cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18321402
title: Zornia multinervosa Burkart ex Bacigalupo. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:56
DOI: 10.5281/zenodo.18321401
title: Zornia multinervosa Burkart ex Bacigalupo. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:56
DOI: 10.48610/8bb06e2
title: Imagery modulates the pupillary response

description: Imagery modulates the pupillary response: Raw data and analysis code.Pupils_Imagery_Analysis_Code: zip file containing Matlab analysis scriptPupils_Imagery_Raw_Data: zip file containing Matlab Raw Data Files
DOI: 10.15468/dl.ncqx6e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((103.147 4.41916, 103.076529 4.413591, 103.007797 4.397018, 102.942498 4.369852, 102.882242 4.332762, 102.828514 4.286663, 102.782638 4.232692, 102.745742 4.172178, 102.718734 4.106613, 102.702276 4.037613, 102.696771 3.966877, 102.702352 3.896147, 102.718878 3.827164, 102.74594 3.761626, 102.782871 3.701146, 102.828759 3.647211, 102.882475 3.60115, 102.942696 3.564093, 103.007941 3.536954, 103.076605 3.520399, 103.147 3.514835, 103.217395 3.520399, 103.286059 3.536954, 103.351304 3.564093, 103.411525 3.60115, 103.465241 3.647211, 103.511129 3.701146, 103.54806 3.761626, 103.575122 3.827164, 103.591648 3.896147, 103.597229 3.966877, 103.591724 4.037613, 103.575266 4.106613, 103.548258 4.172178, 103.511362 4.232692, 103.465486 4.286663, 103.411758 4.332762, 103.351502 4.369852, 103.286203 4.397018, 103.217471 4.413591, 103.147 4.41916))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001464-260120142942310/datasets/export for details.
DOI: 10.15468/dl.ef2cxq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((109.531 0.674184, 109.460732 0.668617, 109.392195 0.652052, 109.327076 0.624897, 109.26698 0.587821, 109.213385 0.5417380000000001, 109.167612 0.487781, 109.130787 0.42728, 109.103818 0.361725, 109.087367 0.29273, 109.081839 0.221993, 109.087371 0.151257, 109.103826 0.082262, 109.130798 0.016709, 109.167625 -0.04379, 109.213399 -0.097745, 109.266993 -0.143826, 109.327087 -0.1809, 109.392203 -0.208053, 109.460736 -0.224618, 109.531 -0.230185, 109.601264 -0.224618, 109.669797 -0.208053, 109.734913 -0.1809, 109.795007 -0.143826, 109.848601 -0.097745, 109.894375 -0.04379, 109.931202 0.016709, 109.958174 0.082262, 109.974629 0.151257, 109.980161 0.221993, 109.974633 0.29273, 109.958182 0.361725, 109.931213 0.42728, 109.894388 0.487781, 109.848615 0.5417380000000001, 109.79502 0.587821, 109.734924 0.624897, 109.669805 0.652052, 109.601268 0.668617, 109.531 0.674184))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001462-260120142942310/datasets/export for details.
DOI: 10.5281/zenodo.18321406
title: Turneroideae Eaton. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:56
DOI: 10.5281/zenodo.18321405
title: Turneroideae Eaton. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:56
DOI: 10.5281/zenodo.18320768
title: Darwin Operator Symmetry Atlas (DOSA) v2.1.2

description: Darwin Operator Symmetry Atlas (DOSA) - A comprehensive database of operator-defined symmetries in bacterial replicons.
This dataset contains 24,090,832 window-level symmetry measurements across 959 bacterial replicons, analyzing circular sequence symmetries under the dihedral group Dn (rotations + reverse-complement reflection).
Key Statistics

Total Windows: 24,090,832 records (1.4 GB)
Unique Replicons: 959 bacterial replicons
Window Sizes: 100bp (18.5M), 500bp (3.7M), 1000bp (1.85M)
Quality Validation: 14/14 checks passed (100.0%)
Mean orbit_ratio: 0.99999871 (near-perfect asymmetry)
Palindromes: 3 / 24M windows (0.00001%)
Biological Insights

99.9998% of windows have orbit_ratio ≥ 0.95
Only 37 windows show significant symmetry
Bacterial genomes are informationally complex, not periodic
Files Included

atlas_windows_exact.csv (1.4 GB) - 24M window-level symmetry measurements
atlas_replicons.csv (16 MB) - 114K replicon metadata records
atlas_knowledge.jsonl (627 MB) - 1.1M epistemic knowledge records
Statistics: overall_stats.json, by_window_size.csv, orbit_ratio_histogram.csv, etc.
Documentation: README.md, QUALITY_REPORT.md, STATISTICS_REPORT.md
Reproducibility
Fully reproducible with:

