cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tzk5ncrvw9.2
title: Retrofits and Revisions: How Evolutionary Theory Fails the Test of Predictive Science

description: The gold standard of science is prediction. If a theory can’t make testable, risky forecasts—and if it consistently fails when put to the test—then it doesn’t deserve the title of science.

Evolution has been presented as biology’s untouchable framework… but history reveals a long trail of failed predictions, retroactive just-so stories, and an inability to generate genuinely novel structures through its core mechanism of mutation and selection.

Meanwhile, the Young Earth Creationist (YEC) model—dismissed by many as unscientific—has quietly built a track record of making specific, accurate predictions:
✅ Genetic similarity across “distant” organisms
✅ Hierarchical clustering of kinds
✅ Mutation rates that match a recent origin
✅ Limits to beneficial mutations
✅ Functional “junk DNA” (ERVs, regulatory networks, Hox clusters, etc.)
✅ Rapid speciation and founder effects
✅ Unity of the human race

And the results? Time and again, data aligns with YEC predictions—while evolutionary theory scrambles to retrofit explanations after the fact.

 In this study, we put the predictions of both models head-to-head across genetics, development, and population biology. The verdict is striking: creation’s design-based framework repeatedly outperforms evolution’s story-driven narrative.

From “junk DNA” turning out functional, to mutation rates exposing hard limits, to the stubborn absence of macroevolutionary innovation—modern evidence is shouting what many scientists won’t admit: the Darwinian framework is crumbling.

Even insiders acknowledge it:

“For what good is a theory that is guaranteed to agree with all conceivable observations? …Is that not exactly the situation with Darwinism?” — Richard Lewontin, Harvard Geneticist

“Nearly all the evolutionary stories I learned as a student… have now been debunked.” — Derek Ager, Imperial College, London

📖 As Dr. Richard Bliss once put it:
“The miracles required to make evolution feasible are far greater in number and far harder to believe than the miracle of creation.”

⚡ It’s time to put predictions on trial.
⚡ It’s time to ask hard questions about falsifiability.
⚡ It’s time to reconsider which model really explains the data.

Dive in and see why the evidence doesn’t just challenge evolution—it points to creation as the better scientific model.
DOI: 10.17632/4yvym5m8n8
title: Extensive Repository of Jackfruit Leaf Images Capturing Health States and Developmental Stage Patterns in Natural Environments

description: This dataset contains raw images of jackfruit (Artocarpus heterophyllus) leaves categorized into five classes: Dried, Healthy, Leaf_Miner, Senescence, and Young. The images were collected using smartphone devices from Savar and Rajbari districts, Bangladesh under natural field conditions. Each image represents different health conditions and growth stages of jackfruit leaves. The dataset is unprocessed, making it suitable for developing models on leaf health and growth stage detection, as well as general agricultural image analysis.

Dataset Classes and Image Counts:
Dried:500 images
Healthy:500 images
Leaf_Miner:500 images
Senescence:500 images
Young:500 images

Image Details:
Original image resolution:3072 x 4096
Resized image resolution:480 x 560
Image formate:JPG
Color mode:RGB
Collection device: Smartphone camera
Location: Savar and Rajbari districts, Bangladesh
DOI: 10.17632/4yvym5m8n8.1
title: Extensive Repository of Jackfruit Leaf Images Capturing Health States and Developmental Stage Patterns in Natural Environments

description: This dataset contains raw images of jackfruit (Artocarpus heterophyllus) leaves categorized into five classes: Dried, Healthy, Leaf_Miner, Senescence, and Young. The images were collected using smartphone devices from Savar and Rajbari districts, Bangladesh under natural field conditions. Each image represents different health conditions and growth stages of jackfruit leaves. The dataset is unprocessed, making it suitable for developing models on leaf health and growth stage detection, as well as general agricultural image analysis.

