cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.x4y5dh
title: Occurrence Download

description: A dataset containing 57536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "Geometry POLYGON((-115.40753 -9.55635,-37.59247 -9.55635,-37.59247 21.55635,-115.40753 21.55635,-115.40753 -9.55635))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Araceae"
 ]
}

 The dataset includes 57536 records from 478 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047855-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tdvdcm
title: Occurrence Download

description: A dataset containing 2085 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Liquidambar formosana Hance"
 ]
}

 The dataset includes 2085 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006621-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14029555
title: Supplemental tables for 'LA4SR: illuminating the dark proteome with generative AI'
DOI: 10.5281/zenodo.14868966
title: Supplemental tables for 'Integrating terminal-free sequence modeling and explainability to resolve 'dark matter' in algal genomics'

description: AI language models (LMs) show promise for microbial sequence classification. We re-engineered open-source LMs (e.g., GPT-2, BLOOM, DistilRoBERTa, ELECTRA, and Mamba, ranging from 70 million (m) to 12 billion (B) parameters) to classify genomic ‘dark-matter’ using translated ORFeomes from 166 species in ten phyla. The total training data was comprised of ~77 million sequences. The models achieved F1 scores up to 95 and operated 16,580x faster and at 2.9x the recall of BLASTP. They effectively classified uncharacterized sequence from non-model algal species (average of ~65% of total translated ORFs), validated on new data including a new, complete Hi-C/Pacbio Chlamydomonas genome. Larger (> 1B) LA4SR models reached high accuracy (F1 > 86) when trained on less than 2% of available data, rapidly achieving strong generalization capacity. High accuracy was achieved when training data had intact or scrambled terminal information, demonstrating robust generalization to incomplete sequences. Finally, we provide custom AI explainability software tools for attributing amino acid patterns to AI generative processes and interpret their outputs in evolutionary and biophysical contexts.

 

Table S1 | External spreadsheet. This spreadsheet contains LA4SR performance metrics, technical performance estimations, and preliminary BLAST screening results of genomes comprising the algal training data. 

Table S2 | External spreadsheet. Captum attributions for 100 sequences each of algal and bacterial origin obtained using the LayerIntegratedGradients function.

Table S3 | External spreadsheet. Influential motifs found with the DeepMotifMinerPro software introduced in this work (see Data S3).

Table S4 | External spreadsheet. LA4SR and BLAST results for new, real-world sequencing data (uploaded to NCBI SRA accession SUB14799921, related to Fig. S3).
DOI: 10.5281/zenodo.15354813
title: Supplemental tables for 'Pan-microalgal dark proteome mapping: interpretable deep learning with synthetic chimeras'

description: Distinguishing genuine microbial proteins from contaminants remains a major bottleneck in genomics, particularly for environmental and non-model organisms where conventional homology-based tools are slow, resource-intensive, and leave large fractions of the "dark proteome" unclassified. LA4SR offers a scalable, interpretable framework that classifies algal and bacterial proteins directly from translated sequence data, achieving near-complete recall while accelerating inference by nearly 9,000-fold relative to BLASTP. By revealing that internal sequence features alone can drive robust classification, LA4SR bypasses the need for complete gene models or perfect annotations—opening new opportunities for analyzing complex microbial communities and metagenomes. Interpretability methods further link emergent amino acid signatures to evolutionary and ecological features, highlighting the potential of language models not only to accelerate genomics workflows but also to uncover new biological insights. As sequencing efforts expand into diverse and uncharacterized ecosystems, frameworks like LA4SR will be essential for scalable, explainable genomic analysis.

 

Table S1 | External spreadsheet. This spreadsheet contains LA4SR performance metrics, technical performance estimations, and preliminary BLAST screening results of genomes comprising the algal training data. 

Table S2 | External spreadsheet. Captum attributions for 100 sequences each of algal and bacterial origin obtained using the LayerIntegratedGradients function.

Table S3 | External spreadsheet. Influential motifs found with the DeepMotifMinerPro software introduced in this work (see Data S3).