✓ Deterministic outputs (no random seeds)
✓ SHA256 checksums for all genome downloads
✓ Complete audit trail in git history
✓ Source code: https://github.com/agourakis82/darwin-atlas
Mathematical Framework
Dihedral Group Dn: Symmetry of circular sequences under rotations + RC reflection

orbit_size ∈ [1, 2n] where orbit_size divides 2n (Lagrange's theorem)
orbit_ratio = orbit_size / (2n) ∈ [1/(2n), 1.0]
Generic sequences: orbit_ratio ≈ 1.0 (full orbit, maximal asymmetry)
See README.md for complete documentation.
description: v2.1.2 adds windows_subset_replicons.csv and updates validation/statistics reports to explicitly document the two-layer structure: a 114,245-replicon catalog layer (atlas_replicons.csv, atlas_knowledge.jsonl) and a deep window-atlas subset over 959 replicons (atlas_windows_exact.csv). Data tables are unchanged from v2.1.1; this is a metadata+documentation alignment release for Scientific Data.
DOI: 10.5281/zenodo.18312341
title: Darwin Operator Symmetry Atlas (DOSA) v2.1.2

description: Darwin Operator Symmetry Atlas (DOSA) - A comprehensive database of operator-defined symmetries in bacterial replicons.
This dataset contains 24,090,832 window-level symmetry measurements across 959 bacterial replicons, analyzing circular sequence symmetries under the dihedral group Dn (rotations + reverse-complement reflection).
Key Statistics

Total Windows: 24,090,832 records (1.4 GB)
Unique Replicons: 959 bacterial replicons
Window Sizes: 100bp (18.5M), 500bp (3.7M), 1000bp (1.85M)
Quality Validation: 14/14 checks passed (100.0%)
Mean orbit_ratio: 0.99999871 (near-perfect asymmetry)
Palindromes: 3 / 24M windows (0.00001%)
Biological Insights

99.9998% of windows have orbit_ratio ≥ 0.95
Only 37 windows show significant symmetry
Bacterial genomes are informationally complex, not periodic
Files Included

atlas_windows_exact.csv (1.4 GB) - 24M window-level symmetry measurements
atlas_replicons.csv (16 MB) - 114K replicon metadata records
atlas_knowledge.jsonl (627 MB) - 1.1M epistemic knowledge records
Statistics: overall_stats.json, by_window_size.csv, orbit_ratio_histogram.csv, etc.
Documentation: README.md, QUALITY_REPORT.md, STATISTICS_REPORT.md
Reproducibility
Fully reproducible with:

✓ Deterministic outputs (no random seeds)
✓ SHA256 checksums for all genome downloads
✓ Complete audit trail in git history
✓ Source code: https://github.com/agourakis82/darwin-atlas
Mathematical Framework
Dihedral Group Dn: Symmetry of circular sequences under rotations + RC reflection

orbit_size ∈ [1, 2n] where orbit_size divides 2n (Lagrange's theorem)
orbit_ratio = orbit_size / (2n) ∈ [1/(2n), 1.0]
Generic sequences: orbit_ratio ≈ 1.0 (full orbit, maximal asymmetry)
See README.md for complete documentation.
description: v2.1.2 adds windows_subset_replicons.csv and updates validation/statistics reports to explicitly document the two-layer structure: a 114,245-replicon catalog layer (atlas_replicons.csv, atlas_knowledge.jsonl) and a deep window-atlas subset over 959 replicons (atlas_windows_exact.csv). Data tables are unchanged from v2.1.1; this is a metadata+documentation alignment release for Scientific Data.
DOI: 10.5281/zenodo.18321409
title: Intrusion Datasets
DOI: 10.5281/zenodo.18321410
title: Intrusion Datasets
DOI: 10.15468/dl.wyhaxu
title: Occurrence Download

