cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e185741
title: Data and Code for: Confidence, Self-Selection and Bias in the Aggregate

description: The influence of behavioral biases on aggregate outcomes depends in part on self-selection: whether rational people opt more strongly into aggregate interactions than biased individuals. In betting market, auction and committee experiments, we document that some errors are strongly reduced through self-selection, while others are not affected at all or even amplified. A large part of this variation is explained by differences in the relationship between confidence and performance. In some tasks, they are positively correlated, such that self-selection attenuates errors. In other tasks, rational and biased people are equally confident, such that self-selection has no effects on aggregate quantities.
DOI: 10.3886/e185741v1
title: Data and Code for: Confidence, Self-Selection and Bias in the Aggregate

description: The influence of behavioral biases on aggregate outcomes depends in part on self-selection: whether rational people opt more strongly into aggregate interactions than biased individuals. In betting market, auction and committee experiments, we document that some errors are strongly reduced through self-selection, while others are not affected at all or even amplified. A large part of this variation is explained by differences in the relationship between confidence and performance. In some tasks, they are positively correlated, such that self-selection attenuates errors. In other tasks, rational and biased people are equally confident, such that self-selection has no effects on aggregate quantities.
DOI: 10.34740/kaggle/dsv/5973224
title: Balochi Language to English Translation Dataset
DOI: 10.5281/zenodo.8009878
title: UHI dataset

description: These files contains dataset of spatial nocturnal Urban Heat Island (UHI) intensities for 45 French urban agglomerations, at a horizontal resolution of 250 m. The urban influence on air temperature at 2 m above ground level was obtained by coupling the mesoscale atmospheric model Meso-NH with the land surface model SURFEX-TEB. For each agglomeration, two specific local weather situations that favour the development of a strong UHI in summer are simulated and described in a specfic sheet. Simulation results have been postprocessed to 1) identify the time of day when the UHI is the most developed, 2) to merge information from both meteorological situations in order to obtain one synthetic UHI map and 3) a geographical analysis that allows to classify each city among five spatial UHI classes (Concentrated Very High Intensity; Concentrated High Intensity ; Limited Intensity; Dispersed High Intensity; and Dispersed Cool Zones). This dataset can therefore be used for several purposes, from the analysis at the scale of a city to the comparison of the urban agglomerations among them.
DOI: 10.6084/m9.figshare.23542116
title: S1_Table_Supporting Data.pdf

description: This dataset is compounded by demographic, clinical, pain-related, biochemical, and EEG brain connectivity measures.
DOI: 10.5281/zenodo.7871692
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Mac Alford, https://orcid.org/0000-0002-3448-9735. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.7871691
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Mac Alford, https://orcid.org/0000-0002-3448-9735. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.7884577
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Mac Alford, https://orcid.org/0000-0002-3448-9735. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.7909363
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Mac Alford, https://orcid.org/0000-0002-3448-9735. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.8057042
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Mac Alford, https://orcid.org/0000-0002-3448-9735. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.8057056
title: Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems

description: Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.
DOI: 10.5281/zenodo.8057055
title: Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems

description: Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.
DOI: 10.5281/zenodo.7805311
title: Jacob_et_al_2023_DevCell

description: Data files for the scRNA-seq analysis performed for Jacob et al., 2023, Developmental Cell. Included are:<br> (A) Input files required for running the All/FIB/EPI notebooks (incl. expression matrices, loom files, metadata, etc.)<br> (B) Input files required for running the Receptor-Ligand-Pairing notebook<br> (C) H5 files containing the relevant output of the All/FIB/EPI notebooks<br> (D) UMAP coordinates for All/FIB/EPI<br> (E) Cluster assignment for all cells included in the final analysis Associated data analysis Jupyter notebooks can be found on kasperlab github page: https://github.com/kasperlab/Jacob_et_al_2023_Developmental_Cell Associated raw data and expression matrices can be found on ArrayExpress (E-MTAB-11920)
DOI: 10.5281/zenodo.7805310
title: Jacob_et_al_2023_DevCell

