cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17675256
title: SEMODS: A Validated Dataset of Open-Source Software Engineering Models (November 2025 Snapshot)

description: SEMODS (Software Engineering MODels) is a validated dataset comprising 3,427 open-source software engineering models hosted on Hugging Face. This dataset provides a structured view of the SE model ecosystem as of November 2025, addressing the lack of SE-specific catalogues in generalist model registries. Additionally, this dataset offers a standardized representation of model evaluation results and supports multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.
DOI: 10.15468/dl.4f6g6w
title: Occurrence Download

description: A dataset containing 1453 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Senna uniflora"
 ]
}

 The dataset includes 1453 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002666-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17675191
title: A Unified Multiscale Framework for De Novo Enzyme Design: Integrating Quantum Mechanics, Machine Learning, and Bayesian Uncertainty Quantification for Ab Initio Catalyst Synthesis

description: This paper presents a conceptual theoretical framework for de novo enzyme design, aiming to generate efficient biocatalysts for chemical reactions from first principles. The integration of quantum mechanical calculations, molecular dynamics simulations, and generative artificial intelligence is proposed within a Bayesian uncertainty quantification framework. A multiscale modeling approach combines quantum mechanics for transition state characterization, molecular dynamics for conformational sampling, and machine learning for sequence optimization. Fundamental mathematical equations governing catalytic efficiency are derived, including extensions of Marcus theory for enzyme environments, with a thermodynamically consistent model for enzyme preorganization that accounts for desolvation penalties and entropic contributions. Sensitivity analysis identifies critical parameters affecting catalytic activity, and Bayesian inference ensures robust uncertainty quantification. The framework is implemented in a reproducible Python pipeline. Application to olefin metathesis, a challenging abiological reaction, is demonstrated through retrospective simulation, yielding predicted turnover numbers consistent with experimental benchmarks in artificial metalloenzymes (e.g., TONs up to 1,000) [jeschek2016directed]. This work builds on advances such as anchoring of ruthenium cofactors in proteins [jeschek2016directed], providing a foundation for enzyme design platforms while addressing limitations in current methodologies.
DOI: 10.5281/zenodo.17675190
title: A Unified Multiscale Framework for De Novo Enzyme Design: Integrating Quantum Mechanics, Machine Learning, and Bayesian Uncertainty Quantification for Ab Initio Catalyst Synthesis

description: This paper presents a conceptual theoretical framework for de novo enzyme design, aiming to generate efficient biocatalysts for chemical reactions from first principles. The integration of quantum mechanical calculations, molecular dynamics simulations, and generative artificial intelligence is proposed within a Bayesian uncertainty quantification framework. A multiscale modeling approach combines quantum mechanics for transition state characterization, molecular dynamics for conformational sampling, and machine learning for sequence optimization. Fundamental mathematical equations governing catalytic efficiency are derived, including extensions of Marcus theory for enzyme environments, with a thermodynamically consistent model for enzyme preorganization that accounts for desolvation penalties and entropic contributions. Sensitivity analysis identifies critical parameters affecting catalytic activity, and Bayesian inference ensures robust uncertainty quantification. The framework is implemented in a reproducible Python pipeline. Application to olefin metathesis, a challenging abiological reaction, is demonstrated through retrospective simulation, yielding predicted turnover numbers consistent with experimental benchmarks in artificial metalloenzymes (e.g., TONs up to 1,000) [jeschek2016directed]. This work builds on advances such as anchoring of ruthenium cofactors in proteins [jeschek2016directed], providing a foundation for enzyme design platforms while addressing limitations in current methodologies.
DOI: 10.15468/dl.fmmkrp
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hoplopeza colombiana"
 ]
}

 The dataset includes 17 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002667-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16966285
title: TCGA-SBU-TIL-Maps: AI-derived Tumor Infiltrating Lymphocyte maps for the TCGA collections

description: This collection contains the spatial distributions and patterns of tumor-infiltrating lymphocytes (TILs) computed at the patch level in whole slide tissue images from a subset of cancer types in TCGA. The spatial distributions and patterns  were computed automatically using two distinct AI algorithms developed by the Joel Saltz lab at Stony Brook University. The collection includes both the binary classification (TIL-positive or not) and the prediction probability generated by the AI model for each included whole slide image.  Each image in the collection was partitioned into a uniform grid of 50x50 square micron image patches; each patch was assigned a classification probability by the AI models. 

The computational results  included in this dataset were produced using the algorithms described in [1,2]. The data generated using the algorithm described in [1] were shared earlier on The Cancer Imaging Archive [3].

This dataset contains the annotations harmonized into DICOM Segmentation representation.

This dataset is available from the NCI Imaging Data Commons (IDC), and can be explored interactively in the IDC Portal using this link: https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TCGA-SBU-TIL-Maps

Specific files included in the record are organized in the manifests by the individual TCGA collections that were processed. The suffix of the manifest indicates its content, which is the list of pointers to the public Google Cloud Storage (GCS) or Amazon Web Services (AWS) buckets containing the files included in the collection:





-gcs.s5cmd: GCS-based manifest (to download the files described in the manifest, execute this command: pip install --upgrade idc-index && idc download manifest).




