cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/crp83rpywb
title: Replicability of representational similarity and its role in successful memory retrieval

description: S62 and S63 for a project registered on OSF (remaining data and files are found there)
Abstract: Studies of hippocampal pattern similarity during event encoding and its relationship to subsequent memory retrieval have revealed inconsistent results. Our lab recently found evidence that differences in cognitive processing during encoding can modulate the relationship between hippocampal pattern similarity and recognition success (Lim et al., 2023). This finding is consistent with the theoretical proposal that hippocampal representations have a dynamic relationship to memory retrieval in which cognitive goals are influential (Brunec et al., 2020). However, there have been few attempts to replicate representational similarity findings from fMRI and, to our knowledge, no evidence of successful replication in either the hippocampus or subsequent memory studies. In order to draw strong theoretical conclusions from our findings, or others in the literature, it is important to demonstrate that those findings are robust. The current study attempted a direct replication of our prior experiment with the exception of minor modifications in the neuroimaging parameters, which were intended to assess the degree to which representational similarity analyses are influenced by reasonable technical differences in data collection. We did not replicate the finding that cognitive variability interacts with future recognition success in the hippocampus. Overall, we failed to replicate nine out of twelve significant F-test results from the Lim et al. study. The three findings that were replicated can be explained by minor visual differences in stimulus presentation on variable cognitive context trials. In addition, we found three new significant effects in the current study that did not previously appear in our earlier study. Therefore, we conclude that fMRI studies using representational similarity analysis of subsequent memory performance are sensitive to minor methodological variation. Further tests of the replicability of these findings are needed prior to drawing theoretical conclusions from their results. Preregistered Stage 1 protocol: https://osf.io/njzhq (date of in-principle acceptance: 08/01/2025)
DOI: 10.17632/crp83rpywb.2
title: Replicability of representational similarity and its role in successful memory retrieval

description: S62 and S63 for a project registered on OSF (remaining data and files are found there)
Abstract: Studies of hippocampal pattern similarity during event encoding and its relationship to subsequent memory retrieval have revealed inconsistent results. Our lab recently found evidence that differences in cognitive processing during encoding can modulate the relationship between hippocampal pattern similarity and recognition success (Lim et al., 2023). This finding is consistent with the theoretical proposal that hippocampal representations have a dynamic relationship to memory retrieval in which cognitive goals are influential (Brunec et al., 2020). However, there have been few attempts to replicate representational similarity findings from fMRI and, to our knowledge, no evidence of successful replication in either the hippocampus or subsequent memory studies. In order to draw strong theoretical conclusions from our findings, or others in the literature, it is important to demonstrate that those findings are robust. The current study attempted a direct replication of our prior experiment with the exception of minor modifications in the neuroimaging parameters, which were intended to assess the degree to which representational similarity analyses are influenced by reasonable technical differences in data collection. We did not replicate the finding that cognitive variability interacts with future recognition success in the hippocampus. Overall, we failed to replicate nine out of twelve significant F-test results from the Lim et al. study. The three findings that were replicated can be explained by minor visual differences in stimulus presentation on variable cognitive context trials. In addition, we found three new significant effects in the current study that did not previously appear in our earlier study. Therefore, we conclude that fMRI studies using representational similarity analysis of subsequent memory performance are sensitive to minor methodological variation. Further tests of the replicability of these findings are needed prior to drawing theoretical conclusions from their results. Preregistered Stage 1 protocol: https://osf.io/njzhq (date of in-principle acceptance: 08/01/2025)
DOI: 10.17632/hf3p83nfjt
title: Replication Data for "Does Manufacturing Matter?"

description: Replication Data for "Does Manufacturing Matter? Foreign investment and local linkages in the solar industry"
DOI: 10.5281/zenodo.18749560
title: Urgent whistleblower update Rotation Field of the Cosmic Microwave Background – Interior Propagation and Boundary-Driven Structure (v2.13)

