cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c0d0t
title: CCDC 2175115: Experimental Crystal Structure Determination

description: Related Article: xiang xiangsh|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.8121523
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Antoine Charles François Wallays, http://www.wikidata.org/entity/Q5553723. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5553723
DOI: 10.5281/zenodo.8121522
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Antoine Charles François Wallays, http://www.wikidata.org/entity/Q5553723. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5553723
DOI: 10.5281/zenodo.8048017
title: Discrete Flow Cytometry of Underway Samples from Gradients 5 (2023) Using a BD Influx Cell Sorter

description: The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (&lt;5 µm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton
DOI: 10.5281/zenodo.8121529
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Willem Vrolik, http://www.wikidata.org/entity/Q363406. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q363406
DOI: 10.5281/zenodo.8121528
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Willem Vrolik, http://www.wikidata.org/entity/Q363406. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q363406
DOI: 10.5281/zenodo.8121543
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Otto Heinrich Volk, http://www.wikidata.org/entity/Q19355834. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q19355834
DOI: 10.5281/zenodo.8121542
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Otto Heinrich Volk, http://www.wikidata.org/entity/Q19355834. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q19355834
DOI: 10.5281/zenodo.8121555
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Artis Vinas, http://www.wikidata.org/entity/Q109766740. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q109766740
DOI: 10.5281/zenodo.8121557
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Lammert Visch, http://www.wikidata.org/entity/Q116985644. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q116985644
DOI: 10.5281/zenodo.8121556
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Lammert Visch, http://www.wikidata.org/entity/Q116985644. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q116985644
DOI: 10.6084/m9.figshare.22588963.v4
title: Prediction of bacterial pH preferences using machine learning

description: This code includes hyperparameter optimization and evaluation of a boosted regression model to predict bacterial pH preferences using information on the presence/absence of functional genes in bacterial genomes. For a more detailed description of the approach see https://github.com/fiererlab/ph_preference/blob/main/Whole_FeatureSelection_Final.ipynb. <br> Important file descriptions: <br> load_and_run_pH_model.ipynb is the notebook required to run the model. pH_model_2022-10-17_56_full.sav is the model file (trained on the data from this study). Readme.txt specifies how to set up an environment with the dependencies found in environment.yaml, which are required to run the notebook and model.
DOI: 10.5281/zenodo.8121575
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Immanuel Vigeland, http://www.wikidata.org/entity/Q105620471. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q105620471
DOI: 10.5281/zenodo.8121574
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Immanuel Vigeland, http://www.wikidata.org/entity/Q105620471. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q105620471
DOI: 10.5281/zenodo.8121577
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Chambre Corker Vigurs, http://www.wikidata.org/entity/Q93704930. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q93704930
DOI: 10.5281/zenodo.8121576
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Chambre Corker Vigurs, http://www.wikidata.org/entity/Q93704930. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q93704930
DOI: 10.5522/04/23533731
title: Microsurgical Aneurysm Clipping Surgery (MACS) Dataset with image-level aneurysm presence/absence annotations

description: We collected and present a microsurgical aneurysm clipping surgery (MACS) dataset comprising of 16 videos of MACS procedures with aneurysm presence/ absence annotations at frame-level (~350k frames) conducted by expert neurosurgeons. We extract frames at 5 fps and establish a dataset of 356,165 images. The MACS dataset is composed of FHD (1920x1080) surgical videos from the operative microscope of 16 patients during surgical repair of intracerebral aneurysms. The study was registered with the hospital’s (National Hospital for Neurology and Neurosurgery, UCLH NHS) local audit committee and data sharing was approved by the information governance lead. All patients provided written informed consent for their video to be collected for research. The MACS dataset was blindly reviewed by two senior vascular neurosurgeons in duplicate. Frames were classified as follows: Type-X : No aneurysm in microscope’s view, Type-Y : Aneurysm in microscope’s view (including both visible and clipped aneurysms), Type-Z: Frame excluded from analysis. <br>
DOI: 10.5522/04/23533731.v1
title: Microsurgical Aneurysm Clipping Surgery (MACS) Dataset with image-level aneurysm presence/absence annotations

