cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21954/ou.rd.22120516.v1
title: CLEAN_SmallRingTensileTest_StainlessSteel316L_ExtensionRate1_35mmMin

description: This is a part of the test dataset (Digital Image Correlation Images and CSV data) that describes the experiments done at The Open University for the Small Ring Tensile Testing of SS316L performed at various displacement rates. Unprocessed images (.NEF format) start with the prefix 'RAW' while processed images (.TIF format) start with the prefix 'CLEAN'. The following letters after that describe the test type (Small Ring Test or Uniaxial Test), followed by the material. Lastly, after the material, the crosshead extension rate is described. For instance, 'Extension Rate0_3mmMin' refers to an extension rate of 0.3 mm/min and so on. The 'RAW' folders also contain the unprocessed CSV files. The 'CLEAN' folders contain the camera information (capture interval, ISO, etc) as well as the denoised CSV experimental files. The CSV files are denoised with the help of a Butterworth filter.
DOI: 10.21954/ou.rd.22120493
title: CLEAN_SmallRingTensileTest_StainlessSteel316L_ExtensionRate1_3mmMin

description: This is a part of the test dataset (Digital Image Correlation Images and CSV data) that describes the experiments done at The Open University for the Small Ring Tensile Testing of SS316L performed at various displacement rates. Unprocessed images (.NEF format) start with the prefix 'RAW' while processed images (.TIF format) start with the prefix 'CLEAN'. The following letters after that describe the test type (Small Ring Test or Uniaxial Test), followed by the material. Lastly, after the material, the crosshead extension rate is described. For instance, 'Extension Rate0_3mmMin' refers to an extension rate of 0.3 mm/min and so on. The 'RAW' folders also contain the unprocessed CSV files. The 'CLEAN' folders contain the camera information (capture interval, ISO, etc) as well as the denoised CSV experimental files. The CSV files are denoised with the help of a Butterworth filter.
DOI: 10.21954/ou.rd.22120493.v1
title: CLEAN_SmallRingTensileTest_StainlessSteel316L_ExtensionRate1_3mmMin

description: This is a part of the test dataset (Digital Image Correlation Images and CSV data) that describes the experiments done at The Open University for the Small Ring Tensile Testing of SS316L performed at various displacement rates. Unprocessed images (.NEF format) start with the prefix 'RAW' while processed images (.TIF format) start with the prefix 'CLEAN'. The following letters after that describe the test type (Small Ring Test or Uniaxial Test), followed by the material. Lastly, after the material, the crosshead extension rate is described. For instance, 'Extension Rate0_3mmMin' refers to an extension rate of 0.3 mm/min and so on. The 'RAW' folders also contain the unprocessed CSV files. The 'CLEAN' folders contain the camera information (capture interval, ISO, etc) as well as the denoised CSV experimental files. The CSV files are denoised with the help of a Butterworth filter.
DOI: 10.21954/ou.rd.22120424
title: CLEAN_SmallRingTensileTest_StainlessSteel316L_ExtensionRate1_25mmMin

description: This is a part of the test dataset (Digital Image Correlation Images and CSV data) that describes the experiments done at The Open University for the Small Ring Tensile Testing of SS316L performed at various displacement rates. Unprocessed images (.NEF format) start with the prefix 'RAW' while processed images (.TIF format) start with the prefix 'CLEAN'. The following letters after that describe the test type (Small Ring Test or Uniaxial Test), followed by the material. Lastly, after the material, the crosshead extension rate is described. For instance, 'Extension Rate0_3mmMin' refers to an extension rate of 0.3 mm/min and so on. The 'RAW' folders also contain the unprocessed CSV files. The 'CLEAN' folders contain the camera information (capture interval, ISO, etc) as well as the denoised CSV experimental files. The CSV files are denoised with the help of a Butterworth filter.
DOI: 10.21954/ou.rd.22120424.v1
title: CLEAN_SmallRingTensileTest_StainlessSteel316L_ExtensionRate1_25mmMin