Table S4 | External spreadsheet. LA4SR and BLAST results for new, real-world sequencing data (uploaded to NCBI SRA accession SUB14799921, related to Fig. S3).
DOI: 10.5281/zenodo.15401216
title: Supplemental tables for 'Pan-microalgal dark proteome mapping: interpretable deep learning with synthetic chimeras'

description: Distinguishing genuine microbial proteins from contaminants remains a major bottleneck in genomics, particularly for environmental and non-model organisms where conventional homology-based tools are slow, resource-intensive, and leave large fractions of the "dark proteome" unclassified. LA4SR offers a scalable, interpretable framework that classifies algal and bacterial proteins directly from translated sequence data, achieving near-complete recall while accelerating inference by nearly 9,000-fold relative to BLASTP. By revealing that internal sequence features alone can drive robust classification, LA4SR bypasses the need for complete gene models or perfect annotations—opening new opportunities for analyzing complex microbial communities and metagenomes. Interpretability methods further link emergent amino acid signatures to evolutionary and ecological features, highlighting the potential of language models not only to accelerate genomics workflows but also to uncover new biological insights. As sequencing efforts expand into diverse and uncharacterized ecosystems, frameworks like LA4SR will be essential for scalable, explainable genomic analysis.

 

Table S1 | External spreadsheet. This spreadsheet contains LA4SR performance metrics, technical performance estimations, and preliminary BLAST screening results of genomes comprising the algal training data. 

Table S2 | External spreadsheet. Captum attributions for 100 sequences each of algal and bacterial origin obtained using the LayerIntegratedGradients function.

Table S3 | External spreadsheet. Influential motifs found with the DeepMotifMinerPro software introduced in this work (see Data S3).

Table S4 | External spreadsheet. LA4SR and BLAST results for new, real-world sequencing data (uploaded to NCBI SRA accession SUB14799921, related to Fig. S3).
DOI: 10.5281/zenodo.15401576
title: Supplemental tables for 'Pan-microalgal dark proteome mapping: interpretable deep learning with synthetic chimeras'

description: Distinguishing genuine microbial proteins from contaminants remains a major bottleneck in genomics, particularly for environmental and non-model organisms where conventional homology-based tools are slow, resource-intensive, and leave large fractions of the "dark proteome" unclassified. LA4SR offers a scalable, interpretable framework that classifies algal and bacterial proteins directly from translated sequence data, achieving near-complete recall while accelerating inference by nearly 9,000-fold relative to BLASTP. By revealing that internal sequence features alone can drive robust classification, LA4SR bypasses the need for complete gene models or perfect annotations—opening new opportunities for analyzing complex microbial communities and metagenomes. Interpretability methods further link emergent amino acid signatures to evolutionary and ecological features, highlighting the potential of language models not only to accelerate genomics workflows but also to uncover new biological insights.

 

Table S1 | External spreadsheet. This spreadsheet contains LA4SR performance metrics, technical performance estimations, and preliminary BLAST screening results of genomes comprising the algal training data. 

Table S2 | External spreadsheet. Captum attributions for 100 sequences each of algal and bacterial origin obtained using the LayerIntegratedGradients function.

Table S3 | External spreadsheet. Influential motifs found with the DeepMotifMinerPro software introduced in this work (see Data S3).

Table S4 | External spreadsheet. LA4SR and BLAST results for new, real-world sequencing data (uploaded to NCBI SRA accession SUB14799921, related to Fig. S3).
DOI: 10.5281/zenodo.15421121
title: Supplemental tables for 'Pan-microalgal dark proteome mapping: interpretable deep learning with synthetic chimeras'

description: Distinguishing genuine microbial proteins from contaminants remains a major bottleneck in genomics, particularly for environmental and non-model organisms where conventional homology-based tools are slow, resource-intensive, and leave large fractions of the "dark proteome" unclassified. LA4SR offers a scalable, interpretable framework that classifies algal and bacterial proteins directly from translated sequence data, achieving near-complete recall while accelerating inference by nearly 9,000-fold relative to BLASTP. By revealing that internal sequence features alone can drive robust classification, LA4SR bypasses the need for complete gene models or perfect annotations—opening new opportunities for analyzing complex microbial communities and metagenomes. Interpretability methods further link emergent amino acid signatures to evolutionary and ecological features, highlighting the potential of language models not only to accelerate genomics workflows but also to uncover new biological insights.

 

Table S1 | External spreadsheet. This spreadsheet contains LA4SR performance metrics, technical performance estimations, and preliminary BLAST screening results of genomes comprising the algal training data. 

Table S2 | External spreadsheet. Captum attributions for 100 sequences each of algal and bacterial origin obtained using the LayerIntegratedGradients function.

Table S3 | External spreadsheet. Influential motifs found with the DeepMotifMinerPro software introduced in this work (see Data S3).