description: We collected and present a microsurgical aneurysm clipping surgery (MACS) dataset comprising of 16 videos of MACS procedures with aneurysm presence/ absence annotations at frame-level (~350k frames) conducted by expert neurosurgeons. We extract frames at 5 fps and establish a dataset of 356,165 images. The MACS dataset is composed of FHD (1920x1080) surgical videos from the operative microscope of 16 patients during surgical repair of intracerebral aneurysms. The study was registered with the hospital’s (National Hospital for Neurology and Neurosurgery, UCLH NHS) local audit committee and data sharing was approved by the information governance lead. All patients provided written informed consent for their video to be collected for research. The MACS dataset was blindly reviewed by two senior vascular neurosurgeons in duplicate. Frames were classified as follows: Type-X : No aneurysm in microscope’s view, Type-Y : Aneurysm in microscope’s view (including both visible and clipped aneurysms), Type-Z: Frame excluded from analysis. <br>
DOI: 10.5281/zenodo.8121583
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Sergio Antonio Vanin, http://www.wikidata.org/entity/Q108401040. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q108401040
DOI: 10.5281/zenodo.8121571
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Joyce Winifred Vickery, http://www.wikidata.org/entity/Q6297664. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q6297664
DOI: 10.5281/zenodo.8121579
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Anders Vik, http://www.wikidata.org/entity/Q110551583. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q110551583
DOI: 10.5281/zenodo.8121578
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Anders Vik, http://www.wikidata.org/entity/Q110551583. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q110551583
DOI: 10.5281/zenodo.8121585
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Lino Vaccari, http://www.wikidata.org/entity/Q3241652. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q3241652
DOI: 10.5281/zenodo.8121584
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Lino Vaccari, http://www.wikidata.org/entity/Q3241652. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q3241652
DOI: 10.5281/zenodo.8121587
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Máirin de Valéra, http://www.wikidata.org/entity/Q19975776. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q19975776
DOI: 10.5281/zenodo.8121586
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Máirin de Valéra, http://www.wikidata.org/entity/Q19975776. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q19975776
DOI: 10.5281/zenodo.8121591
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Edwin Cooper Van Dyke, http://www.wikidata.org/entity/Q23813352. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q23813352
DOI: 10.5281/zenodo.8121593
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Marie Unarová, http://www.wikidata.org/entity/Q95214729. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q95214729
DOI: 10.5281/zenodo.8121592
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Marie Unarová, http://www.wikidata.org/entity/Q95214729. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q95214729
DOI: 10.5281/zenodo.8121597
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Alan W. Upham, http://www.wikidata.org/entity/Q111603306. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q111603306
DOI: 10.5281/zenodo.8121596
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Alan W. Upham, http://www.wikidata.org/entity/Q111603306. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q111603306
DOI: 10.5281/zenodo.8121604
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Knut Schjeldrup Tylden, http://www.wikidata.org/entity/Q106551978. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q106551978
DOI: 10.5281/zenodo.8121603
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Knut Schjeldrup Tylden, http://www.wikidata.org/entity/Q106551978. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q106551978
DOI: 10.5281/zenodo.8121612
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by William H. Twenhofel, http://www.wikidata.org/entity/Q12869758. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q12869758
DOI: 10.5281/zenodo.8121611
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by William H. Twenhofel, http://www.wikidata.org/entity/Q12869758. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q12869758
DOI: 10.5281/zenodo.8121560
title: ΔG-RDKit: Solvation Free Energy Database

description: We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy". This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy Δ<em>G</em><sub>solv</sub> values. Data entries were collected from two separate databases. The FreeSolv library, with 642 experimental aqueous Δ<em>G</em><sub>solv </sub>determinations and the Solv@TUM database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties. Experimental Δ<em>G</em><sub>solv</sub> values range from -14 to 4 kcal mol<sup>-1</sup> and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using RDKit software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<em>G</em><sub>solv</sub>. Through Machine Learning regression algorithms, our models were able to make Δ<em>G</em><sub>solv</sub> predictions with high accuracy, based on the information encoded in each chemical feature.
description: {"references": ["Mobley, D. L.; Guthrie, J. P., FreeSolv: a database of experimental and calculated hydration free energies, with input files. Journal of Computer-Aided Molecular Design 2014, 28 (7), 711-720.", "Hille, C.; Ringe, S.; Deimel, M.; Kunkel, C.; Acree, W. E.; Reuter, K.; Oberhofer, H., Solv@TUM v 1.0. 2018. https://mediatum.ub.tum.de/1452571?v=1", "Landrum, G. RDKit: Open-source cheminformatics 2022_09_4 (Q3 2022) Release - January 16, 2023. http://www.rdkit.org/ (accessed January 18, 2023).", "Python Software Foundation - Python Language Reference, version 3.9.8. http://www.python.org."]}
DOI: 10.5281/zenodo.8121561
title: ΔG-RDKit: Solvation Free Energy Database

description: We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy". This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy Δ<em>G</em><sub>solv</sub> values. Data entries were collected from two separate databases. The FreeSolv library, with 642 experimental aqueous Δ<em>G</em><sub>solv </sub>determinations and the Solv@TUM database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties. Experimental Δ<em>G</em><sub>solv</sub> values range from -14 to 4 kcal mol<sup>-1</sup> and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using RDKit software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<em>G</em><sub>solv</sub>. Through Machine Learning regression algorithms, our models were able to make Δ<em>G</em><sub>solv</sub> predictions with high accuracy, based on the information encoded in each chemical feature.
description: {"references": ["Mobley, D. L.; Guthrie, J. P., FreeSolv: a database of experimental and calculated hydration free energies, with input files. Journal of Computer-Aided Molecular Design 2014, 28 (7), 711-720.", "Hille, C.; Ringe, S.; Deimel, M.; Kunkel, C.; Acree, W. E.; Reuter, K.; Oberhofer, H., Solv@TUM v 1.0. 2018. https://mediatum.ub.tum.de/1452571?v=1", "Landrum, G. RDKit: Open-source cheminformatics 2022_09_4 (Q3 2022) Release - January 16, 2023. http://www.rdkit.org/ (accessed January 18, 2023).", "Python Software Foundation - Python Language Reference, version 3.9.8. http://www.python.org."]}