water depth is above critical flow depth (Heal and others, 2022).
DOI: 10.3886/e180001
title: STEM Liaison Librarianship Survey

description: The problem this study addresses is the literature gap of liaison librarians’ perspectives on their duties and the frequency of which they perform these duties for academic researchers. Liaison librarians’ roles have broadened with new policies, data, and services stemming from data-driven research across sciences. STEM liaison librarians from a sampling frame of 135 at 16 similarly Association of Research Libraries were surveyed with demographic and job analysis questions. A total of 41 participants completed the entirety of the survey. Findings indicate the majority of our respondents serving 4 or more departments and continue to spend a majority of their time on traditional liaison tasks, but data-related duties are in greater demand. This study validates past work and concludes with discussion and recommendations for staffing and future research of STEM liaison librarians.
description: Response Rates: A total of 41 participants of the 135 completed the entirety of the survey (30.4%).
description: Participants were recruited via email from a sampling frame of current STEM liaison librarians in January 2022. The sampling frame included 135 STEM liaison librarians from 16 institutions. Participants were sent a recruitment email explaining the purpose of the study and a link to the survey. The survey landing page was the IRB approved consent form and the only incentive for participation was a better understanding of these jobs to inform training in this area. The recruitment email and consent form contained information including the researcher's contact information and study details including potential benefits and risks of the study, confidentiality of participants, anonymization of all data collection, and completely voluntary nature of participation. The sampling frame was developed using a list of peer-institutions maintained by the researcher's academic library that have similar research expenditures, and other status considerations within the Association of Research Libraries. These academic libraries’ websites were then examined by one of the researchers to determine if their internal college organization was similar to the institution of this research team in terms of number and types of disciplines. If more than two discrepancies were found such as additional or fewer colleges, the institution was removed from the pool. Those remaining then had their number of STEM serving liaisons counted in 2 separate sweeps with libraries containing 4 or more being added to a final list. A total of 16 institutions for a total of 135 STEM liaison librarians was identified.
description: web-based survey; Qualtrics survey software used to collect data and present responses
DOI: 10.3886/e180001v1
title: STEM Liaison Librarianship Survey

description: The problem this study addresses is the literature gap of liaison librarians’ perspectives on their duties and the frequency of which they perform these duties for academic researchers. Liaison librarians’ roles have broadened with new policies, data, and services stemming from data-driven research across sciences. STEM liaison librarians from a sampling frame of 135 at 16 similarly Association of Research Libraries were surveyed with demographic and job analysis questions. A total of 41 participants completed the entirety of the survey. Findings indicate the majority of our respondents serving 4 or more departments and continue to spend a majority of their time on traditional liaison tasks, but data-related duties are in greater demand. This study validates past work and concludes with discussion and recommendations for staffing and future research of STEM liaison librarians.
description: Response Rates: A total of 41 participants of the 135 completed the entirety of the survey (30.4%).
description: Participants were recruited via email from a sampling frame of current STEM liaison librarians in January 2022. The sampling frame included 135 STEM liaison librarians from 16 institutions. Participants were sent a recruitment email explaining the purpose of the study and a link to the survey. The survey landing page was the IRB approved consent form and the only incentive for participation was a better understanding of these jobs to inform training in this area. The recruitment email and consent form contained information including the researcher's contact information and study details including potential benefits and risks of the study, confidentiality of participants, anonymization of all data collection, and completely voluntary nature of participation. The sampling frame was developed using a list of peer-institutions maintained by the researcher's academic library that have similar research expenditures, and other status considerations within the Association of Research Libraries. These academic libraries’ websites were then examined by one of the researchers to determine if their internal college organization was similar to the institution of this research team in terms of number and types of disciplines. If more than two discrepancies were found such as additional or fewer colleges, the institution was removed from the pool. Those remaining then had their number of STEM serving liaisons counted in 2 separate sweeps with libraries containing 4 or more being added to a final list. A total of 16 institutions for a total of 135 STEM liaison librarians was identified.
description: web-based survey; Qualtrics survey software used to collect data and present responses
DOI: 10.17633/rd.brunel.21080746.v2
title: Numerical studies of batch and inline high shear melt conditioning technology using different rotors dataset

description: This collection contains data from the article: G.S.B. Lebon, J.B. Patel, and Z. Fan. Numerical studies of batch and inline high shear melt conditioning technology using different rotors. Crystals.
DOI: 10.17633/rd.brunel.21080746
title: Numerical studies of batch and inline high shear melt conditioning technology using different rotors dataset

description: This collection contains data from the article: G.S.B. Lebon, J.B. Patel, and Z. Fan. Numerical studies of batch and inline high shear melt conditioning technology using different rotors. Crystals.
DOI: 10.17633/rd.brunel.21080746.v1
title: Numerical studies of batch and inline high shear melt condi-tioning technology using different rotors dataset

description: This collection contains data from the article: G.S.B. Lebon, J.B. Patel, and Z. Fan. Numerical studies of batch and inline high shear melt conditioning technology using different rotors. Crystals.
DOI: 10.34729/3jtx-we73
title: Harmonized Mex-Cog, Version A
DOI: 10.6084/m9.figshare.21121243
title: dorylogaster_longipes_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21121243.v1
title: dorylogaster_longipes_zanfona_1.fasta.gz

description: .......
DOI: 10.5281/zenodo.7082473
title: Daily bus transportation demand - São Paulo-Brazil - Jan-2017 to Jun-2022