Dataset Classes and Image Counts:
Dried:500 images
Healthy:500 images
Leaf_Miner:500 images
Senescence:500 images
Young:500 images

Image Details:
Original image resolution:3072 x 4096
Resized image resolution:480 x 560
Image formate:JPG
Color mode:RGB
Collection device: Smartphone camera
Location: Savar and Rajbari districts, Bangladesh
DOI: 10.17632/gmyt9wrgst
title: Topological data analysis and Network analysis approach for sustainable mobility in cities

description: Data Description
This dataset contains detailed information on urban network structures and socio-demographic variables for 65 cities across various continents for the year 2023. The data were collected and processed to explore the relationship between network topology and urban mobility readiness (UMRi). The dataset includes key metrics such as graph entropy, node degree, clustering coefficient, graph diameter, GDP per capita, population density, and more.

The dataset is organized into multiple columns representing both network-derived variables and socio-economic indicators. The primary objective of this dataset is to provide a comprehensive basis for analyzing how urban network structures influence mobility efficiency and sustainability in cities worldwide.

Researchers and urban planners can use this dataset to further explore the complex dynamics of urban mobility and develop strategies for improving transportation systems in growing urban areas. The data are provided in a structured format, suitable for use in statistical software and network analysis tools.

Main Source primary data: https://www.oliverwymanforum.com/mobility/urban-mobility-readiness-index.html

A paper (in revision stage!) is included in this dataset for a general introduction, aims and scope. The full citation for that is:

Herrera-Acevedo, D. D., & Sierra-Porta, D. (2025). Network structure and urban mobility sustainability: A topological analysis of cities from the urban mobility readiness index. Sustainable Cities and Society, 119, 106076. https://doi.org/10.1016/j.scs.2024.106076

Reproducibility:
For transparency and reproducibility, all scripts and notebooks used in the data collection and processing are openly available in the Mendeley Data repository. The repository includes (i) an executable Python script (Network.py) that retrieves the street networks from OpenStreetMap via OSMnx and stores them in .graphml and .npy formats, and (ii) a Jupyter notebook (Preparing_data_v2.ipynb) that implements NetworkX and igraph functions to compute the topological metrics reported in this article. All analyses were performed in Python 3.11.4, using OSMnx 1.3.0, NumPy 1.25.2, NetworkX 3.2.1, igraph 0.10.8, Pandas 2.0.3, and joblib 1.3.2 for parallelization. Parameterization followed default values unless otherwise specified, e.g., Louvain community detection was executed with resolution parameter γ = 1.0. Users are referred to the repository’s README file for detailed instructions and environment specifications.
DOI: 10.17632/gmyt9wrgst.3
title: Topological data analysis and Network analysis approach for sustainable mobility in cities

description: Data Description
This dataset contains detailed information on urban network structures and socio-demographic variables for 65 cities across various continents for the year 2023. The data were collected and processed to explore the relationship between network topology and urban mobility readiness (UMRi). The dataset includes key metrics such as graph entropy, node degree, clustering coefficient, graph diameter, GDP per capita, population density, and more.

The dataset is organized into multiple columns representing both network-derived variables and socio-economic indicators. The primary objective of this dataset is to provide a comprehensive basis for analyzing how urban network structures influence mobility efficiency and sustainability in cities worldwide.

Researchers and urban planners can use this dataset to further explore the complex dynamics of urban mobility and develop strategies for improving transportation systems in growing urban areas. The data are provided in a structured format, suitable for use in statistical software and network analysis tools.

Main Source primary data: https://www.oliverwymanforum.com/mobility/urban-mobility-readiness-index.html

A paper (in revision stage!) is included in this dataset for a general introduction, aims and scope. The full citation for that is:

Herrera-Acevedo, D. D., & Sierra-Porta, D. (2025). Network structure and urban mobility sustainability: A topological analysis of cities from the urban mobility readiness index. Sustainable Cities and Society, 119, 106076. https://doi.org/10.1016/j.scs.2024.106076