description: Data files for the scRNA-seq analysis performed for Jacob et al., 2023, Developmental Cell. Included are:<br> (A) Input files required for running the All/FIB/EPI notebooks (incl. expression matrices, loom files, metadata, etc.)<br> (B) Input files required for running the Receptor-Ligand-Pairing notebook<br> (C) H5 files containing the relevant output of the All/FIB/EPI notebooks<br> (D) UMAP coordinates for All/FIB/EPI<br> (E) Cluster assignment for all cells included in the final analysis Associated data analysis Jupyter notebooks can be found on kasperlab github page: https://github.com/kasperlab/Jacob_et_al_2023_Developmental_Cell Associated raw data and expression matrices can be found on ArrayExpress (E-MTAB-11920)
DOI: 10.25345/c5445hp25
title: MassIVE MSV000092208 - Endocytic vesicles act as vehicles for glucose uptake in response to growth factor stimulation
title: Glycolysis is a fundamental cellular process, yet its regulatory mechanisms remain incompletely understood. Here, we show that a subset of glucose transporter 1 (GLUT1/SLC2A1) co-endocytoses with platelet-derived growth factor (PDGF) receptor (PDGFR) upon PDGF-stimulation. LC-MS was used to characterize the isolates from cells stimulated with PDGF-BB-conjugated nanoparticles (endocytic vesicle fraction) or PDGF-BB plus unconjugated nanoparticles (control). Taken together, multiple glycolytic enzymes localize to these endocytosed PDGFR/GLUT1-containing vesicles adjacent to mitochondria. Contrary to current models, which emphasize the importance of glucose transporters on the cell surface, we find that PDGF-stimulated glucose uptake depends on receptor/transporter endocytosis. Our results suggest that growth factors generate glucose-loaded endocytic vesicles that deliver glucose to the glycolytic machinery in proximity to mitochondria, and argue for a new layer of regulation for glycolytic control governed by cellular membrane dynamics.
DOI: 10.25345/c50c4sv8d
title: MassIVE MSV000092209 - 06019023-Mobley-collaborative-project_new_method_pos
title: Proteus mirabilis WGLW4 were cultured, supernatant was extracted using HLB-SPE and 80% MeOH/water and metabolome was analyzed with QExactive HF. [doi:10.25345/C5NP1WV14] [dataset license: CC0 1.0 Universal (CC0 1.0)] New method, positive
DOI: 10.6084/m9.figshare.19281761.v4
title: SMURPHS OHC dataset

description: <strong>Data produced by analysis of the SMURPHS ensemble model output, as described in Boland et al 2023 (https://doi.org/10.1029/2022JC018725). This data is required to reproduce the figures from this paper.</strong> <br> See https://doi.org/10.5281/zenodo.6418479 for: -notebooks to produce the figures from Boland et al 2023 using this data - see below for which tar balls are needed for which figure.<br> -code to reproduce this data from the SMURPHS model output<br> <br> To reproduce the figures, you need the following directories/files: - Figure 1 & Table 2: ohc_tseries, pic_data, other_model_data - Figure 2: ohc_tseries, pic_data, other_model_data - Figure 3: ohc_trends - Figures 4, S2, S3: ohc_xy - Figures 5, S4-S7: ohc_yz - Figure 6: ohc_xy - Figure 7: ohc_yz, other_model_data - Figure S1: pic_data - Figure S8: amoc_tseries - Figure S9: SIE_SH.nc <br> The data files loaded were created using the python scripts in https://github.com/emmomp/SMURPHS_OHC/code/ as follows: - ohc_tseries: ohc_by_basin_depth.py<br> - pic_data: ohc_by_basin_depth_pic.py, ohc_pic_drift.py, ohc_xy_pic_drift.py, ohc_yz_pic_drift.py, ohc_xy_pic.py, ohc_yz_pic.py - ohc_trends: ohc_weightedtrends_obs.py, ohc_weightedtrends.py - ohc_xy: ohc_xy.py, ohc_xy_trends.py - ohc_yz: ohc_yz.py, ohc_yz_trends.py - amoc_tseries: calculate_AMOC.py - SIE_SH.nc : calc_SH_SIE.py <br> For the SMURPHS ensemble, see Dittus et al. 2020 (https://doi.org/10.1029/2019GL085806) <br> <br>
DOI: 10.5281/zenodo.7871760
title: Data - Positive and Negative Affect Schedule in early COVID-19 pandemic

description: Data of a Positive and Negative Affect Schedule (PANAS) study done in early COVID-19 pandemic. The dataset has 4 sheets. The first one and the second one have 334 rows and 45 columns. The third and the fourth have 87 rows and 2 columns. Note that the first sheet includes a comment about the data in column 5, rows 336-341. Sheets 1 and 2 present the same information, but in different language: sheet one is in English and sheet 2 is in Spanish. Sheets 3 and 4 also have the same content, again: sheet 3 in English and sheet 4 in Spanish. SPANISH: Datos de estudio con escala Positive and Negative Affect Schedule (PANAS) realizado en los inicio de la pandemia COVID-19. El dataset incluye 4 hojas. La primera y la segunda contienen 334 filas y 45 columnas. La tercera y la cuarta tienen 87 filas y 2 columnas. Nótese que la primera hoja incluye un comentario sobre los datos en la columna 5, filas 336-341. Las hojas 1 y 2 contienen la misma información, pero en distinto idioma: la hoja 1 en inglés y la hoja 2 en español. Las hojas 3 y 4 también contienen la misma información, de nuevo: la hoja 3 en inglés y la hoja 4 en español.
DOI: 10.5281/zenodo.7871761
title: Data - Positive and Negative Affect Schedule in early COVID-19 pandemic