description: A dataset containing 57 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((100.987 4.42016, 100.916529 4.41459, 100.847797 4.398018, 100.782498 4.370852, 100.722242 4.333762, 100.668514 4.287663, 100.622637 4.233692, 100.585742 4.173178, 100.558733 4.107613, 100.542275 4.038613, 100.53677 3.967877, 100.542351 3.897147, 100.558877 3.828164, 100.58594 3.762626, 100.62287 3.702146, 100.668759 3.648211, 100.722474 3.60215, 100.782696 3.565093, 100.847941 3.537954, 100.916605 3.521399, 100.987 3.515835, 101.057395 3.521399, 101.126059 3.537954, 101.191304 3.565093, 101.251526 3.60215, 101.305241 3.648211, 101.35113 3.702146, 101.38806 3.762626, 101.415123 3.828164, 101.431649 3.897147, 101.43723 3.967877, 101.431725 4.038613, 101.415267 4.107613, 101.388258 4.173178, 101.351363 4.233692, 101.305486 4.287663, 101.251758 4.333762, 101.191502 4.370852, 101.126203 4.398018, 101.057471 4.41459, 100.987 4.42016))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 57 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001468-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2n1hdm
title: CCDC 2444300: Experimental Crystal Structure Determination

description: Related Article: ziyang wang|2026|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.29e6jd
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((99.71599999999999 0.676184, 99.645732 0.670617, 99.577195 0.654052, 99.51207599999999 0.626897, 99.45198000000001 0.589821, 99.398385 0.5437380000000001, 99.35261199999999 0.489781, 99.315787 0.42928, 99.28881800000001 0.363725, 99.272367 0.29473, 99.266839 0.223993, 99.27237100000001 0.153257, 99.288826 0.084262, 99.315798 0.018709, 99.352625 -0.04179, 99.398399 -0.095745, 99.451993 -0.141826, 99.51208699999999 -0.1789, 99.577203 -0.206053, 99.645736 -0.222618, 99.71599999999999 -0.228185, 99.786264 -0.222618, 99.854797 -0.206053, 99.91991299999999 -0.1789, 99.980007 -0.141826, 100.033601 -0.095745, 100.079375 -0.04179, 100.116202 0.018709, 100.143174 0.084262, 100.159629 0.153257, 100.165161 0.223993, 100.159633 0.29473, 100.143182 0.363725, 100.116213 0.42928, 100.079388 0.489781, 100.033615 0.5437380000000001, 99.98002 0.589821, 99.91992399999999 0.626897, 99.854805 0.654052, 99.78626800000001 0.670617, 99.71599999999999 0.676184))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001469-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18321419
title: Verbena sessilis (Cham.) Kuntze. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:56
DOI: 10.5281/zenodo.18321418
title: Verbena sessilis (Cham.) Kuntze. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:56
DOI: 10.5281/zenodo.18321427
title: Solanum xanti A.Gray. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:55
DOI: 10.5281/zenodo.18321426
title: Solanum xanti A.Gray. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:55
DOI: 10.57760/sciencedb.hjs.00610
title: CFD study on the bubble dynamics characteristics of water-cooled reactors based on AMR
title: 基于自适应网格的水冷堆气泡动力学特性的CFD研究

description: Background: Computational Fluid Dynamics (CFD) is a crucial tool for analyzing bubble dynamics in water-cooled reactors. Current studies on gas-liquid two-phase flow in such reactors predominantly employ fixed grids, which require a large number of grid cells to ensure 3D simulation accuracy at the gas-liquid interface, leading to high computational resource demands. Purpose: This study aims to apply Adaptive Mesh Refinement (AMR) technology to reduce computational cost while maintaining accurate capture of bubble dynamics in 3D simulations of water-cooled reactors. Methods: The AMR technique, which dynamically adjusts grid density, was combined with the VOF (Volume of Fluid) model. Three-dimensional numerical simulations were performed on typical bubble motion and phase change scenarios. Results: The research findings demonstrate that the AMR technique can effectively reduce the overall number of grid cells while appropriately refining the mesh at the gas-liquid interface. This approach accurately captures the kinematic characteristics of bubbles. Conclusions: The AMR technique shows good applicability for three-dimensional simulation of single bubbles, as it can lower computational costs while ensuring accurate prediction of bubble motion behavior.
description: Background: Computational Fluid Dynamics (CFD) is a crucial tool for analyzing bubble dynamics in water-cooled reactors. Current studies on gas-liquid two-phase flow in such reactors predominantly employ fixed grids, which require a large number of grid cells to ensure 3D simulation accuracy at the gas-liquid interface, leading to high computational resource demands. Purpose: This study aims to apply Adaptive Mesh Refinement (AMR) technology to reduce computational cost while maintaining accurate capture of bubble dynamics in 3D simulations of water-cooled reactors. Methods: The AMR technique, which dynamically adjusts grid density, was combined with the VOF (Volume of Fluid) model. Three-dimensional numerical simulations were performed on typical bubble motion and phase change scenarios. Results: The research findings demonstrate that the AMR technique can effectively reduce the overall number of grid cells while appropriately refining the mesh at the gas-liquid interface. This approach accurately captures the kinematic characteristics of bubbles. Conclusions: The AMR technique shows good applicability for three-dimensional simulation of single bubbles, as it can lower computational costs while ensuring accurate prediction of bubble motion behavior.
DOI: 10.57760/sciencedb.hjs.00608
title: Studies of muon track reconstruction based on timing and charge in Water Cherenkov Detector
title: 基于时间与电荷的径迹重建算法在水切伦科夫 探测器中的应用研究

