cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23659155.v1
title: Supplementary Material for: A Tunable Calcium Phosphate Coating to Drive in vivo Osseointegration of Composite Engineered Tissues

description: Varying degrees of hydroxyapatite (HA) surface functionalization have been implicated as the primary driver of differential osteogenesis observed in infiltrating cells. The ability to reliably create spatially controlled areas of mineralization in composite engineered tissues is of growing interest in the field, and the use of HA-functionalized biomaterials may provide a robust solution to this challenge. In this study, we successfully fabricated polycaprolactone salt-leached scaffolds with two levels of a biomimetic calcium phosphate coating to examine their effects on MSC osteogenesis. Longer duration coating in simulated body fluid (SBF) led to increased HA crystal nucleation within scaffold interiors as well as more robust HA crystal formation on scaffold surfaces. Ultimately, the increased surface stiffness of scaffolds coated in SBF for 7 days in comparison to scaffolds coated in SBF for 1 day led to more robust osteogenesis of MSCs in vitro without the assistance of osteogenic signaling molecules. This study also demonstrated that the use of SBF-based HA coatings can promote higher levels of osteogenesis in vivo. Finally, when incorporated as the endplate region of a larger tissue-engineered intervertebral disc replacement, HA coating did not induce mineralization in or promote cell migration out of neighboring biomaterials. Overall, these results verified tunable biomimetic HA coatings as a promising biomaterial modification to promote discrete regions of mineralization within composite engineered tissues.
DOI: 10.21227/t7ef-6n46
title: HDLNET: A Hybrid Deep Learning Network Model with Intelligent IOT for Detection and Classification of Chronic Kidney Disease

description: Over 10% of the world's population now suffers from chronic kidney disease (CKD), and millions die yearly. To extend the lives of those suffering and lower the cost of therapy, CKD should be detected early. Building such a multimedia-driven model is necessary to detect the illness effectively and accurately before it worsens the situation. It is challenging for doctors to identify the various conditions connected to CKD early to prevent the condition. For CKD early detection and prediction, this study introduces a novel hybrid deep learning network model (HDLNet). A deep learning-based technique called the Deep Separable Convolution Neural Network (DSCNN) has been suggested in this research for the early detection of CKD. More processing attributes of characteristics chosen to indicate a kidney issue are extracted by the Capsule Network (CapsNet). Using the Aquila Optimisation Algorithm (AO) method, the pertinent characteristics are selected to speed up the categorization process. The necessary features improve classification effectiveness while needing less computational effort. The DSCNN technique is optimized to diagnose kidney illness as CKD or non-CKD using the Sooty Tern Optimization Algorithm (STOA). The CKD dataset, found in the UCI machine learning repository, is then used to test the dataset. Accuracy, sensitivity, MCC, PPV, FPR, FNR, and specificity are the performance metrics for the suggested CKD classification approach. Additional experimental findings demonstrate that the suggested method produces a better categorization of CKD than the present state-of-the-art method.
DOI: 10.18170/dvn/wcvb7r
title: Curated Dataset of Eight Chinese Ancient Book Catalogs

description: This is a structured dataset that include eight Chinese Ancient Book Catalogs from Han to Qing.
DOI: 10.5281/zenodo.8133543
title: Supplemental data for: Current ethics policies in 36 preprint servers

description: Preprints, which are non–peer-reviewed articles, offer a rapid dissemination of information, and have gained traction in academic publishing, often serving as the publishing precursors of peer-reviewed journals. Given that many peer-reviewed journals, with often strict ethics policies, allow the prior publication of papers as preprints, we were interested in appreciating the ethics policies in place in preprint servers. We selected 36 preprint servers from the ASAPbio list to base our assessment. We found that that only 8% of analyzed preprint servers contained a dedicated section on the server's ethics policy. As documents that purportedly transmit scientific information, including data-based evidence, we believe that there is a need to fortify the stringency of ethics policies in preprint servers, even standardize them across ASAPbio-listed preprint servers.
DOI: 10.5281/zenodo.8133542
title: Supplemental data for: Current ethics policies in 36 preprint servers