description: The raw demand data was obtained from SPTRANS, the public company responsible for bus transportation in São Paulo. São Paulo's greatest public transportation network is the bus network, which is mostly managed by public company SPTRANS. SPTRANS provides bus transportation services during business hours and night shifts, along with specific services directed towards impaired and disabled citizens who need support in traveling to hospitals and health centers. SPTRANS makes available online daily mobility data for all bus lines in São Paulo, providing an enormously valuable resource for data mining, urban geography studies, and transportation planning. By using this data, it is possible to effectively study the impacts of pandemics on São Paulo's bus transportation network and gain insights into the regional differences and if different kinds of lines were affected differently. The data is distributed in xlxs format and contains information about line name, demand by kind of users, such as users who pay with money, users who use travel cards, elderly users (who do not need to pay for travel), and total demand. The line name is a string containing a code made of letters and numbers. Demand data between January/2017 and June/2022 were automatically downloaded and accessed by a script in python, using pandas library for creating dataframes. There were significant challenges in using SPTRANS data, such as the irregular formatting of row and column names through the years, inconsistent abbreviations of keywords, such as terminal stations and metro stations (i.e "term.","terminal","metr","m", etc). These issues were solved by capturing the code and crossing the data with General Transit Feed Specification (GTFS) data, which is provided by SPTRANS as well on an almost weekly basis and contains information about route and schedule of lines for a given time period. This step enriched the data with standardized names and route points. Another difficulty in obtaining the data through automatic scripts is that Links do not have regular naming patterns. A significant number of downloaded XLSX files came with a bad configuration so that empty rows are read by pandas as filled with content, triggering an error related to a prohibitively large number of rows. This bug was solved by simply individually opening it and re-saving it. The details about the origin of this error are not understood by the authors. All the bus lines were enriched with geospatial information, containing all points belonging to each route, extracted from GTFS. After all these steps, the data was organized in a matrix where each row represents a day and each column represents a line. The geospatial information was stored separately. And since bus lines are created or deactivated with some regularity in São Paulo, The choice was to keep in the sample only the lines that remained active throughout the observation period.
DOI: 10.5281/zenodo.7082436
title: Daily bus transportation demand - São Paulo-Brazil - Jan-2017 to Jun-2022

description: The raw demand data was obtained from SPTRANS, the public company responsible for bus transportation in São Paulo. São Paulo's greatest public transportation network is the bus network, which is mostly managed by public company SPTRANS. SPTRANS provides bus transportation services during business hours and night shifts, along with specific services directed towards impaired and disabled citizens who need support in traveling to hospitals and health centers. SPTRANS makes available online daily mobility data for all bus lines in São Paulo, providing an enormously valuable resource for data mining, urban geography studies, and transportation planning. By using this data, it is possible to effectively study the impacts of pandemics on São Paulo's bus transportation network and gain insights into the regional differences and if different kinds of lines were affected differently. The data is distributed in xlxs format and contains information about line name, demand by kind of users, such as users who pay with money, users who use travel cards, elderly users (who do not need to pay for travel), and total demand. The line name is a string containing a code made of letters and numbers. Demand data between January/2017 and June/2022 were automatically downloaded and accessed by a script in python, using pandas library for creating dataframes. There were significant challenges in using SPTRANS data, such as the irregular formatting of row and column names through the years, inconsistent abbreviations of keywords, such as terminal stations and metro stations (i.e "term.","terminal","metr","m", etc). These issues were solved by capturing the code and crossing the data with General Transit Feed Specification (GTFS) data, which is provided by SPTRANS as well on an almost weekly basis and contains information about route and schedule of lines for a given time period. This step enriched the data with standardized names and route points. Another difficulty in obtaining the data through automatic scripts is that Links do not have regular naming patterns. A significant number of downloaded XLSX files came with a bad configuration so that empty rows are read by pandas as filled with content, triggering an error related to a prohibitively large number of rows. This bug was solved by simply individually opening it and re-saving it. The details about the origin of this error are not understood by the authors. All the bus lines were enriched with geospatial information, containing all points belonging to each route, extracted from GTFS. After all these steps, the data was organized in a matrix where each row represents a day and each column represents a line. The geospatial information was stored separately. And since bus lines are created or deactivated with some regularity in São Paulo, The choice was to keep in the sample only the lines that remained active throughout the observation period.
DOI: 10.5281/zenodo.7082807
title: The Gloria2 fish farm escapes identification dataset (v1.0)

description: This is version 1.0 of the <strong>GLORiA<sup>2</sup> dataset</strong>, which can be used for the training of computer vision models aimed at <strong>detecting fish farm escapes caused by sea storms</strong>. Escaped fish can be recognized based on their appearance from pictures taken in lab conditions, when compared to farmed and wild specimens. Several images of fish specimens raised in <strong>captivity</strong> (C), farm <strong>escapes</strong> (E), and the <strong>sea</strong> (S) are provided for each of the <strong>three</strong> considered commercial <strong>species</strong>: Mediterranean <strong>sea bream</strong> (<em>S. aurata</em>), <strong>sea bass</strong> (<em>D. labrax</em>), and <strong>meagre</strong> (<em>A. regius</em>). The images provided are 224x224 crops of the original images, centred around the fish in the image, as <strong>preprocessed</strong> by the <strong>accompanying Python scripts</strong>. <strong>Acknowledgement: </strong>GLORiA<sup>2</sup> is a project supported by the Biodiversity Foundation of the Ministry for the Ecological Transition, through the FEMP Pleamar Programme. It is also part of the LIFE IP INTEMARES project "Integrated, innovative and participatory management of the Natura 2000 Network in the Spanish marine environment", coordinated by the Ministry, through the Biodiversity Foundation.