cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30948881
title: HK97-fold proteins evolutionary analyses (sequences, structures, and phylogenies)

description: This item contains sequence- and structure-based resources supporting the evolutionary analyses of HK97-fold major capsid proteins (MCPs) and encapsulins reported in our manuscript. The work was carried out in the Luque Lab (Department of Biology, University of Miami). Files include the environmental MCP sequence set, reference identifiers and reference MCP sequences, maximum-likelihood phylogenetic inference outputs, protein structure models, and structure-based phylogenetic results spanning viruses and related HK97-fold systems (encapsulins, PICIs, GTAs, and environmental viral-like entities).<b>Contents:</b><b>data_file_S10_environmental_HK97_proteins.faa</b><br>FASTA file of amino acid sequences for all HK97-fold major capsid proteins (MCPs) detected in environmental viral genomes.<br><b>data_file_S11_environmental_MCPs_trees.zip</b><br>IQ-TREE outputs for environmental HK97 MCPs, including maximum-likelihood trees, model statistics, and TreeCluster groupings at varying distance thresholds.<br><b>data_file_S12_HK97_reference_identifiers.txt</b><br>List of protein and genome identifiers used in the reference HK97-fold dataset, including entries for encapsulins, PICIs, GTAs, and phages.<br><b>data_file_S13_IDs_and_encapsulin_enrichment.csv</b><br>List of HK97-fold protein sequence identifiers (environmental contigs, reference viruses and encapsulins), and phage coat and encapsulin coverage per protein.<br><b>data_file_S14_HK97_protein_structures.zip</b><br>Protein structure models used for structural phylogenetics. Includes both experimentally resolved structures (PDB) and AlphaFold-predicted models with confidence assessments.<br><b>data_file_S15_HK97_large_scale_structural_tree.txt</b><br>Newick tree file of structure-based phylogenetic tree of HK97-fold proteins across viruses, encapsulins, PICIs, GTAs, and viral environmental contigs.<br><b>data_file_S16_HK97_large_scale_sequence_tree.txt</b><br>Newick tree file of sequence-based phylogenetic tree of HK97-fold proteins across viruses, encapsulins, PICIs, GTAs, and viral environmental contigs.<br><b>data_file_S17_tail_proteins_non_encapsulin_enriched_genomes.zip</b><br>Annotation summaries for tail-related proteins detected in DTR-containing twilight-zone genomes lacking encapsulin domains in their HK97 MCPs.<br><b>Notes on reuse:</b> These are processed outputs used in the manuscript’s evolutionary analyses. Please cite the associated manuscript and this Figshare item DOI when reusing these resources. For third-party inputs (e.g., PDB, RefSeq, AlphaFold models, and external tools/databases), please follow their respective citation and usage guidance.
DOI: 10.6084/m9.figshare.30948881.v1
title: HK97-fold proteins evolutionary analyses (sequences, structures, and phylogenies)

description: This item contains sequence- and structure-based resources supporting the evolutionary analyses of HK97-fold major capsid proteins (MCPs) and encapsulins reported in our manuscript. The work was carried out in the Luque Lab (Department of Biology, University of Miami). Files include the environmental MCP sequence set, reference identifiers and reference MCP sequences, maximum-likelihood phylogenetic inference outputs, protein structure models, and structure-based phylogenetic results spanning viruses and related HK97-fold systems (encapsulins, PICIs, GTAs, and environmental viral-like entities).<b>Contents:</b><b>data_file_S10_environmental_HK97_proteins.faa</b><br>FASTA file of amino acid sequences for all HK97-fold major capsid proteins (MCPs) detected in environmental viral genomes.<br><b>data_file_S11_environmental_MCPs_trees.zip</b><br>IQ-TREE outputs for environmental HK97 MCPs, including maximum-likelihood trees, model statistics, and TreeCluster groupings at varying distance thresholds.<br><b>data_file_S12_HK97_reference_identifiers.txt</b><br>List of protein and genome identifiers used in the reference HK97-fold dataset, including entries for encapsulins, PICIs, GTAs, and phages.<br><b>data_file_S13_IDs_and_encapsulin_enrichment.csv</b><br>List of HK97-fold protein sequence identifiers (environmental contigs, reference viruses and encapsulins), and phage coat and encapsulin coverage per protein.<br><b>data_file_S14_HK97_protein_structures.zip</b><br>Protein structure models used for structural phylogenetics. Includes both experimentally resolved structures (PDB) and AlphaFold-predicted models with confidence assessments.<br><b>data_file_S15_HK97_large_scale_structural_tree.txt</b><br>Newick tree file of structure-based phylogenetic tree of HK97-fold proteins across viruses, encapsulins, PICIs, GTAs, and viral environmental contigs.<br><b>data_file_S16_HK97_large_scale_sequence_tree.txt</b><br>Newick tree file of sequence-based phylogenetic tree of HK97-fold proteins across viruses, encapsulins, PICIs, GTAs, and viral environmental contigs.<br><b>data_file_S17_tail_proteins_non_encapsulin_enriched_genomes.zip</b><br>Annotation summaries for tail-related proteins detected in DTR-containing twilight-zone genomes lacking encapsulin domains in their HK97 MCPs.<br><b>Notes on reuse:</b> These are processed outputs used in the manuscript’s evolutionary analyses. Please cite the associated manuscript and this Figshare item DOI when reusing these resources. For third-party inputs (e.g., PDB, RefSeq, AlphaFold models, and external tools/databases), please follow their respective citation and usage guidance.
DOI: 10.15468/dl.t3jqpg
title: Occurrence Download