description: Preprints, which are non–peer-reviewed articles, offer a rapid dissemination of information, and have gained traction in academic publishing, often serving as the publishing precursors of peer-reviewed journals. Given that many peer-reviewed journals, with often strict ethics policies, allow the prior publication of papers as preprints, we were interested in appreciating the ethics policies in place in preprint servers. We selected 36 preprint servers from the ASAPbio list to base our assessment. We found that that only 8% of analyzed preprint servers contained a dedicated section on the server's ethics policy. As documents that purportedly transmit scientific information, including data-based evidence, we believe that there is a need to fortify the stringency of ethics policies in preprint servers, even standardize them across ASAPbio-listed preprint servers.
DOI: 10.5281/zenodo.7950723
title: Supplemental data for: A bibliometric assessment of the incidence of amyloid-Eszett (Aß), a false positive of amyloid-beta (Aβ), in the neurodegenerative disease literature

description: One claimed reason for the development of Alzheimer’s disease (AD), a prominent neurodegenerative disease, is the extracellular aggregation of amyloid-beta (Aβ). A linguistic or formatting error has resulted in the misrepresentation of the Greek letter β with the German letter Eszett (ß), resulting in the formation of a non-existent compound, amyloid-Eszett (Aß). These datasets offer a quantified appreciation of the AD-related literature, carrying a mention of this false positive in the title, abstract and keywords of papers indexed in the Web of Science Core Collection and Scopus. Also, as a curiosity given the popularity of this large language model, we asked the questions to ChatGPT. This AI chatbot developed by OpenAI was able to recognize Eszett as a linguistic or typographic error, within this context, recognizing Aß and Aβ as equals. This erroneous substitution of a Greek letter (in Aβ) by a German one (Aß), despite giving a non-existent compound, will likely not change the underlying scientific conclusions of the affected papers, although errata might be useful to enlighten others, including metadata managers and journal copyeditors, so as not to repeat the same mistake.
description: {"references": ["Nazarovets, S., & Teixeira da Silva, J. A. (2023). A bibliometric assessment of the incidence of amyloid\u2010 Eszett (A\u00df), a false positive of amyloid\u2010 beta (A\u03b2), in the neurodegenerative disease literature. European Journal of Neuroscience, 115(April), 1\u20133. https://doi.org/10.1111/ejn.16058"]}
DOI: 10.5281/zenodo.7950722
title: Supplemental data for: A bibliometric assessment of the incidence of amyloid-Eszett (Aß), a false positive of amyloid-beta (Aβ), in the neurodegenerative disease literature

description: One claimed reason for the development of Alzheimer’s disease (AD), a prominent neurodegenerative disease, is the extracellular aggregation of amyloid-beta (Aβ). A linguistic or formatting error has resulted in the misrepresentation of the Greek letter β with the German letter Eszett (ß), resulting in the formation of a non-existent compound, amyloid-Eszett (Aß). These datasets offer a quantified appreciation of the AD-related literature, carrying a mention of this false positive in the title, abstract and keywords of papers indexed in the Web of Science Core Collection and Scopus. Also, as a curiosity given the popularity of this large language model, we asked the questions to ChatGPT. This AI chatbot developed by OpenAI was able to recognize Eszett as a linguistic or typographic error, within this context, recognizing Aß and Aβ as equals. This erroneous substitution of a Greek letter (in Aβ) by a German one (Aß), despite giving a non-existent compound, will likely not change the underlying scientific conclusions of the affected papers, although errata might be useful to enlighten others, including metadata managers and journal copyeditors, so as not to repeat the same mistake.
description: {"references": ["Nazarovets, S., & Teixeira da Silva, J. A. (2023). A bibliometric assessment of the incidence of amyloid\u2010 Eszett (A\u00df), a false positive of amyloid\u2010 beta (A\u03b2), in the neurodegenerative disease literature. European Journal of Neuroscience, 115(April), 1\u20133. https://doi.org/10.1111/ejn.16058"]}
DOI: 10.4121/b6bb4c63-dc61-4a7d-9ad9-66f82139604a.v1
title: Data underlying the publication: Rapid flapping and fiber-reinforced membrane wings are key to high-performance bat flight

description: The dataset underlying the research article "Rapid flapping and fiber-reinforced membrane wings are key to high-performance bat flight", where fully-coupled fluid-structure interaction simulations are used to investigate the different performance aspect of bat flight, namely the influence of the kinematics, specifically the flapping frequency and secondly the material properties of the membrane wing. This dataset contains the post-processed data (phase-averaged) for the structural response of the membrane wing for the selected case presented in the article. The script generates the figures associated with the article and the corresponding analysis.
DOI: 10.4121/e1d89cb5-6872-48fc-be63-aadd687ee6f9
title: Modified Swiss Dwellings: a Machine Learning-ready Dataset for Floor Plan Auto-Completion at Scale

