cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7589312
title: Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data

description: The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC&gt;0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.
description: Datasets: Cell Painting Descriptors (before feature selection) for all compounds available from the BROAD Cell Painting assay: Cell_Painting_Median_Features_Median_doses.csv md5:635daafefd295b1a48aeb19fc7674d55 1.1 GB Selected 89 assays, Cell Painting Descriptors and Morgan Fingerprint bits (after feature selection) for 15272 compounds in the BROAD dataset: BROAD_assay_data_filtered_89_inchi.csv md5:527e7ee2b58ad7a0b0194541920c0f92 5.5 MB BROAD_CP_filtered_15272.csv md5:4325cfe9cf66e38b2bba960eac1e3336 59.3 MB BROAD_Mfp_filtered_15272.csv md5:65905e26c301e3d7e598e89dc2f0759f 11.3 MB Selected 88 assays, Cell Painting Descriptors and Morgan Fingerprint bits (after feature selection) for 9876 compounds in the Public dataset: Public_assay_data_filtered_88_inchi.csv md5:a52895a6a8480d25343d7e7b6fdbcc24 2.8 MB Public_CP_filtered_9876.csv md5:2606037db1d9e22044454bd9f1eb41a0 36.3 MB Public_Mfp_filtered_9876.csv md5:846931cd6c9eac29caff424946a63d5e 4.4 MB
description: {"references": ["Bray, M. A.; Gustafsdottir, S. M.; Rohban, M. H.; Singh, S.; Ljosa, V.; Sokolnicki, K. L.; Bittker, J. A.; Bodycombe, N. E.; Dan\u010d\u00edk, V.; Hasaka, T. P.; Hon, C. S.; Kemp, M. M.; Li, K.; Walpita, D.; Wawer, M. J.; Golub, T. R.; Schreiber, S. L.; Clemons, P. A.; Shamji, A. F.; Carpenter, A. E. A Dataset of Images and Morphological Profiles of 30 000 Small-Molecule Treatments Using the Cell Painting Assay. Giga-Science. 2017, pp 1\u20135.", "Moshkov, N.; Moshkov, T.; Yang, K.; Horvath, P.; Dancik, V.; Wagner, B. K.; Clemons, P. A.; Singh, S.; Carpenter, A. E.; Caicedo, J. C. Predicting Compound Activity from Phenotypic Profiles and Chem-ical Structures. bioRxiv 2022, 2020.12.15.422887"]}
DOI: 10.5281/zenodo.6613740
title: Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data

description: The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC&gt;0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.
description: Datasets: Cell Painting Descriptors (before feature selection) for all compounds available from the BROAD Cell Painting assay: Cell_Painting_Median_Features_Median_doses.csv md5:635daafefd295b1a48aeb19fc7674d55 1.1 GB Selected 89 assays, Cell Painting Descriptors and Morgan Fingerprint bits (after feature selection) for 15272 compounds in the BROAD dataset: BROAD_assay_data_filtered_89_inchi.csv md5:527e7ee2b58ad7a0b0194541920c0f92 5.5 MB BROAD_CP_filtered_15272.csv md5:4325cfe9cf66e38b2bba960eac1e3336 59.3 MB BROAD_Mfp_filtered_15272.csv md5:65905e26c301e3d7e598e89dc2f0759f 11.3 MB Selected 88 assays, Cell Painting Descriptors and Morgan Fingerprint bits (after feature selection) for 9876 compounds in the Public dataset: Public_assay_data_filtered_88_inchi.csv md5:a52895a6a8480d25343d7e7b6fdbcc24 2.8 MB Public_CP_filtered_9876.csv md5:2606037db1d9e22044454bd9f1eb41a0 36.3 MB Public_Mfp_filtered_9876.csv md5:846931cd6c9eac29caff424946a63d5e 4.4 MB
description: {"references": ["Bray, M. A.; Gustafsdottir, S. M.; Rohban, M. H.; Singh, S.; Ljosa, V.; Sokolnicki, K. L.; Bittker, J. A.; Bodycombe, N. E.; Dan\u010d\u00edk, V.; Hasaka, T. P.; Hon, C. S.; Kemp, M. M.; Li, K.; Walpita, D.; Wawer, M. J.; Golub, T. R.; Schreiber, S. L.; Clemons, P. A.; Shamji, A. F.; Carpenter, A. E. A Dataset of Images and Morphological Profiles of 30 000 Small-Molecule Treatments Using the Cell Painting Assay. Giga-Science. 2017, pp 1\u20135.", "Moshkov, N.; Moshkov, T.; Yang, K.; Horvath, P.; Dancik, V.; Wagner, B. K.; Clemons, P. A.; Singh, S.; Carpenter, A. E.; Caicedo, J. C. Predicting Compound Activity from Phenotypic Profiles and Chem-ical Structures. bioRxiv 2022, 2020.12.15.422887"]}
DOI: 10.5281/zenodo.7589383
title: ASPFuzz Data

