cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17654451
title: Mass Cytometry (CyTOF) profiling of baseline influenza virus-reactive T-cells in a Controlled Human Influenza Challenge (CHIM) study

description: This dataset contains the raw mass cytometry (CyTOF) files associated with the publication: "Innate antiviral readiness drives the expansion of protective T stem cell memory against influenza."

To determine the clinical relevance of T stem cell-like memory (TSCM) cells, this study analyzed an independent Controlled Human Influenza Challenge (CHIM) cohort (n = 9). This validation cohort was used to investigate the role of memory CD8+ T-cell subsets in host defense, in which baseline memory CD8 T-cell frequencies were evaluated as predictors of rapid viral load clearance, distinct from antibody-mediated symptom mitigation.

Experimental Design The dataset includes paired .fcs files for 9 individuals at the pre-infection baseline timepoint. Each subject has files for two conditions:





Unstimulated Controls: Matched samples processed in parallel without peptide stimulation.




Antigen Stimulation: Thawed PBMCs stimulated for 12-16 hours with a comprehensive peptide pool library spanning the entire proteome of influenza A/California/07/2009, consisting of 483 20-mers and 24 9-mers.



Methodology





Panel: Cells were stained with a 36-marker metal-labeled antibody panel designed to distinguish T-cell memory subsets (including TSCM) and intracellular cytokine production. The workflow included surface labeling, fixation (Maxpar Fix I), permeabilization (Maxpar Perm Buffer), and intracellular staining.




Acquisition: Data were acquired on a Fluidigm Helios mass cytometer.




Analysis Software: Data were analyzed initially using Cytosplore and FlowJo.
DOI: 10.5281/zenodo.17654450
title: Mass Cytometry (CyTOF) profiling of baseline influenza virus-reactive T-cells in a Controlled Human Influenza Challenge (CHIM) study

description: This dataset contains the raw mass cytometry (CyTOF) files associated with the publication: "Innate antiviral readiness drives the expansion of protective T stem cell memory against influenza."

To determine the clinical relevance of T stem cell-like memory (TSCM) cells, this study analyzed an independent Controlled Human Influenza Challenge (CHIM) cohort (n = 9). This validation cohort was used to investigate the role of memory CD8+ T-cell subsets in host defense, in which baseline memory CD8 T-cell frequencies were evaluated as predictors of rapid viral load clearance, distinct from antibody-mediated symptom mitigation.

Experimental Design The dataset includes paired .fcs files for 9 individuals at the pre-infection baseline timepoint. Each subject has files for two conditions:





Unstimulated Controls: Matched samples processed in parallel without peptide stimulation.




Antigen Stimulation: Thawed PBMCs stimulated for 12-16 hours with a comprehensive peptide pool library spanning the entire proteome of influenza A/California/07/2009, consisting of 483 20-mers and 24 9-mers.



Methodology





Panel: Cells were stained with a 36-marker metal-labeled antibody panel designed to distinguish T-cell memory subsets (including TSCM) and intracellular cytokine production. The workflow included surface labeling, fixation (Maxpar Fix I), permeabilization (Maxpar Perm Buffer), and intracellular staining.




Acquisition: Data were acquired on a Fluidigm Helios mass cytometer.




Analysis Software: Data were analyzed initially using Cytosplore and FlowJo.
DOI: 10.15468/dl.u99ve4
title: Occurrence Download

description: A dataset containing 35673 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "OccurrenceStatus is Present",
 "TaxonKey is Bombina variegata"
 ]
}