description: A dataset containing 570 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammillaria pottsii",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 570 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014796-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vydx49
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Laimosemion gili",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014794-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j7x28f
title: Occurrence Download

description: A dataset containing 7729 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crotalaria pallida",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 7729 records from 344 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014795-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v79we3
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Calycosia petiolata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 8 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014797-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/d2c9e684-faa5-4e8b-b9ee-c0a2baa5c8d2
title: Data and code for "Characterisation of material optical properties for indoor daylight simulation"

description: This repository contains the data and code for the PhD thesis chapter <strong>"Characterisation of material optical properties for indoor daylight simulation."</strong><strong>Summary:</strong> This chapter evaluates three image-based techniques for on-site material reflectance characterisation in daylight simulations—Illuminance-Proxy, Ferwerda’s phone-based method, and Luo’s learned SVBRDF estimation—and validates a spectral uplifting approach for non-visual simulations. A meeting room with diverse finishes was measured using a Konica Minolta CM-26dG spectrophotometer (SCI/SCE) to establish ground truth. The Illuminance-Proxy reconstructs wall reflectance maps from HDR luminance and sparse reference points (kriging); Ferwerda’s technique estimates tristimulus reflectance from unconstrained phone images; Luo’s technique infers diffuse/specular/roughness from flash-lit phone photos. Accuracy is reported using NMBE, NRMSE, ∆Eₐᵦ, and room-scale metrics (TAI, UDI, DGP). Ferwerda’s technique shows the most balanced performance (NMBE ≈ 13%, NRMSE ≈ 31%), stable across lighting conditions, with TAI overestimation of 33–65%. Illuminance-Proxy yields NMBE ≈ 19%, NRMSE ≈ 41%, but overpredicts TAI by ∼101% in the case study; a 3-channel HDR test indicates per-point ∆ρ &lt; 0.08. Luo’s technique exhibits the largest errors (NMBE ≈ 25%, NRMSE ≈ 62%), with ∆Eₐᵦ up to ∼60 and TAI overestimation up to ∼134%. All techniques predict low DGP with negligible deviation (NRMSE &lt; 0.02). Spectral uplifting reconstructs smooth, energy-conserving spectra with wavelength-wise errors typically &lt; 20%, yielding melanopic equivalent illuminance (EML) differences &lt; 6%. Overall, Ferwerda’s technique offers the best accuracy–practicability trade-off, Illuminance-Proxy is useful for planar diffuse surfaces when sparse ground truth is available, and spectral uplifting enables non-visual analyses from tristimulus inputs with minimal error.
DOI: 10.4121/d2c9e684-faa5-4e8b-b9ee-c0a2baa5c8d2.v1
title: Data and code for "Characterisation of material optical properties for indoor daylight simulation"

