cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e190484v1-145520
title: Code for: Stereotypes About Successful Entrepreneurs

description: This is code accompanying the article "Stereotypes About Successful Entrepreneurs". The article asks what comes to mind when thinking about a successful entrepreneur. Belief formation models suggest that what comes to mind is an oversimplified picture of the characteristics of successful entrepreneurs, i.e, stereotypes about successful entrepreneurs. Using French administrative data on 48,767 new firms, we show that some characteristics are stereotypical of success and have distributions that can generate miscalibrated beliefs. To illustrate how stereotypical thinking can lead to biased assessments, we report the discrepancies between the implied fraction of successful entrepreneurs under Bayesian vs. stereotypical thinking for several stereotypes. We discuss the consequences of stereotyping for venture capital allocation.
DOI: 10.5281/zenodo.8197702
title: Validation dataset and results for article "Challenges in real-time generation of scintillation index maps"

description: The complete evaluation table for all satellites of all constellations of each station is available in the file “Validation of the GNSS data acquisition.csv”.

The validation datasets, including real-time (rt.csv.gz extension) and post-processed (ismr.csv.gz) data for each monitoring GNSS stations is available in the remaining compressed files.
DOI: 10.5281/zenodo.8197703
title: Validation dataset and results for article "Challenges in real-time generation of scintillation index maps"

description: The complete evaluation table for all satellites of all constellations of each station is available in the file “Validation of the GNSS data acquisition.csv”.

The validation datasets, including real-time (rt.csv.gz extension) and post-processed (ismr.csv.gz) data for each monitoring GNSS stations is available in the remaining compressed files.
DOI: 10.7931/d88x-s544
title: Shrubland Fragments - vegetation data
DOI: 10.6084/m9.figshare.24133086.v1
title: example_data.zip

description: The data is an example dataset for mouse embryo viability assay presented in "<b>EVATOM: a novel embryo health assessment tool</b>." It contains three folders: <i>healthy</i>: a H/I class embryo,<i> sick:</i> a S class embryo and <i>sparse_prediction</i>: for demonstrating sparse prediction functionality on select z-slices of live embryo. Healthy and sick folders, each have two subfolders: <i>'cropped'</i>-containing LS-GLIM quantitative phase images and <i>'new'</i>-containing corresponding nucleus predictions from NPM. <i>Sparse_prediction</i> folder has select z-slices in <i>'overlapped'</i> folder with raw file needed for IBM prediction <i>'unknown.csv' </i>and IBM prediction example<i> 'check.csv'</i>.MATLAB app for demonstration of EVATOM is also provided.modelp99.pth is the trained IBMfinal_model.h5 is the trained NPMmodel_94202.mat is the trained FBM<br>
DOI: 10.5281/zenodo.10520485
title: Existing Hydropower Assets (EHA) Plant Database 9505 Point of Diversion

description: This dataset is an extension of the Existing Hydropower Assets (EHA) Plant Database, 2021, released by Oak Ridge National Laboratory through HydroSource. The extension enhanced EHA to support modeling performed under the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505). This dataset identifies the spatial locations of the sources of water (point of diversion) for each hydropower facility. The locations are explicit where possible - including headgates, diversion dams, and penstock intakes - and conceptual where needed - including generically at a dam. In some instances, the point of diversion is marked at the main diversion for a canal/conveyance network as the the intent is to identify the source of water on the natural river system. Discretion was used though in identifying these locations, and there are exeptions to the above rule. For example, the point of diversion for hydropower facilities in series, where the tailwater of one supplies the headworks of another, is identified along the conveyance network.

A companion dataset was developed that explicitly identifies the location of the powerhouse for each hydropower facility.