Reproducibility:
For transparency and reproducibility, all scripts and notebooks used in the data collection and processing are openly available in the Mendeley Data repository. The repository includes (i) an executable Python script (Network.py) that retrieves the street networks from OpenStreetMap via OSMnx and stores them in .graphml and .npy formats, and (ii) a Jupyter notebook (Preparing_data_v2.ipynb) that implements NetworkX and igraph functions to compute the topological metrics reported in this article. All analyses were performed in Python 3.11.4, using OSMnx 1.3.0, NumPy 1.25.2, NetworkX 3.2.1, igraph 0.10.8, Pandas 2.0.3, and joblib 1.3.2 for parallelization. Parameterization followed default values unless otherwise specified, e.g., Louvain community detection was executed with resolution parameter γ = 1.0. Users are referred to the repository’s README file for detailed instructions and environment specifications.
DOI: 10.5281/zenodo.17048762
title: dbs-source-data

description: This dataset contains the processed and intermediate data underlying the figures and statistical analyses presented in the manuscript “DBS-induced normalization of hippocampal synchrony in Alzheimer's disease progression” (Trevisiol et al., submitted to Theranostics).

The data include processed electrophysiology outputs (local field potentials, spiking activity, and model outputs corresponding to the figures in the manuscript

These files were directly used to generate the figures and results presented in the paper. Due to the very large size of raw data (continuous electrophysiological recordings), only representative processed datasets are included. Raw data are available from the authors upon request.
DOI: 10.5281/zenodo.17048763
title: dbs-source-data

description: This dataset contains the processed and intermediate data underlying the figures and statistical analyses presented in the manuscript “DBS-induced normalization of hippocampal synchrony in Alzheimer's disease progression” (Trevisiol et al., submitted to Theranostics).

The data include processed electrophysiology outputs (local field potentials, spiking activity, and model outputs corresponding to the figures in the manuscript

These files were directly used to generate the figures and results presented in the paper. Due to the very large size of raw data (continuous electrophysiological recordings), only representative processed datasets are included. Raw data are available from the authors upon request.
DOI: 10.15468/dl.eyz3pe
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Himalaiella deltoidea"
 ]
}

 The dataset includes 81 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023180-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mxzmej
title: Occurrence Download

description: A dataset containing 12320 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Campylorhamphus pusillus"
 ]
}

 The dataset includes 12320 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023177-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ysrz5pyjc7
title: One Species, Many Names: Mitochondrial Evidence Unites Humans, Neanderthals, Denisovans, and Heidelbergensis

description: One barcode. Four “species.” One story.
Using the first 650 bp of the mitochondrial COI-5P gene, this study pulls public BOLD data for Homo sapiens, Neanderthals, Denisovans, and H. heidelbergensis and asks a deceptively simple question: Do these lineages form distinct barcode clusters—or a single maternal line? The answer is striking: all sequences fall into a single BOLD BIN, with &gt;96% identity, no indels, and only minor SNP differences (≈7 for Neanderthals; ≈18–20 for Denisovans/Heidelbergensis). Reverting lineage-specific SNPs yields a consensus sequence identical to modern H. sapiens, consistent with a recent shared maternal ancestor after a severe bottleneck.
Beyond the clustering result, we outline why barcode-level homogeneity challenges “splitter” taxonomies, summarize interbreeding evidence, and review population-size modeling that explains rapid loss/stabilization of archaic segments. Bottom line: within COI-5P, these hominins look like one interbreeding human family, not four barcode-separable species.

What you’ll see inside

Data: COI-5P (650 bp) from BOLD for H. sapiens, H. neanderthalensis, H. denisova, H. heidelbergensis

Methods: MUSCLE alignment, BIN clustering (RESL), pairwise divergence, consensus reconstruction

Key results: Single BIN; ≤3% divergence; SNP-only differences; modern-human consensus after reverting lineage SNPs

Implications: Supports a shared recent maternal lineage and a lumper view of human variation; aligns with a post-bottleneck history and rapid stabilization of archaic DNA after limited admixture

Why it matters

Clarifies what COI-5P can—and can’t—resolve inside Homo

Provides a clean, visual framework (BIN + consensus) for discussing “ancestral” labels vs. actual barcode evidence

Offers testable predictions for other taxa and barcodes in post-bottleneck scenarios