description: This repository contains the data and code for the PhD thesis chapter <strong>"Characterisation of material optical properties for indoor daylight simulation."</strong><strong>Summary:</strong> This chapter evaluates three image-based techniques for on-site material reflectance characterisation in daylight simulations—Illuminance-Proxy, Ferwerda’s phone-based method, and Luo’s learned SVBRDF estimation—and validates a spectral uplifting approach for non-visual simulations. A meeting room with diverse finishes was measured using a Konica Minolta CM-26dG spectrophotometer (SCI/SCE) to establish ground truth. The Illuminance-Proxy reconstructs wall reflectance maps from HDR luminance and sparse reference points (kriging); Ferwerda’s technique estimates tristimulus reflectance from unconstrained phone images; Luo’s technique infers diffuse/specular/roughness from flash-lit phone photos. Accuracy is reported using NMBE, NRMSE, ∆Eₐᵦ, and room-scale metrics (TAI, UDI, DGP). Ferwerda’s technique shows the most balanced performance (NMBE ≈ 13%, NRMSE ≈ 31%), stable across lighting conditions, with TAI overestimation of 33–65%. Illuminance-Proxy yields NMBE ≈ 19%, NRMSE ≈ 41%, but overpredicts TAI by ∼101% in the case study; a 3-channel HDR test indicates per-point ∆ρ &lt; 0.08. Luo’s technique exhibits the largest errors (NMBE ≈ 25%, NRMSE ≈ 62%), with ∆Eₐᵦ up to ∼60 and TAI overestimation up to ∼134%. All techniques predict low DGP with negligible deviation (NRMSE &lt; 0.02). Spectral uplifting reconstructs smooth, energy-conserving spectra with wavelength-wise errors typically &lt; 20%, yielding melanopic equivalent illuminance (EML) differences &lt; 6%. Overall, Ferwerda’s technique offers the best accuracy–practicability trade-off, Illuminance-Proxy is useful for planar diffuse surfaces when sparse ground truth is available, and spectral uplifting enables non-visual analyses from tristimulus inputs with minimal error.
DOI: 10.15468/dl.x7rbz3
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lecidea phaeops",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 13 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014798-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30948863
title: Protein function annotation (ORFs and annotation outputs)

description: This item contains processed protein-level annotation files supporting our manuscript on the evolutionary relationships between HK97-fold viruses (Duplodnaviria) and encapsulins. The work was carried out in the Luque Lab (Department of Biology, University of Miami). These files capture predicted open reading frames (ORFs) and multiple complementary annotation strategies used for downstream analyses (protein function inference, remote homology assessment, and HK97-fold detection).<b>Contents:</b><b>data_file_S5_ORFs.gb</b><br>Multi-GenBank file containing all contigs with their predicted open reading frames (ORFs), including annotations used for downstream protein-level analyses.<br><b>data_file_S6_plm_annotation.zip</b><br>Protein function predictions generated with the Virus Protein Function Protein Language Model (VPF-PLM). Includes per-protein <code>.csv</code> outputs (predicted functions and probability matrices) and <code>.pkl</code> embedding files.<br><b>data_file_S7_vibrant_annotation.zip</b><br>VIBRANT-derived annotations for environmental viral contigs, including KEGG, Pfam, VOG, and VPFAM HMM scan results, plus summary outputs describing genome size, quality, and gene content.<br><b>data_file_S8_HK97_hmm_profiles.zip</b><br>Hidden Markov Model (HMM) profiles used to detect HK97-fold proteins, including Pfam clan CL0373 (“Phage_coat”) profiles and extended profile sets from the efam database.<br><b>data_file_S9_hhpred_annotation.zip</b><br>Archive of HHpred <code>.hhr</code> output files used for remote homology and function inference; these outputs were manually reviewed for key annotations used in the manuscript.<br><b>Notes on reuse:</b> These are processed outputs and profile resources assembled for the analyses reported in the manuscript. When reusing results, please cite the associated manuscript and this Figshare item DOI, and follow any relevant terms for third-party resources (e.g., database/profile sources) as applicable.
DOI: 10.15468/dl.myr3rk
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Elaphomyces virgatosporus"
 ]
}

 The dataset includes 128 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014799-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30948863.v1
title: Protein function annotation (ORFs and annotation outputs)