The point of diversion mapping was developed to support the needs of the 9505 project. Every attempt was made to ensure accuracy, however errors may exist. Please note that this dataset was created from the 2021 version of EHA. EHA is updated annually and newer versions may contain more reliable information depending on need.
DOI: 10.5281/zenodo.10520486
title: Existing Hydropower Assets (EHA) Plant Database 9505 Point of Diversion

description: This dataset is an extension of the Existing Hydropower Assets (EHA) Plant Database, 2021, released by Oak Ridge National Laboratory through HydroSource. The extension enhanced EHA to support modeling performed under the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505). This dataset identifies the spatial locations of the sources of water (point of diversion) for each hydropower facility. The locations are explicit where possible - including headgates, diversion dams, and penstock intakes - and conceptual where needed - including generically at a dam. In some instances, the point of diversion is marked at the main diversion for a canal/conveyance network as the the intent is to identify the source of water on the natural river system. Discretion was used though in identifying these locations, and there are exeptions to the above rule. For example, the point of diversion for hydropower facilities in series, where the tailwater of one supplies the headworks of another, is identified along the conveyance network.

A companion dataset was developed that explicitly identifies the location of the powerhouse for each hydropower facility.

The point of diversion mapping was developed to support the needs of the 9505 project. Every attempt was made to ensure accuracy, however errors may exist. Please note that this dataset was created from the 2021 version of EHA. EHA is updated annually and newer versions may contain more reliable information depending on need.
DOI: 10.34740/kaggle/dsv/7459454
title: BBC News
DOI: 10.5517/ccdc.csd.cc2fbb15
title: CCDC 2244246: Experimental Crystal Structure Determination

description: Related Article: Helena Roithmeyer, Laurent Sévery, Thomas Moehl, Bernhard Spingler, Olivier Blacque, Thomas Fox, Marcella Iannuzzi, S. David Tilley|2023|J.Am.Chem.Soc.|146|430|doi:10.1021/jacs.3c09725
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.cc2fbb26
title: CCDC 2244247: Experimental Crystal Structure Determination

description: Related Article: Helena Roithmeyer, Laurent Sévery, Thomas Moehl, Bernhard Spingler, Olivier Blacque, Thomas Fox, Marcella Iannuzzi, S. David Tilley|2023|J.Am.Chem.Soc.|146|430|doi:10.1021/jacs.3c09725
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.cc2fbb37
title: CCDC 2244248: Experimental Crystal Structure Determination

description: Related Article: Helena Roithmeyer, Laurent Sévery, Thomas Moehl, Bernhard Spingler, Olivier Blacque, Thomas Fox, Marcella Iannuzzi, S. David Tilley|2023|J.Am.Chem.Soc.|146|430|doi:10.1021/jacs.3c09725
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.10547704
title: BRAND: A platform for closed-loop experiments with deep network models

description: This is the supporting data for "BRAND: A platform for closed-loop experiments with deep network models". It can be analyzed using the code at github.com/brandbci/paper-figures.

This dataset contains:



Latency measurements from computers running the BRAND software under several different conditions

Example data from a real-time neural data simulator made using BRAND

Cursor kinematics from a participant using BRAND to perform cursor control with an intracortical brain-computer interface in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041)*


Funding: This work was supported by the Emory Neuromodulation and Technology Innovation Center (ENTICe), NIH Eunice Kennedy Shriver NICHD K12HD073945, NIH-NINDS/OD DP2NS127291 (CP), NIH-NIDCD/OD DP2DC021055, Simons Collaborations for the Global Brain Pilot Award 872146SPI (SDS), NIH-NIBIB T32EB025816 (YHA), NIH-NICHD F32HD112173 (SRN), NIH-NIDCD U01DC017844, and Department of Veterans Affairs Rehabilitation Research and Development Service A2295R and N2864C (LRH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, or the Department of Veterans Affairs, or the United States Government.

*CAUTION: Investigational device. Limited by federal law to investigational use.
DOI: 10.5281/zenodo.10547705
title: BRAND: A platform for closed-loop experiments with deep network models

description: This is the supporting data for "BRAND: A platform for closed-loop experiments with deep network models". It can be analyzed using the code at github.com/brandbci/paper-figures.