 The dataset includes 35673 records from 379 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000663-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/w5hww7
title: Darwin Wasps (Hymenoptera, Ichneumonidae) from sub-alpineforests (Baima mountain), Yunnan, China

description: This dataset consists of Darwin Wasps (Hymenoptera, Ichneumonidae) specimens collected from the IEHBR project to understand insect diversity along latitude and elevation in Yunnan Province, China. This dataset includes wasps collected using standardized sampling protocol, Malaise traps from Baima Snow Mountain National Nature Reserve in Diqing Tibetan Autonomous Prefecture, Yunnan, China, during 2018-2019. Sampling was conducted along an elevational transect divided into eight elevational bands separated by 200 m vertical intervals (ranging from 2100 to 3500 m a.s.l ). One Malaise trap was deployed in each elevational band. The arthropod community data obtained through this project was aimed an understanding insects responses for increasing elevation in Eastern Himalaya.
DOI: 10.5281/zenodo.17622300
title: PUDL Raw EIA Bulk API Data

description: All data made available in bulk through the EIA Open Data API, including:



the Annual Energy Outlook, the International Energy Outlook and the Short Term Energy Outlook;

aggregate national, state, and mine-level coal production statistics, including imports and exports, reciepts of coal at electric power plants, consumption and quality, market sales, reserves, and productive capacity;

U.S. electric system operating data;

aggregate national, state, and plant-level electricity generation statistics, including fuel quality and consumption, for grid-connectedplants with nameplate capacity of 1 megawatt or greater;

CO2 emissions aggregates, CO2 emissions and carbon coefficients by fuel, state, and sector;

International Energy System (IES) data containing production, reserves, consumption, capacity, storage, imports, exports, and emissions time series by country for electricity, petroleum, natural gas, coal, nuclear, and renewable energy;

statistics of U.S. natural gas production, imports, exploration, pipelines, exports, prices, consumption, stocks, and reserves;

statistics of U.S. petroleum and other liquid fuel production, imports, refining, exports, prices, consumption, stocks, and reserves;

aggregate national, PADD, state, city, port, and refinery petroleum imports data for various grades of crude oil and country of origin;

state and national energy production and consumption, using survey and estimates to create comprehensive state energy statistics and flows;

U.S. total energy production, prices, carbon dioxide emissions, and consumption of energy from all sources by sector.


Archived from https://www.eia.gov/opendata/bulkfiles.php. The Annual Energy Outlook data is also archived separately here.

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. At present, PUDL integrates only a few specific data series related to fuel receipts and costs figures from the Bulk Electricity API. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:



The PUDL Repository on GitHub

PUDL Documentation

Other Catalyst Cooperative data archives
DOI: 10.5281/zenodo.17654309
title: FluPRINT cohort: Mass Cytometry (CyTOF) profiling of T stem cell-like memory (TSCM) responses to LAIV in children

description: The development of universal influenza vaccines relies on eliciting broad CD8+ T-cell immunity, specifically the T stem cell-like memory (TSCM) subset. This study investigates the immunological determinants governing the expansion of influenza virus-reactive TSCM cells in a pediatric cohort (n = 19, ages 4-6y) immunized with the live attenuated influenza vaccine (LAIV).

Experimental Design The dataset includes paired .fcs files for each subject (Baseline/Visit 1 and Post-vaccination/Visit 2) under two conditions:





Unstimulated Controls: Matched samples processed in parallel without peptide stimulation.




Antigen Stimulation: Thawed PBMCs stimulated for 12-16 hours with a comprehensive peptide pool library spanning the entire proteome of influenza A/California/07/2009, consisting of 483 20-mers and 24 9-mers.



Methodology





Panel: Cells were stained with a 36-marker metal-labeled antibody panel designed to distinguish T-cell memory subsets and intracellular cytokine production. The workflow included surface labeling, fixation (Maxpar Fix I), permeabilization (Maxpar Perm Buffer), and intracellular staining.




Acquisition: Data were acquired on a Fluidigm Helios mass cytometer.




Analysis Software: Data were originally analyzed using Cytosplore and FlowJo.
DOI: 10.5281/zenodo.17654308
title: FluPRINT cohort: Mass Cytometry (CyTOF) profiling of T stem cell-like memory (TSCM) responses to LAIV in children

description: The development of universal influenza vaccines relies on eliciting broad CD8+ T-cell immunity, specifically the T stem cell-like memory (TSCM) subset. This study investigates the immunological determinants governing the expansion of influenza virus-reactive TSCM cells in a pediatric cohort (n = 19, ages 4-6y) immunized with the live attenuated influenza vaccine (LAIV).