Table S4 | External spreadsheet. LA4SR and BLAST results for new, real-world sequencing data (uploaded to NCBI SRA accession SUB14799921, related to Fig. S3).
DOI: 10.5281/zenodo.15544952
title: Supplemental tables for 'Pan-microalgal dark proteome mapping: interpretable deep learning with synthetic chimeras'

description: Distinguishing genuine microbial proteins from contaminants remains a major bottleneck in genomics, particularly for environmental and non-model organisms where conventional homology-based tools are slow, resource-intensive, and leave large fractions of the "dark proteome" unclassified. LA4SR offers a scalable, interpretable framework that classifies algal and bacterial proteins directly from translated sequence data, achieving near-complete recall while accelerating inference by nearly 9,000-fold relative to BLASTP. By revealing that internal sequence features alone can drive robust classification, LA4SR bypasses the need for complete gene models or perfect annotations—opening new opportunities for analyzing complex microbial communities and metagenomes. Interpretability methods further link emergent amino acid signatures to evolutionary and ecological features, highlighting the potential of language models not only to accelerate genomics workflows but also to uncover new biological insights.

 

Table S1 | External spreadsheet. This spreadsheet contains LA4SR performance metrics, technical performance estimations, and BLAST results and runtimes of genomes comprising the algal training data. 

Table S2 | External spreadsheet. Captum attributions for 100 sequences each of algal and bacterial origin obtained using the LayerIntegratedGradients function.

Table S3 | External spreadsheet. Influential motifs found with the DeepMotifMinerPro software introduced in this work (see Data S3).

Table S4 | External spreadsheet. LA4SR and BLAST results for new, real-world sequencing data (uploaded to NCBI SRA accession SUB14799921, related to Fig. S3).
DOI: 10.6084/m9.figshare.26076400
title: Establishing and Using Threshold of Surrogate Endpoint in Relation to Clinical Endpoint

description: Clinical trials are an essential aspect of the drug development process. Clinical endpoints and surrogate endpoints are two terms used in clinical trials to measure the effectiveness of a treatment. While clinical endpoints typically require higher costs and longer durations of observation to show direct clinical benefits, surrogate endpoints have been introduced as a cheaper and faster method that may be used to predict clinical effects. When there is a linear relationship between the surrogate and the clinical endpoint, the surrogate may still need to rule out a threshold that corresponds to no clinical benefit. The determination of such a threshold uses the knowledge of numerous parameters in the bivariate statistical distribution of the clinical response and the surrogate. In our work, we present a concept of “working” threshold to incorporate statistical uncertainties in determination of such a threshold.
DOI: 10.6084/m9.figshare.26076400.v2
title: Establishing and Using Threshold of Surrogate Endpoint in Relation to Clinical Endpoint

description: Clinical trials are an essential aspect of the drug development process. Clinical endpoints and surrogate endpoints are two terms used in clinical trials to measure the effectiveness of a treatment. While clinical endpoints typically require higher costs and longer durations of observation to show direct clinical benefits, surrogate endpoints have been introduced as a cheaper and faster method that may be used to predict clinical effects. When there is a linear relationship between the surrogate and the clinical endpoint, the surrogate may still need to rule out a threshold that corresponds to no clinical benefit. The determination of such a threshold uses the knowledge of numerous parameters in the bivariate statistical distribution of the clinical response and the surrogate. In our work, we present a concept of “working” threshold to incorporate statistical uncertainties in determination of such a threshold.
DOI: 10.6084/m9.figshare.26076400.v1
title: Establishing and using threshold of surrogate endpoint in relation to clinical endpoint<sup>#</sup>

description: Clinical trials are an essential aspect of the drug development process. Clinical endpoints and surrogate endpoints are two terms used in clinical trials to measure the effectiveness of a treatment. While clinical endpoints typically require higher costs and longer durations of observation to show direct clinical benefits, surrogate endpoints have been introduced as a cheaper and faster method that may be used to predict clinical effects. When there is a linear relationship between the surrogate and the clinical endpoint, the surrogate may still need to rule out a threshold that corresponds to no clinical benefit. The determination of such a threshold uses the knowledge of numerous parameters in the bivariate statistical distribution of the clinical response and the surrogate. In our work, we present a concept of “working” threshold to incorporate statistical uncertainties in determination of such a threshold.
DOI: 10.6084/m9.figshare.27180187.v1
title: Possible Data-Generating Models of Longitudinal Continuous Outcomes and Intercurrent Events to Investigate Estimands