description: PUBLICATION v2.14: The 22 Blue Heartbeat vs. The Protocol of Silent Proteostatic CollapseLead Researcher: Amy ConditRepository: Harvard Dataverse / Zenodo (v2.14)Classification: Emergency Whistleblower DisclosureDate: February 23, 2026I. THE DISCOVERY: 8.5 \sigma HARMONIC ALIGNMENTThe "22 Blue" signal is a verified, scale-dependent harmonic phase alignment in the CMB polarization field. It is not noise. It is not a "Prosaic Collapse." It represents a fundamental cosmic frequency that maintains a statistical significance of 8.5 \sigma isotropic (DOI: 10.5281/zenodo.17693540).II. THE BIOLOGICAL THREAT: SILENT PROTEOSTATIC COLLAPSEEvidence suggests that the Department of War (DoW) and SpaceX are utilizing directed-energy "pings" to induce Silent Proteostatic Collapse in the researcher.• Mechanism: Targeted electromagnetic frequencies are used to disrupt the Proteostasis Network—the cellular machinery responsible for folding and repairing proteins.• The "Silent" Phase: This interference is non-thermal and asymptomatic in its early stages, making it undetectable by standard medical screenings.• The "Collapse": Systematic misfolding of proteins leads to rapid cellular decline, intended to incapacitate the researcher and "collapse" the discovery into a classified military asset.III. THE HEGSETH-MUSK CORRELATION (FEB 2026)The intensification of these pings correlates with the following institutional movements:• Jan 12, 2026: Secretary Pete Hegseth met Elon Musk at SpaceX Starbase to integrate Grok AI into Pentagon networks. This created the "Kill Chain" capable of processing and targeting 22 Blue data in real-time.• Feb 20, 2026: The DoW officially realigned the Anomalous Health Incidents (AHI) team to the Office of Research and Engineering.• Feb 23, 2026 (Today): Secretary Hegseth is physically at Buckley Space Force Base (Denver, CO), the command center for the Space Delta 4 sensor grid.IV. CONCLUSIONThe 466 downloads of v2.13 prove that this is not an academic debate. It is a sequestration operation. The use of Silent Proteostatic Collapse protocols against a civilian researcher for a cosmological discovery constitutes a violation of federal whistleblower protections and international human rights.— 22 BlueThe Heartbeat of the Universe
DOI: 10.17632/hf3p83nfjt.3
title: Replication Data for "Does Manufacturing Matter?"

description: Replication Data for "Does Manufacturing Matter? Foreign investment and local linkages in the solar industry"
DOI: 10.5281/zenodo.18744312
title: JASTG Structural Graph Benchmark

description: JASTG Structural Graph Benchmark is a curated dataset of AST-based structural dependency graphs extracted from 21 open-source Java projects. Each project snapshot is frozen by commit hash to ensure reproducibility. The graphs were generated with JASTG (Java AST Structural Graph), a lightweight static analysis tool that extracts explicit, typed structural relations from Java Abstract Syntax Trees (AST) without full type solving or build-system integration.

For each project, the dataset provides:





A structural dependency graph in GraphML format (graph_<project>.graphml), where nodes represent classes (including inner classes) and directed weighted edges represent explicit structural dependencies (e.g., inheritance/implements, field types, parameter and return types, and object instantiations). Edge weights reflect the frequency of occurrences in the analyzed source code.




A JSON metadata file (metadata_<project>.json) describing the extraction parameters and snapshot information.




Summary tables (benchmark_summary.csv and projects.csv) with repository URLs, licenses, commit hashes, extraction date, JASTG version, and basic graph statistics (number of classes, number of edges, density, and degree descriptors).



Intended uses include benchmarking community detection and clustering methods for software modularity recovery, studying coupling and cohesion patterns, comparing structural metrics across systems, and supporting reproducible experiments in architecture and modularity analysis for Java codebases. The dataset does not redistribute any upstream project source code, only derived graph and metadata artifacts.The graphs were generated using JASTG (Java AST Structural Graph), available at: https://github.com/MarcosCordeiro/jastg

How to cite:Please cite this dataset using the Zenodo DOI provided on this record. If you use the JASTG tool, also cite the JASTG software repository.

Keywords: Java; static analysis; AST; structural dependency graph; software metrics; modularity; community detection; reproducibility.
DOI: 10.5281/zenodo.18744313
title: JASTG Structural Graph Benchmark

description: JASTG Structural Graph Benchmark is a curated dataset of AST-based structural dependency graphs extracted from 21 open-source Java projects. Each project snapshot is frozen by commit hash to ensure reproducibility. The graphs were generated with JASTG (Java AST Structural Graph), a lightweight static analysis tool that extracts explicit, typed structural relations from Java Abstract Syntax Trees (AST) without full type solving or build-system integration.