description: This is a part of the test dataset (Digital Image Correlation Images and CSV data) that describes the experiments done at The Open University for the Small Ring Tensile Testing of SS316L performed at various displacement rates. Unprocessed images (.NEF format) start with the prefix 'RAW' while processed images (.TIF format) start with the prefix 'CLEAN'. The following letters after that describe the test type (Small Ring Test or Uniaxial Test), followed by the material. Lastly, after the material, the crosshead extension rate is described. For instance, 'Extension Rate0_3mmMin' refers to an extension rate of 0.3 mm/min and so on. The 'RAW' folders also contain the unprocessed CSV files. The 'CLEAN' folders contain the camera information (capture interval, ISO, etc) as well as the denoised CSV experimental files. The CSV files are denoised with the help of a Butterworth filter.
DOI: 10.6084/m9.figshare.21977045
title: Interspecies interaction reduces selection for antibiotic resistance in Escherichia coli

description: Source data for all the figures in the paper with the same title. The description for all column headers are mentioned in the Read_ME file.
DOI: 10.6084/m9.figshare.20937427
title: all ECFP4 of ChEMBL25 and ZINC20 as JSON dicts

description: 2 JSON dicts that list the connectivity features (key) ECFP4 (including the ECFP2) as detected by the GetMorganFingerprint function of the RDkit program. One files encompass all the 556,187 ECFP4 of the substances of ChEMBL25 as downloaded in September 2019 with 1,817,766 unique molecules. It is a large curated database of bioactive molecules. Here the values are 5 ChEMBL references that can be used to represent the fingerprint. <br> The second dict include the 1,156,416 ECFP(2 and 4) encountered in either the ZINC20 or ChEMBL25. ZINC is larger than ChEMBL and is based on commercially available compounds and not restricted to bioactive molecules. It encompass in proportion more inorganic and organometallic compounds than ChEMBL. We have used the already prepared version ZINC20-ML by Artem Cherkasov and Francesco Gentile with all the 1,006,651,037 ZINC20 molecules as of early March 2021. ZINC20-ML is available at https://files.docking.org/zinc20-ML/. <br> <br>
DOI: 10.6084/m9.figshare.20937427.v2
title: all ECFP4 of ChEMBL25 and ZINC20 as JSON dict

description: 2 JSON dict that list the connectivity features (key) ECFP2 and 4as detected by the GetMorganFingerprint function of the RDkit program. One files encompass all the ECFP4 of the substances of ChEMBL25 as downloaded in September 2019 with 1,817,766 unique molecules. It is a large curated database of bioactive molecules. Here the values are 5 ChEMBL references that can be used to represent the fingerprint. <br> The second dict include also ECFP(2 and4) encountered in either the ZINC20. ZINC is larger than ChEMBL and is based on commercially available compounds and not restricted to bioactive molecules. It encompass in proportion more inorganic and organometallic compounds than ChEMBL. We have used the already prepared version ZINC20-ML by Artem Cherkasov and Francesco Gentile with all the 1,006,651,037 ZINC20 molecules as of early March 2021. ZINC20-ML is available at https://files.docking.org/zinc20-ML/. <br> <br>
DOI: 10.5281/zenodo.7673500
title: Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage - DATASET

description: This is the dataset associated with the paper Dui, L. G., Lomurno, E., Lunardini, F., Termine, C., Campi, A., Matteucci, M., & Ferrante, S. (2022). Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage. <em>Scientific Reports</em>, <em>12</em>(1), 21624. File content: characterizationSciRep22.xlsx: an Excel file rows: subjects columns: year of birth month of birth dominant hand (0=right, 1=left) sex (0=male, 1=female) mother tongue (0=non-Italian, 1=Italian) featureSciRep22: a Matlab file. For each sub-matrix, rows are subjects "dataset" sub-matrix: each column is a feature computed from drawings "names_type" sub-matrix: metadata for the "dataset" matrix column 1: feature name column 2: game the feature refers to column 3: feature type "risk_questions": each column corresponds to the answer to one of the 15 items for assessing graphical abilities. 0=not selected, 1=selected "risk": the risk label computed from "risk_questions", 0=not at risk, 1= at risk
DOI: 10.5281/zenodo.7673499
title: Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage - DATASET