Experimental Design The dataset includes paired .fcs files for each subject (Baseline/Visit 1 and Post-vaccination/Visit 2) under two conditions:





Unstimulated Controls: Matched samples processed in parallel without peptide stimulation.




Antigen Stimulation: Thawed PBMCs stimulated for 12-16 hours with a comprehensive peptide pool library spanning the entire proteome of influenza A/California/07/2009, consisting of 483 20-mers and 24 9-mers.



Methodology





Panel: Cells were stained with a 36-marker metal-labeled antibody panel designed to distinguish T-cell memory subsets and intracellular cytokine production. The workflow included surface labeling, fixation (Maxpar Fix I), permeabilization (Maxpar Perm Buffer), and intracellular staining.




Acquisition: Data were acquired on a Fluidigm Helios mass cytometer.




Analysis Software: Data were originally analyzed using Cytosplore and FlowJo.
DOI: 10.5281/zenodo.17651657
title: Berlin EmoDB 2.0

description: A database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions,producing 10 German utterances (5 short and 5 longer sentences) which could be used in everyday communicationand are interpretable in all applied emotions.The recordings were taken in an anechoic chamber with high-quality recording equipment. In addition to the soundelectro-glottograms were recorded. The speech material comprises about 800 sentences (seven emotions * ten actors * ten sentences + some second versions).The complete database was evaluated in a perception test regarding the recognisability of emotions and theirnaturalness. 

This version 2.0 adds the electro-glottograms as well as the ambiguous samples to the database.

They are split into 4 speakers in test and 6 speakers in train.

The database meta information is in audformat.

The audio format is 16kHz wav format.

Here's a sample script in Python how to get train and test dataframes:

import audformat

!wget https://zenodo.org/record/7447302/files/emodb.zip

!unzip emodb.zip

db = audformat.Database.load('emodb')

df_test = db['emotion.categories.test.gold_standard'].get()

df_train = db['emotion.categories.train.gold_standard'].get()

print(f'samples in train: {df_train.shape[0]}, and test: {df_test.shape[0]}')
description: Database description

Funded by DFG (German Research Community) Grant no. SE 462/3-1
DOI: 10.5281/zenodo.7447301
title: Berlin EmoDB 2.0

description: A database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions,producing 10 German utterances (5 short and 5 longer sentences) which could be used in everyday communicationand are interpretable in all applied emotions.The recordings were taken in an anechoic chamber with high-quality recording equipment. In addition to the soundelectro-glottograms were recorded. The speech material comprises about 800 sentences (seven emotions * ten actors * ten sentences + some second versions).The complete database was evaluated in a perception test regarding the recognisability of emotions and theirnaturalness. 

This version 2.0 adds the electro-glottograms as well as the ambiguous samples to the database.

They are split into 4 speakers in test and 6 speakers in train.

The database meta information is in audformat.

The audio format is 16kHz wav format.

Here's a sample script in Python how to get train and test dataframes:

import audformat

!wget https://zenodo.org/record/7447302/files/emodb.zip

!unzip emodb.zip

db = audformat.Database.load('emodb')

df_test = db['emotion.categories.test.gold_standard'].get()

df_train = db['emotion.categories.train.gold_standard'].get()

print(f'samples in train: {df_train.shape[0]}, and test: {df_test.shape[0]}')
description: Database description