How to cite (APA)
Nailor, M. (2025). One Species, Many Names: Mitochondrial COI-5P Barcode Unites Sapiens, Neanderthals, Denisovans, and Heidelbergensis. https://doi.org/10.5281/zenodo.17039432
DOI: 10.17632/ysrz5pyjc7.1
title: One Species, Many Names: Mitochondrial Evidence Unites Humans, Neanderthals, Denisovans, and Heidelbergensis

description: One barcode. Four “species.” One story.
Using the first 650 bp of the mitochondrial COI-5P gene, this study pulls public BOLD data for Homo sapiens, Neanderthals, Denisovans, and H. heidelbergensis and asks a deceptively simple question: Do these lineages form distinct barcode clusters—or a single maternal line? The answer is striking: all sequences fall into a single BOLD BIN, with &gt;96% identity, no indels, and only minor SNP differences (≈7 for Neanderthals; ≈18–20 for Denisovans/Heidelbergensis). Reverting lineage-specific SNPs yields a consensus sequence identical to modern H. sapiens, consistent with a recent shared maternal ancestor after a severe bottleneck.
Beyond the clustering result, we outline why barcode-level homogeneity challenges “splitter” taxonomies, summarize interbreeding evidence, and review population-size modeling that explains rapid loss/stabilization of archaic segments. Bottom line: within COI-5P, these hominins look like one interbreeding human family, not four barcode-separable species.

What you’ll see inside

Data: COI-5P (650 bp) from BOLD for H. sapiens, H. neanderthalensis, H. denisova, H. heidelbergensis

Methods: MUSCLE alignment, BIN clustering (RESL), pairwise divergence, consensus reconstruction

Key results: Single BIN; ≤3% divergence; SNP-only differences; modern-human consensus after reverting lineage SNPs

Implications: Supports a shared recent maternal lineage and a lumper view of human variation; aligns with a post-bottleneck history and rapid stabilization of archaic DNA after limited admixture

Why it matters

Clarifies what COI-5P can—and can’t—resolve inside Homo

Provides a clean, visual framework (BIN + consensus) for discussing “ancestral” labels vs. actual barcode evidence

Offers testable predictions for other taxa and barcodes in post-bottleneck scenarios

How to cite (APA)
Nailor, M. (2025). One Species, Many Names: Mitochondrial COI-5P Barcode Unites Sapiens, Neanderthals, Denisovans, and Heidelbergensis. https://doi.org/10.5281/zenodo.17039432
DOI: 10.17632/dwtm7dhvw2
title: Could a Global Flood Produce All Earth's Oil?

description: 🔥 Why this study matters
Can vast petroleum systems form fast? This paper integrates lab results, natural hydrothermal analogs, and global carbon mass-balance to test whether Earth’s ~9 trillion barrels of petroleum-equivalent hydrocarbons (including oil shale + tar sands) could arise within a young-Earth timeline under catastrophic conditions.

⚡ Core claim (TL;DR)
Given (1) a far more productive pre-Flood biosphere, (2) rapid, anoxic burial, and (3) short-lived but intense thermal/tectonic pulses, petroleum can be generated, expelled, migrated, and trapped in years to decades—with unconverted organics remaining as coal and kerogen. Modern geochemistry shows reaction rates scale with heat/pressure, not calendar time.

🔍 What’s inside
Mass-balance accounting: Converts global reserves (~3 T bbl conventional + ~6 T bbl shale ≈ ~9 T bbl) to required biomass using ~10,000:1 conversion baselines; explores higher-efficiency scenarios under catastrophic conditions.

Pre-Flood productivity: Paleo-oxygen, fossil gigantism, and vegetation reconstructions support 100–1,000× modern biomass; floating-forest/wetland models address coal and lignite volumes.

Rapid generation evidence:

Laboratory oil formation in days–years under elevated T/P.

Natural analogs (e.g., hydrothermal systems like Guaymas Basin) producing gasoline-range hydrocarbons today with radiocarbon-measured young oils.

Pathways & partitions: Why most fossil organic matter persists as kerogen, a smaller share as coal, and a fraction as oil/gas—and how tar sands fit as degraded oils.

Comparative framework: Side-by-side of YEC catastrophic vs conventional gradualist models; notes on abiogenic contributions as a potential minor source.

