cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10374883
title: Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases

description: Data underlying the manuscript 'Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al.

The repository contains:

Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided.

NMR data - raw data and MestReNova files. Also includes HPLC-MS traces of isolated compounds.

HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data.

SCA data - Python scripts, GenBank files and produced data underlying the SCA.

Bioactivity data - Data underlying the toxicity assays in Figure S122.
DOI: 10.5281/zenodo.10379414
title: Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases

description: Data underlying the manuscript 'Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al.

The repository contains:

Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided.

NMR data - raw data and MestReNova files. Also includes HPLC-MS traces of isolated compounds.

HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data.

SCA data - Python scripts, GenBank files and produced data underlying the SCA.

Bioactivity data - Data underlying the toxicity assays in Figure S122.
DOI: 10.5281/zenodo.10651116
title: Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases

description: Data underlying the manuscript 'Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al.

The repository contains:

Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided.

NMR data - raw data and MestReNova files. Also includes HPLC-MS traces of isolated compounds.

HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data.

SCA data - Python scripts, GenBank files and produced data underlying the SCA.

Bioactivity data - Data underlying the toxicity assays in Figure S122.
DOI: 10.5281/zenodo.8146702
title: Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases

description: Data underlying the manuscript 'Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al.

The repository contains:

Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided.

NMR data - raw data and MestReNova files. Also includes HPLC-MS traces of isolated compounds.

HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data.

SCA data - Python scripts, GenBank files and produced data underlying the SCA.

Bioactivity data - Data underlying the toxicity assays in Figure S122.
DOI: 10.5281/zenodo.8146703
title: Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases

description: Data underlying the manuscript 'Evolution-Guided Engineering of <em>Trans</em>-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al. The repository contains: Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided. NMR data - raw data and MestReNova files HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data. SCA data - Python scripts, GenBank files and produced data underlying the SCA.
DOI: 10.14473/csda/izpzbf/gegjph
title: K_ISSP00005.pdf

description: Karty
DOI: 10.14473/csda/izpzbf/nndubj
title: issp96data.tab

description: Data
DOI: 10.14473/csda/izpzbf/oirjdh
title: D_ISSP00005.pdf

description: Dotazník
DOI: 10.14473/csda/izpzbf
title: Pracovní orientace (ISSP 1997) - Česká republika

description: Mezinárodní program výzkumů ISSP:ISSP (International Social Survey Programme) je dlouhodobý mezinárodní výzkumný projekt, který existuje od roku 1983. Tento výzkumný projekt je založen na mezinárodní a meziprojektové spolupráci v oblasti výzkumu v sociálních vědách. Je zaměřen na významná témata týkající se postojů. ISSP kombinuje dvě roviny srovnání - srovnání v mezinárodní a v časové perspektivě. Výzkumné aktivity probíhají nepřetržitě v ročních cyklech. Výzkum Pracovní orientace v České republice byl realizován v rámci mezinárodního programu ISSP (Interenational Social Survey Programme) celkem v 22 zemích. Tým v každé z participujících zemí aplikoval identický modul dotazníku vyvinutý mezinárodním výzkumným týmem. Základní témata společného modulu byla následující: - obecné postoje k práci a volnému času (tj. práce vs. volný čas, pracovní etika, práce a osobnost, efekty nezaměstnanosti), - organizace práce (tj. postoje k samostatnému podnikání, soukromý vs. státní sektor, velké vs. malé podniky, faktory ovlivňující služební postup, obsah práce) a - kolektivní zájmy (odbory). České šetření obsahovalo navíc otázky zaměřené na změny zaměstnání po roce 1989, faktory vedoucí k nerovnosti příjmů a dosahování sociálního postavení. Český datový soubor, plná verze - obsahuje odpovídající proměnné pro všechny otázky dotazníku (tj. modul ISSP a sadu českých národních proměnných) plus modul zkonstruovaných standardních proměnných určujících sociální postavení respondentů (sociální třída EGP, socio-ekonomický index statusu povolání ISEI a prestiž povolání SIOPS).
DOI: 10.14473/csda/izpzbf/ck0nxd
title: TI_ISSP00005.pdf

description: Technická informace
DOI: 10.14473/csda/izpzbf/wqzuxq
title: 0006_issp97.tab

description: Data
DOI: 10.5281/zenodo.4896146
title: Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 2

description: The dataset named SEBAL evapotranspiration in China (SEBAL ET)  characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance.  SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.
DOI: 10.5281/zenodo.10852612
title: DistalPhalanxOutlineAgeGroup

description: This series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems {\em DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup} involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW}, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

This is a pre-processed version of the dataset saved in numpy format. The original dataset is obtained from here.