description: This is the dataset associated with the paper Dui, L. G., Lomurno, E., Lunardini, F., Termine, C., Campi, A., Matteucci, M., & Ferrante, S. (2022). Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage. <em>Scientific Reports</em>, <em>12</em>(1), 21624. File content: characterizationSciRep22.xlsx: an Excel file rows: subjects columns: year of birth month of birth dominant hand (0=right, 1=left) sex (0=male, 1=female) mother tongue (0=non-Italian, 1=Italian) featureSciRep22: a Matlab file. For each sub-matrix, rows are subjects "dataset" sub-matrix: each column is a feature computed from drawings "names_type" sub-matrix: metadata for the "dataset" matrix column 1: feature name column 2: game the feature refers to column 3: feature type "risk_questions": each column corresponds to the answer to one of the 15 items for assessing graphical abilities. 0=not selected, 1=selected "risk": the risk label computed from "risk_questions", 0=not at risk, 1= at risk
DOI: 10.6084/m9.figshare.20769853
title: GDBChEMBL sub set dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity score

description: The 3 786 315 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F and S that belong to GDBChEMBL. <br> Data only proposed for the reproducibility of the results presented in the article. Orignial dataset can be found here : https://gdb.unibe.ch/downloads/ <br> <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20769853.v2
title: GDBChEMBL sub set dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity score

description: The 3 786 315 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F and S that belong to GDBChEMBL. <br> Data only proposed for the reproducibility of the results presented in the article. Orignial dataset can be found here : https://gdb.unibe.ch/downloads/ <br> <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20769853.v1
title: GDBChEMBL sub set dictionnary of CNOFS molecules with SMILES and sillywalk score

description: The 3 786 315 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F and S that belong to GDBChEMBL. <br> Data only proposed for the reproducibility of the results presented in the article. Orignial dataset can be found here : https://gdb.unibe.ch/downloads/ <br> <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.5281/zenodo.5972028
title: Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study - DATASET

description: Dataset to support the findings in the journal paper titled "Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study". Each row is a different subject. Each column represents an answer to the questionnaire. For single choice questions, the answer was reported as-is (Italian). For multiple choice questions, the alternatives where splitted in several columns and the answer was coded as 0/1 (one hot encoding). For the "school" column, 2=primary school, 3=middle school, 4=high school. For the "school.class" column, classes from 4 to 8 belong to primary school, from first to fifth grade; classes from 9 to 11 belong to middle school, from first to third grade; classes from 12 to 16 belong to high school, from first to fifth grade.
DOI: 10.5281/zenodo.5972029
title: Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study - DATASET

description: Dataset to support the findings in the journal paper titled "Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study". Each row is a different subject. Each column represents an answer to the questionnaire. For single choice questions, the answer was reported as-is (Italian). For multiple choice questions, the alternatives where splitted in several columns and the answer was coded as 0/1 (one hot encoding). For the "school" column, 2=primary school, 3=middle school, 4=high school. For the "school.class" column, classes from 4 to 8 belong to primary school, from first to fifth grade; classes from 9 to 11 belong to middle school, from first to third grade; classes from 12 to 16 belong to high school, from first to fifth grade.
DOI: 10.6084/m9.figshare.20059400
title: ZINC sub set dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity scores

description: All ZINC 20 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'nb_atoms' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL}
DOI: 10.6084/m9.figshare.20059400.v1
title: ZINC sub set dictionnary of CNOFS molecules with SMILES and sillywalk scores

description: All ZINC 20 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'nb_atoms' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL}
DOI: 10.6084/m9.figshare.20059400.v2
title: ZINC sub set dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity scores

description: All ZINC 20 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'nb_atoms' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL}
DOI: 10.21942/uva.22153493
title: Data and code for the article 'Bouba/Kiki in Homer? Looking for iconicity in the language of the epics'

description: The file names should be reasonably self-explanatory for those having read the article. For more information consult the readme file.
DOI: 10.21942/uva.22153493.v2
title: Data and code for the article 'Bouba/Kiki in Homer? Looking for iconicity in the language of the epics'

description: The file names should be reasonably self-explanatory for those having read the article. For more information consult the readme file.
DOI: 10.21942/uva.22153493.v1
title: Data and code for the article 'Bouba/Kiki in Homer? Looking for iconicity in the language of the epics'

description: The file names should be reasonably self-explanatory for those having read the article. For more information consult the readme file.
DOI: 10.6084/m9.figshare.20054414
title: GDB11 sub set dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity score

description: Selection of 26 413 375 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20054414.v2
title: GDB11 sub set dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity score

description: Selection of 26 413 375 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20054414.v1
title: GDB11 sub set dictionnary of CNOFS molecules with SMILES and sillywalk score