description: Data of a Positive and Negative Affect Schedule (PANAS) study done in early COVID-19 pandemic.
DOI: 10.6084/m9.figshare.19281761.v6
title: SMURPHS OHC dataset

description: <strong>Data produced by analysis of the SMURPHS ensemble model output, as described in Boland et al 2023 (https://doi.org/10.1029/2022JC018725). This data is required to reproduce the figures from this paper.</strong> <br> See https://doi.org/10.5281/zenodo.6418479 for: -notebooks to produce the figures from Boland et al 2023 using this data - see below for which tar balls are needed for which figure.<br> -code to reproduce this data from the SMURPHS model output<br> <br> To reproduce the figures, you need the following directories/files: - Figure 1 & Table 2: ohc_tseries, pic_data, other_model_data - Figure 2: ohc_tseries, pic_data, other_model_data - Figure 3: ohc_trends - Figures 4, S2, S3: ohc_xy - Figures 5, S4-S7: ohc_yz - Figure 6: ohc_xy - Figure 7: ohc_yz, other_model_data - Figure S1: pic_data - Figure S8: amoc_tseries - Figure S9: SIE_SH.nc <br> The data files loaded were created using the python scripts in https://github.com/emmomp/SMURPHS_OHC/code/ as follows: - ohc_tseries: ohc_by_basin_depth.py<br> - pic_data: ohc_by_basin_depth_pic.py, ohc_pic_drift.py, ohc_xy_pic_drift.py, ohc_yz_pic_drift.py, ohc_xy_pic.py, ohc_yz_pic.py - ohc_trends: ohc_weightedtrends_obs.py, ohc_weightedtrends.py - ohc_xy: ohc_xy.py, ohc_xy_trends.py - ohc_yz: ohc_yz.py, ohc_yz_trends.py - amoc_tseries: calculate_AMOC.py - SIE_SH.nc : calc_SH_SIE.py <br> For the SMURPHS ensemble, see Dittus et al. 2020 (https://doi.org/10.1029/2019GL085806) <br> <br>
DOI: 10.6084/m9.figshare.19281761.v5
title: SMURPHS OHC dataset

description: <strong>Data produced by analysis of the SMURPHS ensemble model output, as described in Boland et al 2023 (https://doi.org/10.1029/2022JC018725). This data is required to reproduce the figures from this paper.</strong> <br> See https://doi.org/10.5281/zenodo.6418479 for: -notebooks to produce the figures from Boland et al 2023 using this data - see below for which tar balls are needed for which figure.<br> -code to reproduce this data from the SMURPHS model output<br> <br> To reproduce the figures, you need the following directories/files: - Figure 1 & Table 2: ohc_tseries, pic_data, other_model_data - Figure 2: ohc_tseries, pic_data, other_model_data - Figure 3: ohc_trends - Figures 4, S2, S3: ohc_xy - Figures 5, S4-S7: ohc_yz - Figure 6: ohc_xy - Figure 7: ohc_yz, other_model_data - Figure S1: pic_data - Figure S8: amoc_tseries - Figure S9: SIE_SH.nc <br> The data files loaded were created using the python scripts in https://github.com/emmomp/SMURPHS_OHC/code/ as follows: - ohc_tseries: ohc_by_basin_depth.py<br> - pic_data: ohc_by_basin_depth_pic.py, ohc_pic_drift.py, ohc_xy_pic_drift.py, ohc_yz_pic_drift.py, ohc_xy_pic.py, ohc_yz_pic.py - ohc_trends: ohc_weightedtrends_obs.py, ohc_weightedtrends.py - ohc_xy: ohc_xy.py, ohc_xy_trends.py - ohc_yz: ohc_yz.py, ohc_yz_trends.py - amoc_tseries: calculate_AMOC.py - SIE_SH.nc : calc_SH_SIE.py <br> For the SMURPHS ensemble, see Dittus et al. 2020 (https://doi.org/10.1029/2019GL085806) <br> <br>
DOI: 10.5281/zenodo.8057132
title: Dissemination of "Disruptive Innovation" and "Lean Startup" in Google Scholar and Scopus

description: Number of documents represented in Google Scholar and Scopus for "Disruptive Innovation" and "Lean Startup" in different years.
DOI: 10.5281/zenodo.8057131
title: Dissemination of "Disruptive Innovation" and "Lean Startup" in Google Scholar and Scopus