description: <em>Modified Swiss Dwellings</em>The Modified Swiss Dwellings (MSD) dataset is a <strong>machine learning-ready dataset for floor plan auto-completion at scale</strong>. The MSD dataset is derived from the Swiss Dwellings database (v3.0.0). The MSD dataset (train split) contains <strong>4167 floor plans</strong> of <strong>single- as well as multi-unit building complexes across Switzerland</strong>, hence extending the building scale w.r.t. of other well know floor plan datasets like the RPLAN dataset. Since the MSD dataset will be part of a challenge @ ICCV in Paris, 2023, October 3, the <strong>test split is not yet made public</strong>. This will be added after the submission deadline of the challenge, which will be around mid September 2023.<br><em>Cleaning, filtering, and processing</em>All cleaning, filtering, and processing is done in Python. The Swiss Dwellings database is cleaned and filtered on residential building complexes that have a minimum room count (&gt;10) and have at least 2 "Zone 2" rooms (<em>e.g.</em>, living room, corridor, kitchen, dining). A graph extraction algorithm fully based on the `shapely` and `networkx` libraries in Python was developed to extract the access graphs from the filtered floor plans.<br><em>Dataset structure</em>The MSD dataset contains 3 files.<br>1) A README.md file explaining the dataset.2) A training set ZIP archive, containing 4 folders: `graph_in` [&lt;index&gt;.pickle], `struct_in` [&lt;index&gt;.npy], `full_out` [&lt;index&gt;.npy], and `graph_out` [&lt;index&gt;.pickle]. Naming is consistent across all folders, meaning that an instance from `graph_in` with name "&lt;index&gt;.pickle" is from the same floor plan as an instance from `full_out` with name "&lt;index&gt;.npy".3) A test set ZIP archive, containing 2 folders: `graph_in` and `struct_in` (similarly structured as the training data; but obviously with withheld annotations.)<br><em>Floor plan auto-completion</em>The MSD dataset is developed with the goal for the computer science community to develop (deep learning) models for the task of floor plan auto-completion. The floor plan auto-completion task takes as input the boundary of a building, the structural elements necessary for the building’s structural integrity, and a set of user constraints formalized in a graph structure, with the goal of automatically generating the full floor plan. Specifically, the goal is to learn the correlation between the the joint distribution of `graph_in` and `struct_in` with that of `full_out`. `graph_out` is provided when researchers want to use / develop methods from graph signal processing, or graph machine learning specifically.<br><em>GIthub guidelines</em>Github page.
DOI: 10.4121/e1d89cb5-6872-48fc-be63-aadd687ee6f9.v2
title: Modified Swiss Dwellings: a Machine Learning-ready Dataset for Floor Plan Auto-Completion at Scale