Funded by DFG (German Research Community) Grant no. SE 462/3-1
DOI: 10.15468/dl.usba5z
title: Occurrence Download

description: A dataset containing 9 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'CA'
 AND GBIF_WITHIN('POLYGON((-79.9725 43.41095,-79.16238 43.41095,-79.16238 43.95547,-79.9725 43.95547,-79.9725 43.41095))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey IN ('2432406', '2432436', '7665644') OR occurrence.acceptedtaxonkey IN ('2432406', '2432436', '7665644') OR occurrence.kingdomkey IN ('2432406', '2432436', '7665644') OR occurrence.phylumkey IN ('2432406', '2432436', '7665644') OR occurrence.classkey IN ('2432406', '2432436', '7665644') OR occurrence.orderkey IN ('2432406', '2432436', '7665644') OR occurrence.familykey IN ('2432406', '2432436', '7665644') OR occurrence.genuskey IN ('2432406', '2432436', '7665644') OR occurrence.specieskey IN ('2432406', '2432436', '7665644'))
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 9 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000657-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/m9sqss
title: Motivational Autonomy and Internalized Norms

description: The Measures for Advancing Gender Equality (MAGNET) initiative aims to broaden and deepen the measurement of women’s agency, based on the development of new tools and rigorous testing and comparison of both new and existing methods for measuring agency, and promoting the adoption of these measures at scale. By increasing the availability of innovative meaningful measures of agency for a broad range of contexts, we hope our work will lead to an improved understanding of what women’s agency is, how it manifests and how it can best be measured across contexts given the research question at hand.
A key component of Self-Determination Theory (Ryan and Deci 2000), motivational autonomy conceptualizes human behavior as driven by distinct types of motivation. Behavior that reflects individuals’ own values and interests is seen as autonomous and intrinsically motivated. Behavior taken to avoid punishment and blame or to gain reward or favor among other people is seen as extrinsically motivated. This survey tool, Motivational Autonomy and Internalized Norms, includes vignettes measuring four different types of motivation, ranging from the most autonomous (intrinsic, integrated) to the least autonomous (introjected, external). We recommend the use of this tool in the design, monitoring, and evaluation of development programs aimed at improving women’s empowerment.&lt;/p&gt;
This data study includes following files.
&lt;/p&gt;1. A survey document (including implementation guidelines)
&lt;/p&gt;2.Two files, CAPI_Choices and CAPI_Survey, along with the accompanying files, can be used to construct a CAPI program ready for survey implementation. Alternatively, users can use an Excel workbook "CAPI_.xlsx" that includes worksheets for survey and choices, along with others, for constructing a CAPI program ready for survey implementation.
description: Tools
DOI: 10.5281/zenodo.17622298
title: EIA STEO -- Short-Term Energy Outlook

description: The Short Term Energy Outlook is a monthly report of the U.S. Energy Information Administration with forecasts and history of energy production, consumption, and trade for electricity, petroleum, natural gas, coal, nuclear, and renewable sources. Archived from 
https://www.eia.gov/outlooks/steo/

This archive contains raw input data for the Public Utility Data Liberation (PUDL)
software developed by Catalyst Cooperative. It is
organized into Frictionless
Data Packages. For additional information about this data and PUDL, see the
following resources:


  
The PUDL Repository on GitHub
  
PUDL Documentation
  
Other Catalyst Cooperative data archives
DOI: 10.5281/zenodo.17546734
title: Glass-Freeze Analysis Protocol: CPA + Constraint Rate Comparison

description: A Physical Derivation of Super-Arrhenius Viscosity: Resolving the VFT Singularity

This record completes the preregistered study Glass-Freeze Analysis Protocol (November 2025), presenting a physical resolution to the long-standing interpretation problem of the Vogel-Fulcher-Tammann (VFT) law.

While the empirical VFT law has successfully modeled glass-forming liquids for over 70 years, its reliance on a divergent temperature (T₀) lacks a clear physical basis. This dataset evaluates the CPA + Constraint (CPA + C) formulation derived from Dynamic Present Theory (DPΦ), which models viscosity not as a function of thermal divergence, but as a result of increasing informational Constraint Load (C) on the rate of Continuous Present Actualization (CPA).

Key Findings:





Statistical Parity: The CPA + C formulation reproduces the VFT curve with negligible statistical difference (R² > 0.99) across three chemically distinct datasets: Ortho-terphenyl (OTP) (Laughlin & Uhlmann, 1972; Plazek et al., 1994) and Glycerol-Water mixtures (Kumar et al., 1994).