-aws.s5cmd: AWS-based manifest (to download the files described in the manifest, execute this command: pip install --upgrade idc-index && idc download manifest).




-dcf.dcf: Gen3-based manifest (see details in https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids). 



[1] Saltz, Joel, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras et al. "Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images." Cell reports 23, no. 1 (2018): 181-193.

[2] Abousamra, Shahira, Rajarsi Gupta, Le Hou, Rebecca Batiste, Tianhao Zhao, Anand Shankar, Arvind Rao et al. "Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer." Frontiers in oncology 11 (2022): 806603.

[3] Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., The Cancer Genome Atlas Research Network, Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., & Thorsson, V. (2018). Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.Y75F9W1
DOI: 10.5281/zenodo.16966286
title: TCGA-SBU-TIL-Maps: AI-derived Tumor Infiltrating Lymphocyte maps for the TCGA collections

description: This collection contains the spatial distributions and patterns of tumor-infiltrating lymphocytes (TILs) computed at the patch level in whole slide tissue images from a subset of cancer types in TCGA. The spatial distributions and patterns  were computed automatically using two distinct AI algorithms developed by the Joel Saltz lab at Stony Brook University. The collection includes both the binary classification (TIL-positive or not) and the prediction probability generated by the AI model for each included whole slide image.  Each image in the collection was partitioned into a uniform grid of 50x50 square micron image patches; each patch was assigned a classification probability by the AI models. 

The computational results  included in this dataset were produced using the algorithms described in [1,2]. The data generated using the algorithm described in [1] were shared earlier on The Cancer Imaging Archive [3].

This dataset contains the annotations harmonized into DICOM Segmentation representation.

This dataset is available from the NCI Imaging Data Commons (IDC), and can be explored interactively in the IDC Portal using this link: https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TCGA-SBU-TIL-Maps

Specific files included in the record are organized in the manifests by the individual TCGA collections that were processed. The suffix of the manifest indicates its content, which is the list of pointers to the public Google Cloud Storage (GCS) or Amazon Web Services (AWS) buckets containing the files included in the collection:





-gcs.s5cmd: GCS-based manifest (to download the files described in the manifest, execute this command: pip install --upgrade idc-index && idc download manifest).




-aws.s5cmd: AWS-based manifest (to download the files described in the manifest, execute this command: pip install --upgrade idc-index && idc download manifest).




-dcf.dcf: Gen3-based manifest (see details in https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids). 



[1] Saltz, Joel, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras et al. "Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images." Cell reports 23, no. 1 (2018): 181-193.

[2] Abousamra, Shahira, Rajarsi Gupta, Le Hou, Rebecca Batiste, Tianhao Zhao, Anand Shankar, Arvind Rao et al. "Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer." Frontiers in oncology 11 (2022): 806603.

[3] Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., The Cancer Genome Atlas Research Network, Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., & Thorsson, V. (2018). Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.Y75F9W1
DOI: 10.15468/dl.qv662f
title: Occurrence Download

description: A dataset containing 92 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "StateProvince is one of (Cundinamarca, Boyacá)",
 "TaxonKey is one of (Hyloscirtus bogotensis [GBIF Backbone Taxonomy])"
 ]
 }
 ]
}

 The dataset includes 92 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002669-251120083545085/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.cc2dnlpc
title: CCDC 2224333: Experimental Crystal Structure Determination

description: Related Article: T.G. Ashebr, Ahlam I. Al-Sulami, Zhifang Ma, Xiao-Lei Li|2024|Struct.Chem.|35|2015|doi:10.1007/s11224-024-02342-0
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: 10.5517/ccdc.csd.cc2kjk0p
title: CCDC 2369392: Experimental Crystal Structure Determination

description: Related Article: Li Zhang, Shuhai Qiu, Shunlong Hu, Hanren Xu, Xuwen Sun, Wei Jiang, Dahui Qu, Zhaohui Wang|2025|Sci.China.Chem.|68|3154|doi:10.1007/s11426-024-2474-6
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: 10.5517/ccdc.csd.cc2l3d04
title: CCDC 2386535: Experimental Crystal Structure Determination

description: Related Article: Huan Zhang, Chenchen Ma, Fengfeng Yin, Yu Jiang, Jiaqi Du, Peifang Lu, Jianhua Wang, Jitian Liu, Xiaoxun Li|2025|Chin.J.Chem.|43|3157|doi:10.1002/cjoc.70191
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: 10.5517/ccdc.csd.cc2mr6by
title: CCDC 2435370: Experimental Crystal Structure Determination