description:  [Background]The reconstruction of cosmic ray tracks is highly important in large neutrino detectors. Precise track reconstruction can effectively eliminate isotopic backgrounds around the tracks without requiring anti-coincidence measures across the entire detector. The muon reconstruction in the water buffer region (interlayer water) of the central detector (CD) has not been studied, which called CWB muon. The CWB muon event number is about 20% of the muons those through the CD. [Purpose] This study aims to measure the mass ordering of neutrinos on the background, veto strategy and effective volume cut. [Methods] This study utilized the earliest photon arrival time and charge information from the photomultiplier tube signals to construct a charge-weighted least squares time equation. Through simulation studies and parameter adjustments, this method can be successfully applied to large water Cherenkov detectors. Using the JUNO (Jiangmen Underground Neutrino Experiment) detector as a case study, Geant4 simulations were performed to optimize parameters. [Results] The method ultimately achieves muon track reconstruction in the water layer, with a spatial resolution better than 12 cm, an angular resolution better than 0.4 degrees, and a reconstruction efficiency of approximately 97%. [Conclusions] This result can help JUNO to further reduce the background, which plays an important role in the sensitivity analysis of JUNO in the future. 
description:  [Background]The reconstruction of cosmic ray tracks is highly important in large neutrino detectors. Precise track reconstruction can effectively eliminate isotopic backgrounds around the tracks without requiring anti-coincidence measures across the entire detector. The muon reconstruction in the water buffer region (interlayer water) of the central detector (CD) has not been studied, which called CWB muon. The CWB muon event number is about 20% of the muons those through the CD. [Purpose] This study aims to measure the mass ordering of neutrinos on the background, veto strategy and effective volume cut. [Methods] This study utilized the earliest photon arrival time and charge information from the photomultiplier tube signals to construct a charge-weighted least squares time equation. Through simulation studies and parameter adjustments, this method can be successfully applied to large water Cherenkov detectors. Using the JUNO (Jiangmen Underground Neutrino Experiment) detector as a case study, Geant4 simulations were performed to optimize parameters. [Results] The method ultimately achieves muon track reconstruction in the water layer, with a spatial resolution better than 12 cm, an angular resolution better than 0.4 degrees, and a reconstruction efficiency of approximately 97%. [Conclusions] This result can help JUNO to further reduce the background, which plays an important role in the sensitivity analysis of JUNO in the future.
DOI: 10.15468/dl.wzau43
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((102.692 4.42016, 102.621529 4.41459, 102.552797 4.398018, 102.487498 4.370852, 102.427242 4.333762, 102.373514 4.287663, 102.327637 4.233692, 102.290742 4.173178, 102.263733 4.107613, 102.247275 4.038613, 102.24177 3.967877, 102.247351 3.897147, 102.263877 3.828164, 102.29094 3.762626, 102.32787 3.702146, 102.373759 3.648211, 102.427474 3.60215, 102.487696 3.565093, 102.552941 3.537954, 102.621605 3.521399, 102.692 3.515835, 102.762395 3.521399, 102.831059 3.537954, 102.896304 3.565093, 102.956526 3.60215, 103.010241 3.648211, 103.05613 3.702146, 103.09306 3.762626, 103.120123 3.828164, 103.136649 3.897147, 103.14223 3.967877, 103.136725 4.038613, 103.120267 4.107613, 103.093258 4.173178, 103.056363 4.233692, 103.010486 4.287663, 102.956758 4.333762, 102.896502 4.370852, 102.831203 4.398018, 102.762471 4.41459, 102.692 4.42016))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001471-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.