cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3695240
title: Data_supplemental Figure 3_11β-Hydroxysteroid dehydrogenases control access of 7β,27-dihydroxycholesterol to retinoid-related orphan receptor γ

description: Data of supplemental figure 3 from 11β-Hydroxysteroid dehydrogenases control access of 7β,27-dihydroxycholesterol to retinoid-related orphan receptor γ Dataset (doi:10.1194/jlr.M092908) contains the original figure as PNG-format (10.1194_jlr.M092908_Fig. S3). Corresponding raw data obtained from liquid scintillation analysis provided as three files in CSV format (31003A-179400_DATE_SK_KB_27Oxysterol_11_6_1-3). All further experiment related information and subsequent data analysis provided as meta-data-file (31003A-179400_DATE_SK_KB_27Oxysterol_11_6_M) as TXT format
DOI: 10.5519/qd.tvxf3ptx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4539140 records)
DOI: 10.5281/zenodo.3726261
title: Dataset for "Architectural Security Weaknesses in Industrial Control Systems: An Empirical Study Based on Security Advisories' Vulnerability Reports"

description: Supplementary artifacts to "<em>Architectural Security Weaknesses in Industrial Control Systems (ICS): An Empirical Study based on Disclosed Software Vulnerabilities</em>" Published in the Proceedings from the 2019 IEEE International Conference on Software Architecture (ICSA) Package Contains: - Raw output showing Components, CAWEs, and CVEs per report - Frequency Data (# of reports) for those concerns - ICS Component - Term Dictionary - HTML versions of reports studied in paper
DOI: 10.5255/ukda-sn-854249
title: The wind of change: maritime technology, trade and economic development 2020
DOI: 10.15468/erwu62
title: Callopistria Hübner, 1821 du massif du Mitaraka, Guyane, avec la description d’une nouvelle espèce (Noctuidae, Eriopinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Barbut, Jérôme, Lalanne-Cassou, Bernard (2019): Callopistria Hübner, 1821 du massif du Mitaraka, Guyane, avec la description d’une nouvelle espèce (Noctuidae, Eriopinae). Zoosystema 41 (20): 375-381, DOI: 10.5252/zoosystema2019v41a20
DOI: 10.21227/j8d0-6s16
title: M2M using OPC UA

description: Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems. This dataset was generated by implementing and injecting various attacks on a OPC UA based CPPS testbed, and it aim to enable users to evaluate the effectiveness of different techniques for developed Intrusion Detection Systems (IDS) in the manufacturing context.
DOI: 10.24431/rw1k448
title: Estimation of Abundance and Demographic Rates of Pacific Walruses in the Bering and Chukchi Seas, 2015

description: This study estimated abundance and demographic rates, including survival and recruitment, of Pacific walruses in the Beaufort Sea in 2015. The data collected was used to determine the feasibility of conducting a large scale and long-term capture-mark-recapture effort to determine the size of the Pacific walrus population as well as age and sex specific survival and recruitment of the population. The decline in sea ice habitat and the associated stressors are causing a population decline of Pacific walrus. This study provided a baseline for monitoring Pacific walrus populations. The data was collected from visual observations of Pacific walruses in which skin biopsy samples were collected and used for individual identification. The field data was collected on an electronic field entry system, a Juniper Mesa and converted to a csv file.
DOI: 10.7910/dvn/oxtlfn/aorcgj
title: DIM_SDG.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/eio7rq
title: DIM_POLICIES_INFO.tab

description: :unav
DOI: 10.15468/2jizxq
title: Unexpected diversity of the genus Collarina Jullien, 1886 (Bryozoa, Cheilostomatida) in the NE Atlantic-Mediterranean region: new species and reappraisal of C. balzaci (Audouin, 1826) and C. fayalensis Harmelin, 1978

description: This dataset contains the digitized treatments in Plazi based on the original journal article Harmelin, Jean-Georges, Bishop, John D. D., Madurell, Teresa, Souto, Javier, Spencer Jones, Mary E., Zabala, Mikel (2019): Unexpected diversity of the genus Collarina Jullien, 1886 (Bryozoa, Cheilostomatida) in the NE Atlantic-Mediterranean region: new species and reappraisal of C. balzaci (Audouin, 1826) and C. fayalensis Harmelin, 1978. Zoosystema 41 (21): 385-418, DOI: 10.5252/zoosystema2019v41a21
DOI: 10.21227/pafe-q652
title: M2M using OPC UA

description: Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems. This dataset was generated by implementing and injecting various attacks on a OPC UA based CPPS testbed, and it aim to enable users to evaluate the effectiveness of different techniques for developed Intrusion Detection Systems (IDS) in the manufacturing context.
DOI: 10.7910/dvn/oxtlfn/1encqh
title: DIM_SRF.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/2neohi
title: Policies cube 2018.pdf