For each project, the dataset provides:





A structural dependency graph in GraphML format (graph_<project>.graphml), where nodes represent classes (including inner classes) and directed weighted edges represent explicit structural dependencies (e.g., inheritance/implements, field types, parameter and return types, and object instantiations). Edge weights reflect the frequency of occurrences in the analyzed source code.




A JSON metadata file (metadata_<project>.json) describing the extraction parameters and snapshot information.




Summary tables (benchmark_summary.csv and projects.csv) with repository URLs, licenses, commit hashes, extraction date, JASTG version, and basic graph statistics (number of classes, number of edges, density, and degree descriptors).



Intended uses include benchmarking community detection and clustering methods for software modularity recovery, studying coupling and cohesion patterns, comparing structural metrics across systems, and supporting reproducible experiments in architecture and modularity analysis for Java codebases. The dataset does not redistribute any upstream project source code, only derived graph and metadata artifacts.The graphs were generated using JASTG (Java AST Structural Graph), available at: https://github.com/MarcosCordeiro/jastg

How to cite:Please cite this dataset using the Zenodo DOI provided on this record. If you use the JASTG tool, also cite the JASTG software repository.

Keywords: Java; static analysis; AST; structural dependency graph; software metrics; modularity; community detection; reproducibility.
DOI: 10.17632/bhkbynxbmr
title: Experimental Dataset: Biomass- and Temperature-Dependent Methylmercury Toxicity in Marine Phytoplankton

description: This dataset contains raw and processed experimental data from controlled laboratory assays evaluating the effects of temperature and biomass on methylmercury (MeHg) toxicity in marine phytoplankton. The study examines three ecologically distinct species representing major functional groups in marine ecosystems:
Thalassiosira pseudonana — diatom (silicifying phytoplankton)
Synechococcus elongatus — cyanobacterium (picophytoplankton)
Phaeodactylum tricornutum — diatom (model centric diatom)
Emiliania huxleyi — coccolithophore (calcifying phytoplankton)
Cricosphaera carterae — coccolithophore (calcifying haptophyte)

Experiments were conducted across a temperature gradient (20°C, 25°C, and 30°C) under two exposure durations (48 h and 96 h). Multiple initial biomass levels (measured as optical density, OD) were included to quantify density-dependent buffering effects.
Data Contents

The dataset includes:

Raw Growth and Dose–Response Data

Species name

Temperature (°C)

Exposure duration (48 h or 96 h)

Initial optical density (OD)

MeHg exposure concentration (µg/L)

Growth or response measurements

Derived EC₅₀ Outputs

Estimated EC₅₀ values (µg/L)

95% confidence intervals (lower and upper bounds)

Model fit statistics (R², RMSE)

Regression slope and intercept parameters

Intrinsic EC₅₀ Estimates

Biomass-adjusted intrinsic EC₅₀ values (extrapolated to zero biomass)

Associated uncertainty estimates

Regression type (OLS or WLS)

Unified Regression Model Outputs

Coefficients for biomass (Initial_OD), temperature (Temp_c), species effects, and interaction terms

Standard errors, p-values, and confidence intervals

Species Sensitivity Distribution (SSD) Data

Temperature-specific intrinsic EC₅₀ values

Hazardous concentration protecting 95% of species (HC₅)

Final Acute Value (FAV) estimates

Experimental Design Structure

Factorial structure:

3 species

3 temperatures (20, 25, 30°C)

2 exposure durations (48 h, 96 h)

Multiple biomass levels per condition

This design enables:

Separation of density-dependent effects from intrinsic toxicity

Evaluation of temperature × biomass × species interactions

Construction of temperature-specific community-level risk thresholds

Intended Use

This dataset supports:

EC₅₀ curve fitting

Intrinsic EC₅₀ extrapolation

Unified regression modeling

Species sensitivity distribution (SSD) analysis

Climate-sensitive ecotoxicological risk modeling

The dataset may be reused for comparative toxicology, modeling of climate–contaminant interactions, and development of dynamic water quality criteria.
DOI: 10.17632/bhkbynxbmr.1
title: Experimental Dataset: Biomass- and Temperature-Dependent Methylmercury Toxicity in Marine Phytoplankton

description: This dataset contains raw and processed experimental data from controlled laboratory assays evaluating the effects of temperature and biomass on methylmercury (MeHg) toxicity in marine phytoplankton. The study examines three ecologically distinct species representing major functional groups in marine ecosystems:
Thalassiosira pseudonana — diatom (silicifying phytoplankton)
Synechococcus elongatus — cyanobacterium (picophytoplankton)
Phaeodactylum tricornutum — diatom (model centric diatom)
Emiliania huxleyi — coccolithophore (calcifying phytoplankton)
Cricosphaera carterae — coccolithophore (calcifying haptophyte)