description: This item contains processed protein-level annotation files supporting our manuscript on the evolutionary relationships between HK97-fold viruses (Duplodnaviria) and encapsulins. The work was carried out in the Luque Lab (Department of Biology, University of Miami). These files capture predicted open reading frames (ORFs) and multiple complementary annotation strategies used for downstream analyses (protein function inference, remote homology assessment, and HK97-fold detection).<b>Contents:</b><b>data_file_S5_ORFs.gb</b><br>Multi-GenBank file containing all contigs with their predicted open reading frames (ORFs), including annotations used for downstream protein-level analyses.<br><b>data_file_S6_plm_annotation.zip</b><br>Protein function predictions generated with the Virus Protein Function Protein Language Model (VPF-PLM). Includes per-protein <code>.csv</code> outputs (predicted functions and probability matrices) and <code>.pkl</code> embedding files.<br><b>data_file_S7_vibrant_annotation.zip</b><br>VIBRANT-derived annotations for environmental viral contigs, including KEGG, Pfam, VOG, and VPFAM HMM scan results, plus summary outputs describing genome size, quality, and gene content.<br><b>data_file_S8_HK97_hmm_profiles.zip</b><br>Hidden Markov Model (HMM) profiles used to detect HK97-fold proteins, including Pfam clan CL0373 (“Phage_coat”) profiles and extended profile sets from the efam database.<br><b>data_file_S9_hhpred_annotation.zip</b><br>Archive of HHpred <code>.hhr</code> output files used for remote homology and function inference; these outputs were manually reviewed for key annotations used in the manuscript.<br><b>Notes on reuse:</b> These are processed outputs and profile resources assembled for the analyses reported in the manuscript. When reusing results, please cite the associated manuscript and this Figshare item DOI, and follow any relevant terms for third-party resources (e.g., database/profile sources) as applicable.
DOI: 10.15468/dl.jdgadq
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammillaria solisioides",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 33 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014801-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cf928x
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Laimosemion kirovskyi",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 45 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014800-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/70d68de0-3c37-4b30-a7cd-f128faa2d098
title: Data and code for "Influence of geometrical levels of detail and inaccurate material optical properties on daylight simulation results"

description: This repository contains the data and code for the paper <strong>"Influence of geometrical levels of detail and inaccurate material optical properties on daylight simulation results."</strong><strong>Abstract:</strong> Optimising the built environment via simulations of building models hinges on standardising data acquisition. In this research, we put forward distinct levels of detail for geometry and material inputs, specifically tailored for indoor daylight applications. We primarily focus on understanding the uncertainties arising from imprecise estimations of material optical properties and incomplete geometrical inputs in climate-based indoor daylight simulations. Employing a Monte Carlo approach, we analysed six office and teaching spaces, creating 20 variations for each by altering geometrical completeness and material accuracy. The technique of excluding non-permanent objects below certain sizes in four graduated steps was used to derive and test the impact of various geometrical levels of detail. Our findings reveal that different levels of geometrical completeness lead to errors ranging from 1.08% to 18.05%. Additionally, a twofold increase in simulation time was noted when geometrical detail was enhanced relative to the most basic model. Errors stemming from imprecise definitions of material optical properties followed a normal distribution. The uncertainty in simulation outcomes showed a linear rise with increasing input material uncertainty, lying between 10% and 30%, depending on space configurations. We observed heightened uncertainty near openings, attributed to window transmittance effects. The research underscores that daylight predictions are markedly more sensitive to transmittance uncertainties than to those in reflectance, regardless of the window-to-floor ratio. These insights may help guide a more efficient data acquisition process for indoor spaces used in daylight simulations.
DOI: 10.4121/70d68de0-3c37-4b30-a7cd-f128faa2d098.v1
title: Data and code for "Influence of geometrical levels of detail and inaccurate material optical properties on daylight simulation results"

description: This repository contains the data and code for the paper <strong>"Influence of geometrical levels of detail and inaccurate material optical properties on daylight simulation results."</strong><strong>Abstract:</strong> Optimising the built environment via simulations of building models hinges on standardising data acquisition. In this research, we put forward distinct levels of detail for geometry and material inputs, specifically tailored for indoor daylight applications. We primarily focus on understanding the uncertainties arising from imprecise estimations of material optical properties and incomplete geometrical inputs in climate-based indoor daylight simulations. Employing a Monte Carlo approach, we analysed six office and teaching spaces, creating 20 variations for each by altering geometrical completeness and material accuracy. The technique of excluding non-permanent objects below certain sizes in four graduated steps was used to derive and test the impact of various geometrical levels of detail. Our findings reveal that different levels of geometrical completeness lead to errors ranging from 1.08% to 18.05%. Additionally, a twofold increase in simulation time was noted when geometrical detail was enhanced relative to the most basic model. Errors stemming from imprecise definitions of material optical properties followed a normal distribution. The uncertainty in simulation outcomes showed a linear rise with increasing input material uncertainty, lying between 10% and 30%, depending on space configurations. We observed heightened uncertainty near openings, attributed to window transmittance effects. The research underscores that daylight predictions are markedly more sensitive to transmittance uncertainties than to those in reflectance, regardless of the window-to-floor ratio. These insights may help guide a more efficient data acquisition process for indoor spaces used in daylight simulations.