description: We aim to explore data-generating models to jointly simulate outcomes and intercurrent events for randomized clinical trials to enable the investigation of estimands. We develop four possible data-generating models for the joint distribution of longitudinal continuous clinical outcomes and intercurrent events under the scenario where they are observable: a selection model, a pattern-mixture mixed model, a shared-parameter model, and a joint model of longitudinally observed outcomes and a survival model for intercurrent events. We present a case study in a short-term depression trial with repeated measurements of continuous outcomes and two types of intercurrent events, and evaluate the potential and challenges of such data-generating models. Simulating randomized trials with outcomes and intercurrent events is a complex undertaking. We found that the four possible data-generating models can simulate different types of intercurrent events and associated longitudinal outcomes. They can be used to emulate envisaged patterns of intercurrent events and outcomes informed by prior available trial data or expectations. Model and parameter choice for a given application require further development. The four possible data-generating models could be used to investigate different estimands and their properties in-depth in the design stage. Thereby they are useful tools for the selection of estimands a priori.
DOI: 10.6084/m9.figshare.27180187
title: Possible Data-Generating Models of Longitudinal Continuous Outcomes and Intercurrent Events to Investigate Estimands

description: We aim to explore data-generating models to jointly simulate outcomes and intercurrent events for randomized clinical trials to enable the investigation of estimands. We develop four possible data-generating models for the joint distribution of longitudinal continuous clinical outcomes and intercurrent events under the scenario where they are observable: a selection model, a pattern-mixture mixed model, a shared-parameter model, and a joint model of longitudinally observed outcomes and a survival model for intercurrent events. We present a case study in a short-term depression trial with repeated measurements of continuous outcomes and two types of intercurrent events, and evaluate the potential and challenges of such data-generating models. Simulating randomized trials with outcomes and intercurrent events is a complex undertaking. We found that the four possible data-generating models can simulate different types of intercurrent events and associated longitudinal outcomes. They can be used to emulate envisaged patterns of intercurrent events and outcomes informed by prior available trial data or expectations. Model and parameter choice for a given application require further development. The four possible data-generating models could be used to investigate different estimands and their properties in-depth in the design stage. Thereby they are useful tools for the selection of estimands a priori.
DOI: 10.15468/dl.uy2bxz
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Baccaurea macrocarpa (Miq.) Müll.Arg.",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003237-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4yfwnc
title: Occurrence Download

description: A dataset containing 869 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Desmognathus organi Crespi, Browne & Rissler, 2010)"
 ]
}

 The dataset includes 869 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003238-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc110821939
title: Persepolis Fortification Elamite Tablet 2273.103, Fort 2273.103

description: [Text Description:] Administrative text ❧ [Text or Publication Number Note:] Fort 2273.103 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. ❧ [Physical Object Note:] All edges broken, except for small part of bottom edge.
DOI: 10.25549/inscriptifact-ouc110822036
title: Persepolis Fortification Elamite Tablet 2277.101, Fort 2277.101

description: [Text Description:] Administrative text ❧ [Text or Publication Number Note:] Fort 2277.101 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. ❧ [Physical Object Note:] Upper edge, left edge broken.
DOI: 10.25549/inscriptifact-ouc110821075
title: Persepolis Fortification Elamite Tablet 2272.102, Fort 2272.102

description: [Text Description:] Administrative text ❧ [Text or Publication Number Note:] Fort 2272.102 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. ❧ [Physical Object Note:] Borken right edge, upper edge, most of left edge.
DOI: 10.25549/inscriptifact-ouc110820830
title: Persepolis Fortification Elamite Tablet 2267.101, Fort 2267.101

description: [Text Description:] Administrative text with seal ❧ [Text or Publication Number Note:] Fort 2267.101 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. Seal impression on right edge. ❧ [Physical Object Note:] Left side of tablet missing.
DOI: 10.25549/inscriptifact-ouc110820789
title: Persepolis Fortification Elamite Tablet 2272.102, Fort 2272.102

description: [Text Description:] Administrative text ❧ [Text or Publication Number Note:] Fort 2272.102 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. ❧ [Physical Object Note:] Borken right edge, upper edge, most of left edge.
DOI: 10.25549/inscriptifact-ouc110821073
title: Persepolis Fortification Elamite Tablet 2272.102, Fort 2272.102

description: [Text Description:] Administrative text ❧ [Text or Publication Number Note:] Fort 2272.102 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet with cuneiform text. ❧ [Physical Object Note:] Borken right edge, upper edge, most of left edge.