description: Fuzzing data generate by ASPFuzz for Ryzen Zen1, Zen+, Zen2, Zen3 and ZenTesla. Check out the ASPFuzz Github repository for more details.
DOI: 10.5281/zenodo.7589382
title: ASPFuzz Data

description: Fuzzing data generate by ASPFuzz for Ryzen Zen1, Zen+, Zen2, Zen3 and ZenTesla. Check out the ASPFuzz Github repository for more details.
DOI: 10.15468/us72da
title: A checklist of Trees in Atewa Range Forest Reserve, Ghana

description: The Atewa Forest located at the Eastern Region of Ghana near Kibi town covers an area of 263.12KM² which is comprised of two separate reserves; The Atewa Range Reserve (236.63 KM²) and Atewa Range Extension Reserve (26.49KM²). The Atewa Range is internationally recognized as one of the highest priority rainforest ecosystems in West Africa for its high species diversity, high level of endemism and great hydrological importance (Larsen, 2006). The forest lies within the moist semi-deciduous forest zone of Ghana (Hall & Swaine 1976) but c. 75% of the area (Hawthorne & Abu-Juam 1995) is an Upland Evergeen forest type prevalent in only one other site in Ghana (Hall & Swaine 1981). Characteristic species of this forest type are Alchornea floribunda, Asplenium dregeanum, Chidlowia sanguinea, Lophira alata and Peperomia fernandopoiana, Funtumia africana and Ficus vogeliana which have either medicinal or nutritional uses. The site has also been noted for its seasonal marshy grasslands, known as bovals. The dataset is drawn from a report by Lindsell et al, 2016 on a forest plot survey in Atewa Range Forest Reserve that aimed to assess the condition of woody vegetation in the Atewa forest and facilitate a feasibility assessment of carbon offset financing for the forest. This dataset comprises of 249 records.
DOI: 10.5281/zenodo.7579705
title: Nudging the tropical Indian Ocean Applied to the IPSL-CM6A-LR model - Cooling Experiments

description: This experiment applies monthly nudging of -2, -1, +1, and +2 degree C to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where we apply a constant nudging and refer to the experiments as TIOxC, where "x" is the monthly nudging value (as in Ferster et al., 2021). Additional experiments were simulated for a paper within the submission process (to be included and updated within future versions) including the "TIOc" and "C0C" experiments, where the TIOC nudges towards towards the time-mean climatology and the C0C nudges at each monthly time step towards the pre-industrial control's corresponding monthly value (similar to adding white noise at each time step). The datasets are included from the experiments to help share our results and reproducibility of the figures within our manuscripts, the original model output is stored on the TGCC computing machine Irene. All datasets are saved on this data repository are saved as annual means! These datasets represent the -2 and -1 degree C experiments. Please use the additional thredds link to access the full IPSL output for the experiments: https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/M1C-TIO/1950-01-01/M1C-TIO1950-01/catalog.html https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/M2C-TIO/1950-01-01/M2C-TIO1950-01/catalog.html The pre-industrial control from IPSL can be found on the CMIP6-ESGF servers and the TGCC computing machine Irene. Please contact us for additional explanations, datasets, or collaboration. *The datasets are created from the IPSL-CM6A-LR output files **This version serves as a preliminary version of the dataset for publication purposes, additional output is available and the experiment output exceeds the 50gb of storage offered through this platform (TGCC Irene). ***Contact: brady.ferster@locean.ipsl.fr or brady.ferster@yale.edu
DOI: 10.5281/zenodo.7589221
title: A Novel Architecture for room temperature microwave optomechanical experiments