description: Selection of 26 413 375 neutral singlet molecules without any atomic charges (formal or real) composed of only H, C, N, O, F. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20054381
title: ChEMBL25 subset dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity score

description: Subset of 1 191 453 neutral singlet molecules without any atomic charges (formal or real) with only H, C, N, O F and S. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20054381.v3
title: ChEMBL25 subset dictionnary of CNOFS molecules with SMILES and ECFP4 connectivity score

description: Subset of 1 191 453 neutral singlet molecules without any atomic charges (formal or real) with only H, C, N, O F and S. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20054381.v2
title: ChEMBL25 subset dictionnary of CNOFS molecules with SMILES and sillywalk score

description: Subset of 1 191 453 neutral singlet molecules without any atomic charges (formal or real) with only H, C, N, O F and S. <br> JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} <br>
DOI: 10.6084/m9.figshare.20054339
title: OD9_0 (union of PC9 and QM9) dataset dictionnary with SMILES and ECFP4 connectivity score

description: JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} Only neutral singlet molecule without any atomic charges (formal or real) composed of H, C, N, O, and F.
DOI: 10.6084/m9.figshare.20054339.v2
title: OD9_0 (union of PC9 and QM9) dataset dictionnary with SMILES and ECFP4 connectivity score

description: JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} Only neutral singlet molecule without any atomic charges (formal or real) composed of H, C, N, O, and F.
DOI: 10.6084/m9.figshare.20054339.v1
title: OD9_0 (union of PC9 and QM9) dataset dictionnary with SMILES and sillywalk score

description: JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} Only neutral singlet molecule without any atomic charges (formal or real) composed of H, C, N, O, and F.
DOI: 10.6084/m9.figshare.20054327
title: QM9 dataset dictionnary with SMILES and ECFP4 connectivity score

description: JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} Only neutral singlet molecule without any atomic charges (formal or real) composed of H, C, N, O, and F.
DOI: 10.6084/m9.figshare.20054327.v1
title: QM9 dataset dictionnary with SMILES and sillywalk score

description: JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} Only neutral singlet molecule without any atomic charges (formal or real) composed of H, C, N, O, and F.
DOI: 10.6084/m9.figshare.20054315
title: PC9 dataset dictionnary with SMILES and ECFP4 connectivity score

description: JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} Only neutral singlet molecule without any atomic charges (formal or real) composed of H, C, N, O, and F.
DOI: 10.6084/m9.figshare.20054315.v1
title: PC9 dataset dictionnary with SMILES and sillywalk score

description: JSON file of python dictionnary. Key: SMILES, value: dict {'HAC' # number of heavy atoms, 'swscore_ChEMBL' # % of ECFP4 of the molecule that belong to ChEMBL, 'swscore_ZINC' # % of ECFP4 of the molecule that belong to ZINC or ChEMBL} Only neutral singlet molecule without any atomic charges (formal or real) composed of H, C, N, O, and F.
DOI: 10.6084/m9.figshare.22144169
title: Incomplete mitogenome assemblies of Pleistocene prey (European wooly rhinoceroses) and predator (cave hyena) from the predators' coprolites

description: The woolly rhinoceros (Coelodonta antiquitatis) is an iconic species of the Pleistocene megafauna, which was abundant in Europe in the Pleistocene until its demise beginning approximately 10,000 years ago. Despite the recovery of several specimens from European archaeological sites, no genomes of the European population were available so far, and all available genomic data originated exclusively from the Siberian population5. Using coprolites of cave hyenas (Crocuta crocuta spelea) recovered from two world heritage caves in Germany (Hohlenstein-Stadel [HST] and Bockstein-Loch [BSVK]), we isolated and enriched predator and prey DNA to assemble the first European woolly rhinoceros mitogenomes, in addition to cave hyena mitogenomes. So far, no mitogenomic data of a European woolly rhinoceros was available, thus the data produced here constitute a considerable addition to European megafauna paleogenomics and may help further resolve the phylogeography of woolly rhinoceroses in Europe.
DOI: 10.5281/zenodo.6368069
title: The global energy balance of the ASDEX Upgrade tokamak determined with the revised cooling water calorimetry

description: Provided data includes data sets required for running the AUG calorimetry. The database consists of the flow rate, time correction and the distance between the temperature measuring points of inlet and outlet cooling water of each cooling unit.