description: <em>Modified Swiss Dwellings</em>The Modified Swiss Dwellings (MSD) dataset is a <strong>machine learning-ready dataset for floor plan auto-completion at scale</strong>. The MSD dataset is derived from the Swiss Dwellings database (v3.0.0). The MSD dataset (train split) contains <strong>4167 floor plans</strong> of <strong>single- as well as multi-unit building complexes across Switzerland</strong>, hence extending the building scale w.r.t. of other well know floor plan datasets like the RPLAN dataset. Since the MSD dataset will be part of a challenge @ ICCV in Paris, 2023, October 3, the <strong>test split is not yet made public</strong>. This will be added after the submission deadline of the challenge, which will be around mid September 2023.<br><em>Cleaning, filtering, and processing</em>All cleaning, filtering, and processing is done in Python. The Swiss Dwellings database is cleaned and filtered on residential building complexes that have a minimum room count (&gt;10) and have at least 2 "Zone 2" rooms (<em>e.g.</em>, living room, corridor, kitchen, dining). A graph extraction algorithm fully based on the `shapely` and `networkx` libraries in Python was developed to extract the access graphs from the filtered floor plans.<br><em>Dataset structure</em>The MSD dataset contains 3 files.<br>1) A README.md file explaining the dataset.2) A training set ZIP archive, containing 4 folders: `graph_in` [&lt;index&gt;.pickle], `struct_in` [&lt;index&gt;.npy], `full_out` [&lt;index&gt;.npy], and `graph_out` [&lt;index&gt;.pickle]. Naming is consistent across all folders, meaning that an instance from `graph_in` with name "&lt;index&gt;.pickle" is from the same floor plan as an instance from `full_out` with name "&lt;index&gt;.npy".3) A test set ZIP archive, containing 2 folders: `graph_in` and `struct_in` (similarly structured as the training data; but obviously with withheld annotations.)<br><em>Floor plan auto-completion</em>The MSD dataset is developed with the goal for the computer science community to develop (deep learning) models for the task of floor plan auto-completion. The floor plan auto-completion task takes as input the boundary of a building, the structural elements necessary for the building’s structural integrity, and a set of user constraints formalized in a graph structure, with the goal of automatically generating the full floor plan. Specifically, the goal is to learn the correlation between the the joint distribution of `graph_in` and `struct_in` with that of `full_out`. `graph_out` is provided when researchers want to use / develop methods from graph signal processing, or graph machine learning specifically.<br><em>GIthub guidelines</em>Github page.
DOI: 10.5281/zenodo.3901000
title: Nobel Laureates' publications 1994-2018 in Physics, Chemistry and Physiology or Medicine co-authored with scientists from Ukraine

description: Publications of Nobel Laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine , co-authored with scientists from Ukrainian institutions. Appendix to the article «Quantitative analysis of the co-publications of Ukrainian scientists with the Nobel laureates 1994-2018 in Science».
description: {"references": ["Nazarovets, S. A. (2020). Quantitative Analysis of the Co-Publications of Ukrainian Scientists with the Nobel Laureates 1994-2018 in Science. Nauka ta innovacii, 16(5), 110\u2013117. https://doi.org/10.15407/scin16.05.110"]}
DOI: 10.5281/zenodo.3900999
title: Nobel Laureates' publications 1994-2018 in Physics, Chemistry and Physiology or Medicine co-authored with scientists from Ukraine

description: Publications of Nobel Laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine , co-authored with scientists from Ukrainian institutions. Appendix to the article «Quantitative analysis of the co-publications of Ukrainian scientists with the Nobel laureates 1994-2018 in Science».
description: {"references": ["Nazarovets, S. A. (2020). Quantitative Analysis of the Co-Publications of Ukrainian Scientists with the Nobel Laureates 1994-2018 in Science. Nauka ta innovacii, 16(5), 110\u2013117. https://doi.org/10.15407/scin16.05.110"]}
DOI: 10.5281/zenodo.6865957
title: Supplemental data for: Variations in the naming of malondialdehyde (MDA) in PubMed-, Scopus-, and Web of Science-indexed literature

description: Three scientific databases (PubMed, Scopus, and Web of Science (WoS)) were consulted (July 14, 2022) to assess the frequency of eight nomenclatural forms of malondialdehyde (MDA). Due to the peculiarities of the search interface of the selected databases, PubMed was searched in the Title and Abstract fields (search query example in PubMed: "malone dialdehyde"[Title/Abstract]), Scopus was searched in the Title, Abstract, and Keywords fields (search query example in Scopus: TITLE-ABS-KEY (“malone dialdehyde”)), and WoS Core Collection was searched in the Title, Abstract, Author keywords, and Keywords Plus fields (search query example in WoS Core Collection: TS=(“malone dialdehyde”)). All types of publications for the years 2002-2021 are taken into account.
description: {"references": ["Teixeira da Silva, J. A., & Nazarovets, S. (2023). Variations in the naming of malondialdehyde (MDA) in PubMed-, Scopus-, and Web of science-indexed literature. International Journal of Phytoremediation, 25(6), 759\u2013764. https://doi.org/10.1080/15226514.2022.2106184"]}
DOI: 10.5281/zenodo.6865958
title: Supplemental data for: Variations in the naming of malondialdehyde (MDA) in PubMed-, Scopus-, and Web of Science-indexed literature