The dataset consists of univariate time series associated with a class label. It can be loaded as follows:

loaded_data = np.load("PHAL.npz")
Xtr = loaded_data['Xtr'] # Training data of shape (400, 80)
Ytr = loaded_data['Ytr'] # Training labels of shape (400, 1)
Xte = loaded_data['Xte'] # Test data of shape (139, 80)
Yte = loaded_data['Yte'] # Test labels of shape (139, 1)
DOI: 10.5281/zenodo.10852613
title: DistalPhalanxOutlineAgeGroup

description: This series of 11 classification problems were created as part of Luke Davis's PhD titled "Predictive Modelling of Bone Ageing". They are all derived from the same images, extracted from Cao et al. "Digital hand atlas and web-based bone age assessment: system design and implementation". They are designed to test the efficacy of hand and bone outline detection and whether these outlines could be helpful in bone age prediction. Algorithms to automatically extract the hand outlines and then the outlines of three bones of the middle finger (proximal, middle and distal phalanges) were applied to over 1300 images, and three human evaluators labelled the output of the image outlining as correct or incorrect. This generated three classification problems: DistalPhalanxOutlineCorrect; MiddlePhalanxOutlineCorrect; and ProximalPhalanxOutlineCorrect. The next stage of the project was to use the outlines to predict information about the subjects age. The three problems {\em DistalPhalanxOutlineAgeGroup, MiddlePhalanxOutlineAgeGroup and ProximalPhalanxOutlineAgeGroup} involve using the outline of one of the phalanges to predict whether the subject is one of three age groups: 0-6 years old, 7-12 years old and 13-19 years old. Note that these problems are aligned by subject, and hence can be treated as a multi dimensional TSC problem. Problem Phalanges contains the concatenation of all three problems. Bone age estimation is usually performed by an expert with an algorithm called Tanner-Whitehouse. This involves scoring each bone into one of seven categories based on the stage of development. The final three bone image classification problems, DistalPhalanxTW, MiddlePhalanxTW and ProximalPhalanxTW}, involve predicting the Tanner-Whitehouse score (as labelled by a human expert) from the outline.

This is a pre-processed version of the dataset saved in numpy format. The original dataset is obtained from here.

The dataset consists of univariate time series associated with a class label. It can be loaded as follows:

loaded_data = np.load("PHAL.npz")
Xtr = loaded_data['Xtr'] # Training data of shape (400, 80)
Ytr = loaded_data['Ytr'] # Training labels of shape (400, 1)
Xte = loaded_data['Xte'] # Test data of shape (139, 80)
Yte = loaded_data['Yte'] # Test labels of shape (139, 1)
DOI: 10.48420/24871794.v1
title: Sample-size-balanced GTEx v8 TWAS models

description: Gene expression predictive model - 49 GTEx (v8) tissues
DOI: 10.48420/24871794
title: Sample-size-balanced Genotype-Tissue Expression (GTEx) v8 prediction models for transcriptome-wide association studies (TWAS)

description: Gene expression prediction model - 49 Genotype-Tissue Expression (GTEx, v8) tissues. More details about the models could be found in Methods "Transcriptome-wide association studies across 49 human cell-types and tissues" in paper "Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets" (https://doi.org/10.1038/s41467-024-46132-y).If you use this data, please cite the following paper: Xu, X., Khunsriraksakul, C., Eales, J.M. et al. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat Commun 15, 2359 (2024). https://doi.org/10.1038/s41467-024-46132-y
DOI: 10.48420/24871794.v2
title: Sample-size-balanced Genotype-Tissue Expression (GTEx) v8 prediction models for transcriptome-wide association studies (TWAS)

description: Gene expression prediction model - 49 Genotype-Tissue Expression (GTEx, v8) tissues. More details about the models could be found in Methods "Transcriptome-wide association studies across 49 human cell-types and tissues" in paper "Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets" (https://doi.org/10.1038/s41467-024-46132-y).If you use this data, please cite the following paper: Xu, X., Khunsriraksakul, C., Eales, J.M. et al. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat Commun 15, 2359 (2024). https://doi.org/10.1038/s41467-024-46132-y
DOI: 10.5281/zenodo.4218412
title: Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 1

description: The dataset named SEBAL evapotranspiration in China (SEBAL ET)  characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance.  SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.
DOI: 10.5281/zenodo.10410258
title: Kinodata-3D: an in silico kinase-ligand complex dataset for kinase-focused machine learning.

description: Project Description

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While the inclusion of 3D complex information is considered to be beneficial, structural ML for affinity prediction suffers from data scarcity. We provide kinodata-3D, a dataset of ~138 000 docked complexes to enable more robust training of 3D-based ML models for kinase activity prediction (see github.com/volkamerlab/kinodata-3D-affinity-prediction).