description: The dataset contains cavity optomechanical measurements of the Si<sub>3</sub>N<sub>4</sub> membrane at room temperature. These datasets correspond to different techniques to extract single photon coupling rate g<sub>0</sub>. The files with the name starting with fig_2 are about the characterization of the microwave cavity (S<sub>21</sub>) and Si<sub>3</sub>N<sub>4</sub> membrane (noise spectrum). The file names with initials as fig_3 is contains data of noise spectrum when Si<sub>3</sub>N<sub>4</sub> membrane is driven by white noise using piezoelectric transducer. It is the file names with initials as fig_4 that are about the optically induced transparency/absorption, while the file names with initials as fig_5 are about the driven nonlinear Si<sub>3</sub>N<sub>4</sub> membrane.
description: This research is supported by the Royal Society (UF150140, RGF\EA\180099, RGF\R1\180059, RGF\EA\201047, RPG\2016\186, URF\R\211009).
DOI: 10.5281/zenodo.7579986
title: Nudging the tropical Indian Ocean Applied to the IPSL-CM6A-LR model - Control Experiments

description: This experiment applies monthly nudging of -2, -1, +1, and +2 degree C to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where we apply a constant nudging and refer to the experiments as TIOxC, where "x" is the monthly nudging value (as in Ferster et al., 2021). Additional experiments were simulated for a paper within the submission process (to be included and updated within future versions) including the "TIOc" and "C0C" experiments, where the TIOC nudges towards towards the time-mean climatology and the C0C nudges at each monthly time step towards the pre-industrial control's corresponding monthly value (similar to adding white noise at each time step). The datasets are included from the experiments to help share our results and reproducibility of the figures within our manuscripts, the original model output is stored on the TGCC computing machine Irene. All datasets are saved on this data repository are saved as annual means! These datasets represent the C0C (TIO=0C) and TIOc control experiments. Please use the additional thredds link to access the full IPSL output for the experiments: https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/Control-0C-TIO/1950-01-01/Control-0C-TIO1950-01/catalog.html https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/TIOc/1950-01-01/TIOc1950-01/catalog.html The pre-industrial control from IPSL can be found on the CMIP6-ESGF servers and the TGCC computing machine Irene. Please contact us for additional explanations, datasets, or collaboration. *The datasets are created from the IPSL-CM6A-LR output files **This version serves as a preliminary version of the dataset for publication purposes, additional output is available and the experiment output exceeds the 50gb of storage offered through this platform (TGCC Irene). ***Contact: brady.ferster@locean.ipsl.fr or brady.ferster@yale.edu
DOI: 10.5519/qd.d0p3x4ur
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (52 records)
DOI: 10.5281/zenodo.7589268
title: Development and Psychometric Validation of the Nursing Self-Efficacy Scale for Managing Cancer Treatment-Induced Cardiotoxicity: An Exploratory Mixed-Method Study.