description: Three scientific databases (PubMed, Scopus, and Web of Science (WoS)) were consulted (July 14, 2022) to assess the frequency of eight nomenclatural forms of malondialdehyde (MDA). Due to the peculiarities of the search interface of the selected databases, PubMed was searched in the Title and Abstract fields (search query example in PubMed: "malone dialdehyde"[Title/Abstract]), Scopus was searched in the Title, Abstract, and Keywords fields (search query example in Scopus: TITLE-ABS-KEY (“malone dialdehyde”)), and WoS Core Collection was searched in the Title, Abstract, Author keywords, and Keywords Plus fields (search query example in WoS Core Collection: TS=(“malone dialdehyde”)). All types of publications for the years 2002-2021 are taken into account.
description: {"references": ["Teixeira da Silva, J. A., & Nazarovets, S. (2023). Variations in the naming of malondialdehyde (MDA) in PubMed-, Scopus-, and Web of science-indexed literature. International Journal of Phytoremediation, 25(6), 759\u2013764. https://doi.org/10.1080/15226514.2022.2106184"]}
DOI: 10.6084/m9.figshare.23654430
title: INNOV-2023-0003 data table.xlsx

description: Data obtained in a small clinical study on the protective effects of sunscreen against sunlight-induced DNA damage.
DOI: 10.25919/60ez-rr88
title: Parkes observations for project P574 semester 2023APRS_05

description: We request time to observe 260 pulsars on a regular basis in order to provide the accurate ephemerides necessary for the detection and characterisation of gamma-ray pulsars with the Fermi satellite. The main science goals are to increase the number of known gamma-ray pulsars (both radio loud and radio quiet), to determine accurate pulse profiles, and to characterise their high energy (phase-resolved) spectra. In the radio, the observations will also allow us to investigate: neutron stars and pulsar emission via detecting glitches and determining pulse timing parameters; and the ISM via measuring and monitoring dispersion measures, Faraday rotation measures, and scintillation. To date, we are (co-)authors on over 95 papers arising from the collaboration and P574 data, including four which use UWL observations. The data have contributed to the PhD theses of students from Bordeaux, Manchester, Oxford, Stanford, and Swinburne.
DOI: 10.5281/zenodo.7804748
title: An Extensive Dataset of Geometric Objects Placed in a Watertank for Electrical Impedance Tomography

description: Open-access experimental Electrical Impedance Tomography (EIT) data set.<br> The measurements were generated using the ScioSpec EIT device from which 16 electrodes were connected to a phantom tank filled with saline. Four 3D-printed cylindrical objects of different sizes were placed in this tank area-wide. Adjacent and opposite injection patterns have been used to measure large datasets with more than 125000 samples. The tutorial can be found here: https://github.com/JacobTh98/sciospeceit/blob/main/tutorial.ipynb All data is available as .npz file. If you want this data in this file format, please contact: jacob.thoenes@uni-rostock.de
description: This work was financially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as part of the projects SFB 1270/2 (grant: 299150580).
DOI: 10.5281/zenodo.7804747
title: An Extensive Dataset of Geometric Objects Placed in a Watertank for Electrical Impedance Tomography

description: Open-access experimental Electrical Impedance Tomography (EIT) data set.<br> The measurements were generated using the ScioSpec EIT device from which 16 electrodes were connected to a phantom tank filled with saline. Four 3D-printed cylindrical objects of different sizes were placed in this tank area-wide. Adjacent and opposite injection patterns have been used to measure large datasets with more than 125000 samples. The tutorial can be found here: https://github.com/JacobTh98/sciospeceit/blob/main/tutorial.ipynb Contact: jacob.thoenes@uni-rostock.de
description: This work was financially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as part of the projects SFB 1270/2 (grant: 299150580).
DOI: 10.5281/zenodo.8133546
title: An Extensive Dataset of Geometric Objects Placed in a Watertank for Electrical Impedance Tomography

description: Open-access experimental Electrical Impedance Tomography (EIT) data set.<br> The measurements were generated using the ScioSpec EIT device from which 16 electrodes were connected to a phantom tank filled with saline. Four 3D-printed cylindrical objects of different sizes were placed in this tank area-wide. Adjacent and opposite injection patterns have been used to measure large datasets with more than 125000 samples. The tutorial can be found here: https://github.com/JacobTh98/sciospeceit/blob/main/tutorial.ipynb Contact: jacob.thoenes@uni-rostock.de
description: This work was financially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as part of the projects SFB 1270/2 (grant: 299150580).