This dataset contains:



Latency measurements from computers running the BRAND software under several different conditions

Example data from a real-time neural data simulator made using BRAND

Cursor kinematics from a participant using BRAND to perform cursor control with an intracortical brain-computer interface in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041)*


Funding: This work was supported by the Emory Neuromodulation and Technology Innovation Center (ENTICe), NIH Eunice Kennedy Shriver NICHD K12HD073945, NIH-NINDS/OD DP2NS127291 (CP), NIH-NIDCD/OD DP2DC021055, Simons Collaborations for the Global Brain Pilot Award 872146SPI (SDS), NIH-NIBIB T32EB025816 (YHA), NIH-NICHD F32HD112173 (SRN), NIH-NIDCD U01DC017844, and Department of Veterans Affairs Rehabilitation Research and Development Service A2295R and N2864C (LRH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, or the Department of Veterans Affairs, or the United States Government.

*CAUTION: Investigational device. Limited by federal law to investigational use.
DOI: 10.6073/pasta/3a26cb75ad1707a1e8f7215ce626ab34
title: USFWS White Sturgeon Egg Monitoring, San Joaquin River, 2011-2018

description: Overview
 The Central Valley Project Improvement Act (CVPIA) funds habitat improvement work and associated monitoring in the Central Valley of California to increase salmonid populations in furtherance of meeting CVPIA fish doubling goals. This data package contains two datasets for White Sturgeon (Acipenser transmontanus) monitoring in the San Joaquin River (SJR) conducted by the US Fish and Wildlife Service, Lodi Fish and Wildlife Office. The objective of this sampling to was determine if White Sturgeon were spawning in the San Joaquin River and to explore where and when spawning occurred, within areas where adult White Sturgeon were known to congregate during the suspected spawning season.
 
 SJR_Egg_WST_Set Data
 This dataset contains data on egg mat sets used to document White Sturgeon spawning in the San Joaquin River. Sets were made at non-random locations from February to May in 2011-2018. In 2017, additional “blitz” sets were used in areas where eggs were detected. Details about set location, timing, and environmental conditions are included, along with the total number eggs of White Sturgeon and other non-sturgeon eggs.
 
 SJR_Egg_WST_Catch Data
 This dataset contains data specific to eggs found in egg mat nets in the San Joaquin River. Across all years, the diameter of eggs (or groups of eggs) were recorded. In 2011 and 2012, efforts were made to describe the developmental stage of White Sturgeon eggs and estimates of spawning timing were sometimes calculated.
DOI: 10.6073/pasta/2a78ecaa3896d5c12d3b20c6e670427c
title: USFWS Juvenile White Sturgeon Monitoring, San Joaquin River, 2016-2017

description: Overview
 The Central Valley Project Improvement Act (CVPIA) funds habitat improvement work and associated monitoring in the Central Valley of California to increase salmonid populations in furtherance of meeting CVPIA fish doubling goals. This data package contains three datasets for juvenile White Sturgeon (Acipenser transmontanus) monitoring in the San Joaquin River (SJR) conducted by the US Fish and Wildlife Service, Lodi Fish and Wildlife Office. After two years of this experimental sampling program, it was discontinued due to low catches of White Sturgeon.
 
 SJR_Juvenile_WST_Set Data
 This dataset contains data on an experimental sampling program using trammel nets and setlines to catch juvenile White Sturgeon in the San Joaquin River. Sets were made at targeted locations from November-January in 2016 and 2017. One White Sturgeon (1000 mm fork length) was captured in a trammel net in 2016.
 
 SJR_Juvenile_WST_Catch Data
 This dataset contains data for individual fish caught in trammel nets or setlines in the San Joaquin River. Species and fork length were recorded for all fish. For White Sturgeon, girth, maturation, and tag information are provided.
 