description: Magon A, Conte G, Arrigoni C, Dellafiore F, de Maria M, Pittella F, Rocco G, Stievano A, Ghizzardi G, Caruso R. Development and Psychometric Validation of the Nursing Self-Efficacy Scale for Managing Cancer Treatment-Induced Cardiotoxicity: An Exploratory Mixed-Method Study. Semin Oncol Nurs. 2022 Nov 18:151367. doi: 10.1016/j.soncn.2022.151367. Epub ahead of print. PMID: 36411124. Abstract <strong>Objective: </strong>Assessing nursing self-efficacy could be strategic to sustain nursing competence. This study aimed to develop and validate the nursing self-efficacy scale for managing cancer treatment-induced cardiotoxicity (NSS-CTC). <strong>Data sources: </strong>An exploratory mixed-method study was performed by including two main phases. The first comprised the developmental tasks to generate the initial pool of items, including a literature review and a consensus meeting based on a nominal group technique. The second phase initially involved an external panel of experts in assessing the content validity of the novel scale, followed by a cross-sectional data collection to perform exploratory factor analysis by employing a multicenter and convenience sampling approach. The most plausible psychometric structure derived from the exploratory factor analysis was tested with a confirmatory factor analysis using a second data collection round on another sample enrolled with a multicenter and convenience sampling approach. Internal consistency was assessed using Cronbach's alfa. <strong>Conclusion: </strong>The NSS-CTS is a novel 15-item self-report measure for assessing nurse self-efficacy in dealing with cancer treatment-related cardiotoxicity. Its two plausible domains were labeled knowledge-related self-efficacy (Cronbach's α = 0.924) and practice-related self-efficacy (Cronbach's α = 0.937); the factor analyses in both samples showed adequate fit to sample statistics. Future studies are necessary to corroborate its construct validity and assess its measurement invariance across various country contexts. <strong>Implications for nursing practice: </strong>Assessing nursing self-efficacy for managing cancer treatment-induced cardiotoxicity is a promising approach for identifying educational gaps and promoting nursing competency in this particular area of cancer care.
DOI: 10.5281/zenodo.7589267
title: Development and Psychometric Validation of the Nursing Self-Efficacy Scale for Managing Cancer Treatment-Induced Cardiotoxicity: An Exploratory Mixed-Method Study.

description: Magon A, Conte G, Arrigoni C, Dellafiore F, de Maria M, Pittella F, Rocco G, Stievano A, Ghizzardi G, Caruso R. Development and Psychometric Validation of the Nursing Self-Efficacy Scale for Managing Cancer Treatment-Induced Cardiotoxicity: An Exploratory Mixed-Method Study. Semin Oncol Nurs. 2022 Nov 18:151367. doi: 10.1016/j.soncn.2022.151367. Epub ahead of print. PMID: 36411124. Abstract <strong>Objective: </strong>Assessing nursing self-efficacy could be strategic to sustain nursing competence. This study aimed to develop and validate the nursing self-efficacy scale for managing cancer treatment-induced cardiotoxicity (NSS-CTC). <strong>Data sources: </strong>An exploratory mixed-method study was performed by including two main phases. The first comprised the developmental tasks to generate the initial pool of items, including a literature review and a consensus meeting based on a nominal group technique. The second phase initially involved an external panel of experts in assessing the content validity of the novel scale, followed by a cross-sectional data collection to perform exploratory factor analysis by employing a multicenter and convenience sampling approach. The most plausible psychometric structure derived from the exploratory factor analysis was tested with a confirmatory factor analysis using a second data collection round on another sample enrolled with a multicenter and convenience sampling approach. Internal consistency was assessed using Cronbach's alfa. <strong>Conclusion: </strong>The NSS-CTS is a novel 15-item self-report measure for assessing nurse self-efficacy in dealing with cancer treatment-related cardiotoxicity. Its two plausible domains were labeled knowledge-related self-efficacy (Cronbach's α = 0.924) and practice-related self-efficacy (Cronbach's α = 0.937); the factor analyses in both samples showed adequate fit to sample statistics. Future studies are necessary to corroborate its construct validity and assess its measurement invariance across various country contexts. <strong>Implications for nursing practice: </strong>Assessing nursing self-efficacy for managing cancer treatment-induced cardiotoxicity is a promising approach for identifying educational gaps and promoting nursing competency in this particular area of cancer care.
DOI: 10.5281/zenodo.7589464
title: Open-source design files for harvesting energy from overhead power line cables

description: OpenSource_CalculationFile_MEH.xlsx : this is an excel file that provides a calculation tool to select a magnetic core for harvesting energy from powerlines. OpenSource_Schematic_MEH.pdf : this is a schematic file that provides a detailed design of the charging circuit.
DOI: 10.5281/zenodo.7589463
title: Open-source design files for harvesting energy from overhead power line cables