Physical Mechanism: The analysis demonstrates that the nonlinear curvature of viscosity arises naturally from constraint-driven actualization dynamics, effectively replacing the non-physical VFT singularity with a quantifiable, mechanistic variable (CPA Lock-In, or Tg).




Robustness: In the independent Plazek OTP dataset, the CPA + C model slightly outperformed the VFT reference (AIC -33.81 vs -33.42), validating the model's predictive capability.



Contents:





Raw viscosity-temperature data for all three systems.




Extracted model parameters and residual analysis.




High-resolution comparative fit figures.




Documentation for the Glass GUI V1.1 (custom analysis tool by C. E. Precker, 2025).
DOI: 10.5281/zenodo.17643985
title: Glass-Freeze Analysis Protocol: CPA + Constraint Rate Comparison

description: A Physical Derivation of Super-Arrhenius Viscosity: Resolving the VFT Singularity

This record completes the preregistered study Glass-Freeze Analysis Protocol (November 2025), presenting a physical resolution to the long-standing interpretation problem of the Vogel-Fulcher-Tammann (VFT) law.

While the empirical VFT law has successfully modeled glass-forming liquids for over 70 years, its reliance on a divergent temperature (T₀) lacks a clear physical basis. This dataset evaluates the CPA + Constraint (CPA + C) formulation derived from Dynamic Present Theory (DPΦ), which models viscosity not as a function of thermal divergence, but as a result of increasing informational Constraint Load (C) on the rate of Continuous Present Actualization (CPA).

Key Findings:





Statistical Parity: The CPA + C formulation reproduces the VFT curve with negligible statistical difference (R² > 0.99) across three chemically distinct datasets: Ortho-terphenyl (OTP) (Laughlin & Uhlmann, 1972; Plazek et al., 1994) and Glycerol-Water mixtures (Kumar et al., 1994).




Physical Mechanism: The analysis demonstrates that the nonlinear curvature of viscosity arises naturally from constraint-driven actualization dynamics, effectively replacing the non-physical VFT singularity with a quantifiable, mechanistic variable (CPA Lock-In, or Tg).




Robustness: In the independent Plazek OTP dataset, the CPA + C model slightly outperformed the VFT reference (AIC -33.81 vs -33.42), validating the model's predictive capability.



Contents:





Raw viscosity-temperature data for all three systems.




Extracted model parameters and residual analysis.




High-resolution comparative fit figures.




Documentation for the Glass GUI V1.1 (custom analysis tool by C. E. Precker, 2025).
DOI: 10.15468/dl.k9f3vp
title: Occurrence Download

description: A dataset containing 3494 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-3.25369 43.28889,-1.55445 42.15764,0.86164 41.06589,2.87948 41.14591,3.94151 42.07886,3.38395 43.09532,2.16262 43.52036,0.70234 43.94243,-0.81104 44.19044,-1.76686 44.26654,-2.37752 44.28555,-3.38644 43.88505,-3.25369 43.28889))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dendrocopos leucotos"
 ]
}

 The dataset includes 3494 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000665-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17652744
title: Free Electron Interaction with Genistein: Positive and Negative Ion Formation

description: These data files contain the associated data for the article "Free Electron Interaction with Genistein: Positive and Negative Ion Formation" by Vy T.T. Nguyen, Jiakuan Chen, Milan Oncak and Stephan Denifl, accepted for publication in RSC Advances on 19. 11. 2025.
DOI: 10.5281/zenodo.17652743
title: Free Electron Interaction with Genistein: Positive and Negative Ion Formation

description: These data files contain the associated data for the article "Free Electron Interaction with Genistein: Positive and Negative Ion Formation" by Vy T.T. Nguyen, Jiakuan Chen, Milan Oncak and Stephan Denifl, accepted for publication in RSC Advances on 19. 11. 2025.