 SJR_Fish_Taxonomy Data
 This dataset contains data for fish codes used in the Catch datafile. For each species that was captured, the Species codes are listed with the corresponding Interagency Ecological Program code, common name, taxonomy (Phylum, Class, Order, Family, Genus, and Species), and whether or not the species is native to the region.
DOI: 10.5066/p9gyxcpg
title: Dead Tree Detection Validation Data from Sequoia and Kings Canyon National Parks

description: Most of these data were collected in order to create a database of tree locations for use in calibrating remote sensing tools and products, particularly dead tree detection tools and canopy species maps. Data include tree locations, species identification, and status (live, dead, and, if dead, sometimes includes information on foliage and twig retention). They are a collection of different sampling efforts performed over several years, starting in a period of severe drought mortality. One csv table is included that shows data and validation results for an additional dataset that was used to test the NAIP derived dead tree detection model that is associated with this data release. Locations are not included for that dataset.

These data support the following publication:
Cheng, Y., Oehmcke, S., Brandt, M., Rosenthal, L., Das, A., Vrieling, A., Saatchi, S., Wagner, F., Mugabowindekwe, M., Verbruggen, W. and Beier, C., 2024. Scattered tree death contributes to substantial forest loss in California. Nature Communications, 15(1), p.641.  https://doi.org/10.1038/s41467-024-44991-z
DOI: 10.3886/e193588v2
title: Relationship Between Smartphone Usage and Sleep Quality & Mental Health of College Students During the COVID-19 Pandemic

description: The study explored the impact of smartphone usage patterns on sleep quality and mental health among 508 college students during the COVID-19 pandemic via PSQI. Findings showed no gender difference in sleep disorders. Activities like watching videos, gaming, and studying on phones were linked to higher sleep disturbances. Usage exceeding 30 minutes weekly and frequency over 4 times a week showed significant sleep issues. Activities like music, chatting, and social interactions had minimal sleep impact. The research emphasizes the need for strategies promoting healthy phone use and awareness of its effects on sleep and mental health.
DOI: 10.3886/e193588v1
title: Relationship Between Smartphone Usage and Sleep Quality & Mental Health of College Students During the COVID-19 Pandemic

description: The study explored the impact of smartphone usage patterns on sleep quality and mental health among 508 college students during the COVID-19 pandemic via PSQI. Findings showed no gender difference in sleep disorders. Activities like watching videos, gaming, and studying on phones were linked to higher sleep disturbances. Usage exceeding 30 minutes weekly and frequency over 4 times a week showed significant sleep issues. Activities like music, chatting, and social interactions had minimal sleep impact. The research emphasizes the need for strategies promoting healthy phone use and awareness of its effects on sleep and mental health.
DOI: 10.3886/e193588v1-141761
title: Relationship Between Smartphone Usage and Sleep Quality & Mental Health of College Students During the COVID-19 Pandemic

description: The study explored the impact of smartphone usage patterns on sleep quality and mental health among 508 college students during the COVID-19 pandemic via PSQI. Findings showed no gender difference in sleep disorders. Activities like watching videos, gaming, and studying on phones were linked to higher sleep disturbances. Usage exceeding 30 minutes weekly and frequency over 4 times a week showed significant sleep issues. Activities like music, chatting, and social interactions had minimal sleep impact. The research emphasizes the need for strategies promoting healthy phone use and awareness of its effects on sleep and mental health.
DOI: 10.3886/e193588v3
title: Relationship Between Smartphone Usage and Sleep Quality & Mental Health of College Students During the COVID-19 Pandemic

description: The study explored the impact of smartphone usage patterns on sleep quality and mental health among 508 college students during the COVID-19 pandemic via PSQI. Findings showed no gender difference in sleep disorders. Activities like watching videos, gaming, and studying on phones were linked to higher sleep disturbances. Usage exceeding 30 minutes weekly and frequency over 4 times a week showed significant sleep issues. Activities like music, chatting, and social interactions had minimal sleep impact. The research emphasizes the need for strategies promoting healthy phone use and awareness of its effects on sleep and mental health.
DOI: 10.26180/23930067
title: Fulton & Wansbrough et al, 2023_Short-term fasting of a single amino acid extends lifespan