description: Number of documents represented in Google Scholar and Scopus for "Disruptive Innovation" and "Lean Startup" in different years.
DOI: 10.22003/xfel.eu-data-003494-00
title: Structural rearrangements during de-excitation of a self-trapped exciton in vitreous silica (derived from 2853)
DOI: 10.22003/xfel.eu-data-005796-00
title: In-situ Study of Ligand Binding Mechanisms using LCP Mixer
DOI: 10.5281/zenodo.8057216
title: GSD Sample Data - Extra Small

description: GSD Open Source Sample Data as found in the Beeline software package but has been reduced to only a 100 cells. It was likely generated using a Boolean Network method described in Ríos, O., Frias, S., Rodríguez, A. <em>et al.</em> (2015). All gene names have been normalized to their NCBI identifiers and any NCBI IDs that contain parentheses such as NCBI:12796(-KTS) or NCBI:12796(+KTS) denoted a sequence variant described by the string within the parentheses.
DOI: 10.5281/zenodo.8057215
title: GSD Sample Data - Extra Small

description: GSD Open Source Sample Data as found in the Beeline software package but has been reduced to only a 100 cells. It was likely generated using a Boolean Network method described in Ríos, O., Frias, S., Rodríguez, A. <em>et al.</em> (2015). All gene names have been normalized to their NCBI identifiers and any NCBI IDs that contain parentheses such as NCBI:12796(-KTS) or NCBI:12796(+KTS) denoted a sequence variant described by the string within the parentheses.
DOI: 10.3886/e192267
title: Data and Codes for 'Charitable giving role-modeling: parent transmission frequency and adolescent reception'

description: Experiments indicate that adult role-modeling of giving has a causal effect on giving done by children, but a previous investigation using data from a natural setting suggests zero causal effect of parental role-modeling on their adolescents’ giving. This paper presents new evidence about the divergent findings: (1) parental giving does not automatically translate into an adolescent knowing that their parent gives, and (2) adolescents are much less likely to know that their parent gives if the parent gives from time-to-time. The results suggest new experimental designs that randomize the frequency of role-modeling, communication approaches that explain role-modeling actions to children, and whether the receiving organization is in-group or out-group. Practical implications of the results are that frequent giving by a parent is necessary for adolescents to successfully ‘receive’ the role model, but may not be sufficient. Purposeful communication is needed to ensure that adolescents know their parent is giving.
DOI: 10.3886/e192267v1
title: Data and Codes for 'Charitable giving role-modeling: parent transmission frequency and adolescent reception'

description: Experiments indicate that adult role-modeling of giving has a causal effect on giving done by children, but a previous investigation using data from a natural setting suggests zero causal effect of parental role-modeling on their adolescents’ giving. This paper presents new evidence about the divergent findings: (1) parental giving does not automatically translate into an adolescent knowing that their parent gives, and (2) adolescents are much less likely to know that their parent gives if the parent gives from time-to-time. The results suggest new experimental designs that randomize the frequency of role-modeling, communication approaches that explain role-modeling actions to children, and whether the receiving organization is in-group or out-group. Practical implications of the results are that frequent giving by a parent is necessary for adolescents to successfully ‘receive’ the role model, but may not be sufficient. Purposeful communication is needed to ensure that adolescents know their parent is giving.
DOI: 10.15468/dl.yatmu3
title: Occurrence Download

description: A dataset containing 157 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Clevosauridae"
 ]
}

 The dataset includes 157 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060174-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jkqfcs
title: Occurrence Download

description: A dataset containing 415 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Eleutherococcus sieboldianus (Makino) Koidz."
 ]
}

 The dataset includes 415 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054733-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mlfkmj
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gymnoschoenus anceps (R.Br.) C.B.Clarke"
 ]
}

 The dataset includes 103 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002880-160509122628363/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uo8gmv
title: Occurrence Download

description: A dataset containing 11292 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Pyrisitia proterpia (Fabricius, 1775)"
 ]
}

 The dataset includes 11292 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034621-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3fvgnn
title: Occurrence Download

description: A dataset containing 43443 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is rmca-albertine-rift-birds"
 ]
}

 The dataset includes 43443 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002880-160822134323880/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jcsbc4
title: Occurrence Download

description: A dataset containing 421 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "OccurrenceStatus is Present",
 "TaxonKey is Philesiaceae"
 ]
}

 The dataset includes 421 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054042-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ftizib
title: Occurrence Download

description: A dataset containing 74 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 3) Abundance of benthic foraminifera and ostracoda in bottom sediments from the Ashadze-1 Hydrothermal Field"
 ]
}

 The dataset includes 74 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002880-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ywghja
title: Occurrence Download

description: A dataset containing 111572 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alces Gray, 1821"
 ]
}

 The dataset includes 111572 records from 219 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049966-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5pa4w0
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brachythecium falcatulum (Broth.) Paris"
 ]
}

 The dataset includes 9 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002880-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.