Dataset

1. Data

This data set consists of three-dimensional protein-ligand complexes that were generated using computational docking from the OpenEye toolkit. The modeled proteins cover the kinase family for which a fair amount of structural data, i.e. co-crystallized protein-ligand complexes in the PDB, enriched through KLIFS annotations, is available. This enables us to use template docking (OpenEye’s POSIT functionality) in which the ligand placement is guided according to a similar co-crystallized ligand pose. The kinase-ligand pairs to dock are sourced from binding assay data via the public ChEMBL archive, version 33. In particular, we use kinase activity data as curated through the OpenKinome kinodata project. The final protein-ligand complexes are annotated with a predicted RMSD of the docked poses. The RMSD model is a simple neural network trained on a kinase-docking benchmark data set using ligand (fingerprint) similarity, docking score (ChemGauss 4), and Posit probability (see kinodata-3D repository).

The final data set contains in total 138 286 deduplicated kinase-ligand pairs, covering ~98 000 distinct compounds and ~271 distinct kinase structures.

2. File structure

The archive kinodata_3d.zip uses the following file structure



data/raw |  kinodata_docked_with_rmsd.sdf.gz |  pocket_sequences.csv |  mol2/pocket     | 1_pocket.mol2     | ...


The file kinodata_docked_with_rmsd.sdf.gz contains the docked ligand poses and the information on the protein-ligand pair inherited from kinodata. The protein pockets located in mol2/pocket are stored according to the MOL2 file format.

The pocket structures were sourced from KLIFS (klifs.net) and complete the poses in the aforementioned SDF file. The files are named {klifs_structure_id}_pocket.mol2. The structure ID is given in the SDF file along with the ligand poses.

The file pocket_sequences.csv contains all KLIFS pocket sequences relevant to the kinodata-3D dataset.

3. Related code

The code used to create the poses can be found in the kinodata-3D repository. The docking pipeline makes heavy use of the kinoml framework, which in turn uses OpenEye's Posit template docking implementation. The details of the original pipeline can also be found in the manuscript by Schaller et al. (2023). Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. bioRxiv.
DOI: 10.5281/zenodo.10410259
title: Kinodata-3D: an in silico kinase-ligand complex dataset for kinase-focused machine learning.

description: Project Description

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While the inclusion of 3D complex information is considered to be beneficial, structural ML for affinity prediction suffers from data scarcity. We provide kinodata-3D, a dataset of ~120 000 docked complexes to enable more robust training of 3D-based ML models for kinase activity prediction (see github.com/volkamerlab/kinodata-3D-affinity-prediction).

Dataset

1. Data

This data set consists of three-dimensional protein-ligand complexes that were generated using computational docking from the OpenEye toolkit. The modeled proteins cover the kinase family for which a fair amount of structural data, i.e. co-crystallized protein-ligand complexes in the PDB, enriched through KLIFS annotations, is available. This enables us to use template docking (OpenEye’s POSIT functionality) in which the ligand placement is guided according to a similar co-crystallized ligand pose. The kinase-ligand pairs to dock are sourced from binding assay data via the public ChEMBL archive, version 31. In particular, we use kinase activity data as curated through the OpenKinome kinodata project. The final protein-ligand complexes are annotated with a predicted RMSD of the docked poses. The RMSD model is a simple neural network trained on a kinase-docking benchmark data set using ligand (fingerprint) similarity, docking score (ChemGauss 4), and Posit probability (see kinodata-3D repository).

The final data set contains in total 118 577 deduplicated kinase-ligand pairs, covering ~80 000 distinct compounds and ~254 distinct kinase structures.

2. File structure

The archive kinodata_3d.zip uses the following file structure



data/raw |  kinodata_docked_with_rmsd.sdf.gz |  pocket_sequences.csv |  mol2/pocket     | 1_pocket.mol2     | ...


The file kinodata_docked_with_rmsd.sdf.gz contains the docked ligand poses and the information on the protein-ligand pair inherited from kinodata. The protein pockets located in mol2/pocket are stored according to the MOL2 file format.

The pocket structures were sourced from KLIFS (klifs.net) and complete the poses in the aforementioned SDF file. The files are named {klifs_structure_id}_pocket.mol2. The structure ID is given in the SDF file along with the ligand poses.

The file pocket_sequences.csv contains all KLIFS pocket sequences relevant to the kinodata-3D dataset.

3. Related code

The code used to create the poses can be found in the kinodata-3D repository. The docking pipeline makes heavy use of the kinoml framework, which in turn uses OpenEye's Posit template docking implementation. The details of the original pipeline can also be found in the manuscript by Schaller et al. (2023). Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery. bioRxiv.