description: OpenSource_CalculationFile_MEH.xlsx : this is an excel file that provides a calculation tool to select a magnetic core for harvesting energy from powerlines. OpenSource_Schematic_MEH.pdf : this is a schematic file that provides a detailed design of the charging circuit.
DOI: 10.34740/kaggle/dsv/4925534
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.21982181
title: Data and analyses

description: Familiarity and idea evaluation. Data and analyses.
DOI: 10.6084/m9.figshare.21982181.v1
title: Data and analyses

description: Familiarity and idea evaluation. Data and analyses.
DOI: 10.5281/zenodo.7487345
title: Survey on household heating, Thessaloniki 2022 - GREEN FORESEEN

description: The file contains the data from a survey carried out as part of GREEN FORESEEN H2020 project. The data pertain to heating energy behaviour of households in the prefecture of Thessaloniki in 2022, with an empasis of solid biofuels.
DOI: 10.5281/zenodo.7487344
title: Survey on household heating, Thessaloniki 2022 - GREEN FORESEEN

description: The file contains the data from a survey carried out as part of GREEN FORESEEN H2020 project. The data pertain to heating energy behaviour of households in the prefecture of Thessaloniki in 2022, with an empasis of solid biofuels.
DOI: 10.5281/zenodo.7589484
title: "Determinants of football players' valuation: a systematic review" datasets

description: 3 interdependent tables are available and are the materials used for a systematic review of the determinants of football players' valuation. - model_specifications is a table where each row represents one of the 111 model specifications included in the systematic review. Characteristics of the article from which specification was retrieved (title, year, authors, journal), attributes of the specification (sample size, population, econometric modeling, etc.), the significance levels of included variables, and the associated coefficients for significant variables. - model_specifications_dictionnary precise the names of the columns of the table model_specifications. - variables_definitions_and_classification is a table that details all the 471 variables used in the 29 articles analysed with definitions quoted from articles when possible and presents a classification of these variables into 6 categories and several subcategories.
DOI: 10.6085/aa/pts001_021mxti006r00_20050227.50.2
title: PISCO: Physical Oceanography: moored temperature data: Point Sur, California, USA (PTS001)

description: This metadata record describes moored seawater temperature data collected at Point Sur, California, USA, by PISCO. Measurements were collected using StowAway XTI Temperature Loggers (Onset Computer Corp. XTI32-05+37) beginning 2005-02-27. The instrument depth was 006 meters, in an overall water depth of 021 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/pts001_021mxti006r00_20050227.50.3
title: PISCO: Physical Oceanography: moored temperature data: Point Sur, California, USA (PTS001)

description: This metadata record describes moored seawater temperature data collected at Point Sur, California, USA, by PISCO. Measurements were collected using StowAway XTI Temperature Loggers (Onset Computer Corp. XTI32-05+37) beginning 2005-02-27. The instrument depth was 006 meters, in an overall water depth of 021 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/pts001_021mxti006r00_20050227.50.4
title: PISCO: Physical Oceanography: moored temperature data: Point Sur, California, USA (PTS001)

description: This metadata record describes moored seawater temperature data collected at Point Sur, California, USA, by PISCO. Measurements were collected using StowAway XTI Temperature Loggers (Onset Computer Corp. XTI32-05+37) beginning 2005-02-27. The instrument depth was 006 meters, in an overall water depth of 021 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/pts001_021mxti006r00_20050227.50.5
title: PISCO: Physical Oceanography: moored temperature data: Point Sur, California, USA (PTS001)

description: This metadata record describes moored seawater temperature data collected at Point Sur, California, USA, by PISCO. Measurements were collected using StowAway XTI Temperature Loggers (Onset Computer Corp. XTI32-05+37) beginning 2005-02-27. The instrument depth was 006 meters, in an overall water depth of 021 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/pts001_021mxti006r00_20050421.40.1
title: (:unkn)
DOI: 10.6085/aa/pts001_021mxti006r00_20050421.40.2
title: (:unkn)