📊 Figures & visuals overview
Petroleum resource breakdown (crude, shale, tar sands)

Biomass requirements: conventional vs accelerated (log-scale)

Radiometric summaries of “young” oils

Biotic vs abiotic formation pathways

Pre-Flood biomass scenarios (modern, 10×, 100×, 1,000×)

Timescale comparison (millions of years vs years–decades)

🧪 Key takeaways
Rates are conditional: With sufficient heat/pressure, oil-forming reactions do not require geologic ages.

Front-loaded system: A single, global burial event can “short-circuit” decay, maximizing preservation and conversion efficiency.

Consistency check: The scale, chemistry, and distribution of global petroleum are consistent with catastrophic burial plus rapid thermal processing, while explaining coal/kerogen abundance.
DOI: 10.17632/dwtm7dhvw2.1
title: Could a Global Flood Produce All Earth's Oil?

description: 🔥 Why this study matters
Can vast petroleum systems form fast? This paper integrates lab results, natural hydrothermal analogs, and global carbon mass-balance to test whether Earth’s ~9 trillion barrels of petroleum-equivalent hydrocarbons (including oil shale + tar sands) could arise within a young-Earth timeline under catastrophic conditions.

⚡ Core claim (TL;DR)
Given (1) a far more productive pre-Flood biosphere, (2) rapid, anoxic burial, and (3) short-lived but intense thermal/tectonic pulses, petroleum can be generated, expelled, migrated, and trapped in years to decades—with unconverted organics remaining as coal and kerogen. Modern geochemistry shows reaction rates scale with heat/pressure, not calendar time.

🔍 What’s inside
Mass-balance accounting: Converts global reserves (~3 T bbl conventional + ~6 T bbl shale ≈ ~9 T bbl) to required biomass using ~10,000:1 conversion baselines; explores higher-efficiency scenarios under catastrophic conditions.

Pre-Flood productivity: Paleo-oxygen, fossil gigantism, and vegetation reconstructions support 100–1,000× modern biomass; floating-forest/wetland models address coal and lignite volumes.

Rapid generation evidence:

Laboratory oil formation in days–years under elevated T/P.

Natural analogs (e.g., hydrothermal systems like Guaymas Basin) producing gasoline-range hydrocarbons today with radiocarbon-measured young oils.

Pathways & partitions: Why most fossil organic matter persists as kerogen, a smaller share as coal, and a fraction as oil/gas—and how tar sands fit as degraded oils.

Comparative framework: Side-by-side of YEC catastrophic vs conventional gradualist models; notes on abiogenic contributions as a potential minor source.

📊 Figures & visuals overview
Petroleum resource breakdown (crude, shale, tar sands)

Biomass requirements: conventional vs accelerated (log-scale)

Radiometric summaries of “young” oils

Biotic vs abiotic formation pathways

Pre-Flood biomass scenarios (modern, 10×, 100×, 1,000×)

Timescale comparison (millions of years vs years–decades)

🧪 Key takeaways
Rates are conditional: With sufficient heat/pressure, oil-forming reactions do not require geologic ages.

Front-loaded system: A single, global burial event can “short-circuit” decay, maximizing preservation and conversion efficiency.

Consistency check: The scale, chemistry, and distribution of global petroleum are consistent with catastrophic burial plus rapid thermal processing, while explaining coal/kerogen abundance.
DOI: 10.15468/dl.y9vv9s
title: Occurrence Download

description: A dataset containing 195 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hydrocharis morsus-ranae",
 "Country is United States of America",
 "BasisOfRecord is Specimen",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null",
 "EventDate is 2016-01-01,2025-08-31"
 ]
}

 The dataset includes 195 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023181-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s4tzvd
title: Occurrence Download

description: A dataset containing 5230 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Laterallus jamaicensis"
 ]
}

 The dataset includes 5230 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023179-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2pg764w6gs
title: Expectations Matter: When (not) to Use Machine Learning Earnings Forecasts

description: Replication package for the paper titled "Expectations Matter: When (not) to Use Machine Learning Earnings Forecasts"
DOI: 10.17632/2pg764w6gs.1
title: Expectations Matter: When (not) to Use Machine Learning Earnings Forecasts

description: Replication package for the paper titled "Expectations Matter: When (not) to Use Machine Learning Earnings Forecasts"