description: Related Article: Zuzana Bielková, Dominika Kendrová, Ján Titiš, Erika Samoľová, Ondřej Malina, Gerald Dräger, Ján Moncoľ, Roman Boča, Cyril Rajnák|2026|J.Mol.Struct.|1352||doi:10.1016/j.molstruc.2025.144535
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: 10.5517/ccdc.csd.cc2mr6d0
title: CCDC 2435372: Experimental Crystal Structure Determination

description: Related Article: Zuzana Bielková, Dominika Kendrová, Ján Titiš, Erika Samoľová, Ondřej Malina, Gerald Dräger, Ján Moncoľ, Roman Boča, Cyril Rajnák|2026|J.Mol.Struct.|1352||doi:10.1016/j.molstruc.2025.144535
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: 10.5517/ccdc.csd.cc2nk21c
title: CCDC 2459262: Experimental Crystal Structure Determination

description: Related Article: Yang Chen, Mei Zuo, Jinlong Zhao, Shu Xiao, Min Yang, Li Liu, Shuzhong He, Jian Huang|2026|J.Mol.Struct.|1352||doi:10.1016/j.molstruc.2025.144548
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: 10.5517/ccdc.csd.cc2npg72
title: CCDC 2463484: Experimental Crystal Structure Determination

description: Related Article: Sichen Li, Rui Zhu, Yu Li, Shuaibo Shi, Jiacheng Li, Kun Zhang|2026|Sens.Actuators,B|448||doi:10.1016/j.snb.2025.139039
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: 10.5517/ccdc.csd.cc2npg83
title: CCDC 2463485: Experimental Crystal Structure Determination

description: Related Article: Sichen Li, Rui Zhu, Yu Li, Shuaibo Shi, Jiacheng Li, Kun Zhang|2026|Sens.Actuators,B|448||doi:10.1016/j.snb.2025.139039
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: 10.5517/ccdc.csd.cc2p1mcr
title: CCDC 2474214: Experimental Crystal Structure Determination

description: Related Article: Chaonan Liu, Yu Shi, Qichao Yan, Linmeng Lu, Li Ma, Ruikang Zhang, Zhengguo Lin, Yuanzhe Gao|2026|J.Mol.Struct.|1352||doi:10.1016/j.molstruc.2025.144490
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: 10.5517/ccdc.csd.cc2p1mds
title: CCDC 2474215: Experimental Crystal Structure Determination

description: Related Article: Chaonan Liu, Yu Shi, Qichao Yan, Linmeng Lu, Li Ma, Ruikang Zhang, Zhengguo Lin, Yuanzhe Gao|2026|J.Mol.Struct.|1352||doi:10.1016/j.molstruc.2025.144490
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: 10.5517/ccdc.csd.cc2p1mft
title: CCDC 2474216: Experimental Crystal Structure Determination

description: Related Article: Chaonan Liu, Yu Shi, Qichao Yan, Linmeng Lu, Li Ma, Ruikang Zhang, Zhengguo Lin, Yuanzhe Gao|2026|J.Mol.Struct.|1352||doi:10.1016/j.molstruc.2025.144490
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.2vrq3b
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ceratinia iolaia"
 ]
}

 The dataset includes 22 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002668-251120083545085/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.cc2p24xv
title: CCDC 2474727: Experimental Crystal Structure Determination

description: Related Article: S. Karthik, C. Sandhya, Ch. Ajay, B. Sridhar, B.V. Subba Reddy|2026|Tetrahedron Lett.|174||doi:10.1016/j.tetlet.2025.155871
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: 10.5517/ccdc.csd.cc2p6mdy
title: CCDC 2479020: Experimental Crystal Structure Determination

description: Related Article: Tom L. Petrick, Thomas Gelbrich, Sven Nerdinger, Marijan Stefinovic, Doris E. Braun|2026|J.Mol.Struct.|1352||doi:10.1016/j.molstruc.2025.144513
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: 10.5517/ccdc.csd.cc2lzxh0
title: CCDC 2412993: Experimental Crystal Structure Determination

description: Related Article: Linfeng Lan, Yuxing Zhou, Liang Li, Chenguang Wang, Panče Naumov, Hongyu Zhang|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c15122
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: 10.5517/ccdc.csd.cc2pvwyd
title: CCDC 2498505: Experimental Crystal Structure Determination

description: Related Article: D.J. Webb, E.A. Bennett, R.T. Boeré|2025|Acta Crystallogr.,Sect.E:Cryst.Commun.|81|1099|doi:10.1107/S2056989025009454
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.kafsus
title: Occurrence Download

description: A dataset containing 1110 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hypophthalmichthys molitrix",
 "Year 2000-2025"
 ]
}

 The dataset includes 1110 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002672-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.huewer
title: Occurrence Download

description: A dataset containing 2975 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Catharanthus roseus",
 "Year 1816-2025"
 ]
}

 The dataset includes 2975 records from 292 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002673-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b7hdtp
title: Occurrence Download

description: A dataset containing 1063 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Alnus glutinosa",
 "Year 2022-2025"
 ]
}

 The dataset includes 1063 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002674-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.