description: Primary data to accompany Fulton et al, 2023 submission to BioRxiv
DOI: 10.26180/23930067.v2
title: Fulton & Wansbrough et al, 2023_Short-term fasting of a single amino acid extends lifespan

description: Primary data to accompany Fulton et al, 2023 submission to BioRxiv
DOI: 10.26180/23930067.v1
title: Short-term fasting of a single amino acid extends lifespan_Fulton & Wansbrough et al, 2023

description: Primary data to accompany Fulton et al, 2023 submission to BioRxiv
DOI: 10.34740/kaggle/dsv/7459729
title: WallStreetBets 2022
DOI: 10.25545/l4yg1l/ahrbpg
title: Supplementary_Data_Table_S1.tab

description: :unav
DOI: 10.25545/l4yg1l/cblylc
title: Supplementary_Data_Figure_S1.pdf

description: :unav
DOI: 10.25545/l4yg1l
title: Chemical and geochronological data for the Park Spur pluton, Cape Breton Island, Nova Scotia

description: These Excel and pdf image files are the supplementary data files that accompany the publication for which the full citation is given below. The Park Spur pluton was emplaced in the Aspy terrane of the central Cape Breton Highlands at 374.2 ± 2.9 Ma (U–Pb zircon). It consists mainly of muscovite-biotite monzogranite with small areas of garnet-bearing muscovite monzogranite and abundant pegmatite and aplite dykes. On its northern margin the pluton intruded metamorphic rocks of the Ordovician–Silurian Cape North Group and associated orthogneiss and on the south it intruded low-grade metamorphic rocks of the Silurian Calumruadh Brook Formation. Deformation along the southern margin of the Park Spur pluton is consistent with emplacement during dextral transpression between the Bras d’Or and Aspy terranes. The Late Devonian age combined with S-type petrological characteristics show that the Park Spur pluton and nearby Canal pluton are related to the ca. 375 Ma Black Brook Granitic Suite, all interpreted to have been emplaced during later stages of the Acadian orogeny in Ganderia as a result of delamination.
DOI: 10.25545/l4yg1l/qkwxus
title: Supplementary_Data_Table_S2.tab

description: :unav
DOI: 10.5281/zenodo.10553534
title: EBSD data
DOI: 10.5517/ccdc.csd.cc2grcxj
title: CCDC 2286588: Experimental Crystal Structure Determination

description: Related Article: Xiaoli Lin, Panke Zhou, Yiqun Gao, Tao Li, Xiong Chen, Haohong Li, Rong Jiang, Zhirong Chen, Huidong Zheng|2023|Inorg.Chem.|63|775|doi:10.1021/acs.inorgchem.3c03787
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.cc2gw8yl
title: CCDC 2290340: Experimental Crystal Structure Determination

description: Related Article: Pragyan J. Hazarika, Poonam Gupta, Anilkumar Gunnam, Ranjit Thakuria, Ashwini K. Nangia, Naba K. Nath|2023|Cryst.Growth Des.|24|763|doi:10.1021/acs.cgd.3c01193
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.cc2gw8zm
title: CCDC 2290341: Experimental Crystal Structure Determination

description: Related Article: Pragyan J. Hazarika, Poonam Gupta, Anilkumar Gunnam, Ranjit Thakuria, Ashwini K. Nangia, Naba K. Nath|2023|Cryst.Growth Des.|24|763|doi:10.1021/acs.cgd.3c01193
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.cc2gw90p
title: CCDC 2290342: Experimental Crystal Structure Determination

description: Related Article: Pragyan J. Hazarika, Poonam Gupta, Anilkumar Gunnam, Ranjit Thakuria, Ashwini K. Nangia, Naba K. Nath|2023|Cryst.Growth Des.|24|763|doi:10.1021/acs.cgd.3c01193
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.