Experiments were conducted across a temperature gradient (20°C, 25°C, and 30°C) under two exposure durations (48 h and 96 h). Multiple initial biomass levels (measured as optical density, OD) were included to quantify density-dependent buffering effects.
Data Contents

The dataset includes:

Raw Growth and Dose–Response Data

Species name

Temperature (°C)

Exposure duration (48 h or 96 h)

Initial optical density (OD)

MeHg exposure concentration (µg/L)

Growth or response measurements

Derived EC₅₀ Outputs

Estimated EC₅₀ values (µg/L)

95% confidence intervals (lower and upper bounds)

Model fit statistics (R², RMSE)

Regression slope and intercept parameters

Intrinsic EC₅₀ Estimates

Biomass-adjusted intrinsic EC₅₀ values (extrapolated to zero biomass)

Associated uncertainty estimates

Regression type (OLS or WLS)

Unified Regression Model Outputs

Coefficients for biomass (Initial_OD), temperature (Temp_c), species effects, and interaction terms

Standard errors, p-values, and confidence intervals

Species Sensitivity Distribution (SSD) Data

Temperature-specific intrinsic EC₅₀ values

Hazardous concentration protecting 95% of species (HC₅)

Final Acute Value (FAV) estimates

Experimental Design Structure

Factorial structure:

3 species

3 temperatures (20, 25, 30°C)

2 exposure durations (48 h, 96 h)

Multiple biomass levels per condition

This design enables:

Separation of density-dependent effects from intrinsic toxicity

Evaluation of temperature × biomass × species interactions

Construction of temperature-specific community-level risk thresholds

Intended Use

This dataset supports:

EC₅₀ curve fitting

Intrinsic EC₅₀ extrapolation

Unified regression modeling

Species sensitivity distribution (SSD) analysis

Climate-sensitive ecotoxicological risk modeling

The dataset may be reused for comparative toxicology, modeling of climate–contaminant interactions, and development of dynamic water quality criteria.
DOI: 10.17632/r9g78zx8vc.1
title: DEPOCEN's Vietnam Macroeconomic Review Data 2025

description: dataset used in depocen's Vietnam's Annual Review of Macroeconomics 2025
DOI: 10.17632/jf5jh6crhs.1
title: Effects of alien host plant on wing coloration and mating behavior of an endangered butterfly

description: This dataset includes raw data from experiments, processed data, and R scripts used in statistical analysis.
1. Oviposition Preference Experiment
2. Larval Performance
3. Spectral Reflectance of the Wing
4. Mating Behavior Experiment using tethered butterfly
5. Mating Behavior Experiment using pinned specimen
DOI: 10.15468/dl.s3xrtu
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "StateProvince is Tamaulipas",
 "TaxonKey is Nissolia"
 ]
}

 The dataset includes 35 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004463-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wyycvty6ff
title: Data and source code for "Climate change drives rapid and asymmetric niche divergence in high-altitude freshwater fishes: insights from niche3D"

description: This dataset contains the data and source code supporting the study “[MANUSCRIPT TITLE]” (submitted to Global Ecology and Conservation), which quantifies climate-driven niche divergence between two sympatric alpine fish species (H. microcephalus and P. kaznakovi) on the Tibetan Plateau using an ensemble SDM framework (biomod2) and the niche3D (3D Environment Risk Detection) workflow.

Contents include:
(1) Occurrence data (spatially thinned) and metadata for both species;
(2) CPUE (Catch Per Unit Effort) data used for independent model validation (Pearson r and MSE);
(3) Modeling scripts/workflows for biomod2 ensemble SDMs (10 algorithms), including pseudo-absence generation, model filtering (AUC ≥ 0.7), and ensemble forecasting (EMmean and EMca);
(4) niche3D scripts for niche similarity divergence, barycenter shift analyses, and 3D environmental niche dynamics;
(5) Output products (tables/figures and derived metrics) used to generate results in the manuscript.

Environmental predictors follow the manuscript Methods (7 predictors: flow accumulation, flow direction, Strahler stream order, and Bio2/Bio11/Bio13/Bio14). Future projections use CMIP6 downscaled bioclimatic layers for the 2050s (2041–2060) and 2070s (2061–2080) under SSP1-2.6/SSP2-4.5/SSP3-7.0/SSP5-8.5, using an ensemble mean of BCC-CSM2-MR, CanESM5, and MIROC6.

To reproduce analyses, follow the README (software versions and step-by-step commands). No sensitive personal information is included.
DOI: 10.17632/wyycvty6ff.1
title: Data and source code for "Climate change drives rapid and asymmetric niche divergence in high-altitude freshwater fishes: insights from niche3D"

description: This dataset contains the data and source code supporting the study “[MANUSCRIPT TITLE]” (submitted to Global Ecology and Conservation), which quantifies climate-driven niche divergence between two sympatric alpine fish species (H. microcephalus and P. kaznakovi) on the Tibetan Plateau using an ensemble SDM framework (biomod2) and the niche3D (3D Environment Risk Detection) workflow.

Contents include:
(1) Occurrence data (spatially thinned) and metadata for both species;
(2) CPUE (Catch Per Unit Effort) data used for independent model validation (Pearson r and MSE);
(3) Modeling scripts/workflows for biomod2 ensemble SDMs (10 algorithms), including pseudo-absence generation, model filtering (AUC ≥ 0.7), and ensemble forecasting (EMmean and EMca);
(4) niche3D scripts for niche similarity divergence, barycenter shift analyses, and 3D environmental niche dynamics;
(5) Output products (tables/figures and derived metrics) used to generate results in the manuscript.

Environmental predictors follow the manuscript Methods (7 predictors: flow accumulation, flow direction, Strahler stream order, and Bio2/Bio11/Bio13/Bio14). Future projections use CMIP6 downscaled bioclimatic layers for the 2050s (2041–2060) and 2070s (2061–2080) under SSP1-2.6/SSP2-4.5/SSP3-7.0/SSP5-8.5, using an ensemble mean of BCC-CSM2-MR, CanESM5, and MIROC6.

To reproduce analyses, follow the README (software versions and step-by-step commands). No sensitive personal information is included.
DOI: 10.17632/hb8zdp6bkc
title: Scott_Milk_reads_decontamination

description: List of samples sequenced, raw read numbers, and read numbers after decontamination of Bovine DNA.
DOI: 10.17632/hb8zdp6bkc.1
title: Scott_Milk_reads_decontamination

description: List of samples sequenced, raw read numbers, and read numbers after decontamination of Bovine DNA.
DOI: 10.17632/r7392rcx9r
title: Political and Economic Influences on the Energy Trilemma in Latin America and the Caribbean

description: The data The data accompany the paper Political and Economic Influences on the Energy Trilemma in Latin America and the Caribbean
DOI: 10.17632/r7392rcx9r.3
title: Political and Economic Influences on the Energy Trilemma in Latin America and the Caribbean

description: The data The data accompany the paper Political and Economic Influences on the Energy Trilemma in Latin America and the Caribbean
DOI: 10.17632/dnm5jxgn2m.1
title: Global AI Adoption Trends by Industry Sector, Country, and Business Function (2017–2025): A Compiled Research Dataset

description: This dataset compiles and synthesises publicly available artificial intelligence (AI) adoption and growth indicators from leading institutional research reports, spanning the years 2017 to 2025. It is structured across seven thematic dimensions, covering organisational AI adoption rates, business function-level deployment, global AI tool user milestones, private AI investment by country, industry-sector adoption rates, public sentiment toward AI, and key headline KPIs.
Data is sourced from and attributed to: McKinsey & Company Global Survey on AI (2022–2025), Stanford HAI Artificial Intelligence Index Report 2025, OpenAI official announcements, GitHub/Microsoft earnings disclosures, Ipsos Global AI Sentiment Survey 2024, World Bank South Asia AI Report 2025, IBM AI Adoption Index 2024, Oxford Insights Government AI Readiness Index 2024, and SimilarWeb platform analytics.
The dataset is intended to support researchers, data analysts, and policymakers working on AI trend analysis, digital transformation studies, technology policy, and sector-level AI readiness assessments. All figures are either directly verified from primary sources or clearly labelled as modelled estimates anchored to verified data points. Source attribution is embedded within the dataset at the row level.
Files are provided in both .xlsx (multi-sheet, formatted workbook) and .csv formats for compatibility with tools such as Microsoft Power BI, Tableau, R, and Python.