description: :unav
DOI: 10.7910/dvn/oxtlfn/42xmpe
title: DIM_POLICY_INVESTMENT_TYPES.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/4ku2ix
title: FACT_POLICIES.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/h6bwu1
title: DIM_GEO_SCOPES.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/hroife
title: DIM_CRPS.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/rtmibf
title: DIM_STAGE_PROCESSES.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/sayk0j
title: DIM_FLAGSHIPS.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/ttvlp6
title: DIM_POLICIES_OWNERS.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/5arojm
title: DIM_OICS_METADATA.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/dugrcx
title: DIM_CCI.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/mhp7ac
title: DIM_STG_MATURITIES.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/phajxn
title: DIM_LOCATIONS.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/quc924
title: DIM_EXT_PARTNERS.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/w6avyg
title: DIM_SMO_ORG_TYPES.tab

description: :unav
DOI: 10.7910/dvn/oxtlfn/xhryg2
title: DIM_PHASES.tab

description: :unav
DOI: 10.7910/dvn/wtayll/bthdta
title: DIM_PHASES.tab

description: :unav
DOI: 10.7910/dvn/wtayll/ll8r5e
title: DIM_LOCATIONS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/qgjyb6
title: BR_SRF_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/qr7ikf
title: DIM_OICS_METADATA.tab

description: :unav
DOI: 10.7910/dvn/wtayll/vrq5nk
title: DIM_SRF_TARGETS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/yh6m6q
title: DIM_TAGS.tab

description: :unav
DOI: 10.21227/ftww-aj47
title: M2M using OPC UA

description: Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems. This dataset was generated by implementing and injecting various attacks on a OPC UA based CPPS testbed, and it aim to enable users to evaluate the effectiveness of different techniques for developed Intrusion Detection Systems (IDS) in the manufacturing context.
DOI: 10.5281/zenodo.3533605
title: Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening

description: Dataset relating to the publication: Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening by Luuk Nico van Oosten and Christian D. Klein Published in the Journal of Medicinal Chemistry, 2020 <strong>Important notice:</strong> <strong>The data are free to use for non-commercial, academic purposes, provided that the original source is<br> cited and the authors and the publication are credited in any derivative work.</strong> <strong>A patent application has been filed for the method described by van Oosten and Klein, which uses mass<br> spectrometry and machine learning to identify the pharmacological or other effects of compounds on cell<br> cultures and other biological systems.</strong> Therefore, a license for the commercial use of the method must be negotiated by contacting either Anke Faller<br> Universität Heidelberg<br> Dezernat Forschung<br> Rechts- und Strukturfragen der Forschungsförderung<br> Seminarstraße 2, 69117 Heidelberg<br> Tel. +49 6221 54-12611<br> anke.faller(at)zuv.uni-heidelberg.de or Prof. Dr. C. Klein; c.klein(at)uni-heidelberg.de<br> Medicinal Chemistry<br> Institute of Pharmacy and Molecular Biotechnology IPMB<br> Heidelberg University, INF 364<br> D-69120 Heidelberg<br> Germany<br> Phone: ++49-6221-54-4875<br> FAX : ++49-6221-54-6430
description: Important notice: The data are free to use for non-commercial, academic purposes, provided that the original source is cited and the authors and the publication are credited in any derivative work. A patent application has been filed for the method described by van Oosten and Klein, which uses mass spectrometry and machine learning to identify the pharmacological or other effects of compounds on cell cultures and other biological systems. Therefore, a license for the commercial use of the method must be negotiated by contacting either Anke Faller Universität Heidelberg Dezernat Forschung Rechts- und Strukturfragen der Forschungsförderung Seminarstraße 2, 69117 Heidelberg Tel. +49 6221 54-12611 anke.faller@zuv.uni-heidelberg.de or Prof. Dr. C. Klein; c.klein(at)uni-heidelberg.de Medicinal Chemistry Institute of Pharmacy and Molecular Biotechnology IPMB Heidelberg University, INF 364 D-69120 Heidelberg Germany Phone: ++49-6221-54-4875 FAX : ++49-6221-54-6430
DOI: 10.7910/dvn/wtayll/bg0rul
title: BR_SRFTAR_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/byb6pf
title: DIM_SMO_ORG_TYPES.tab

description: :unav
DOI: 10.7910/dvn/wtayll/ftb7od
title: DIM_CCI.tab

description: :unav
DOI: 10.7910/dvn/wtayll/ksde0d
title: BR_CCI_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/sb3zfs
title: DIM_POLICY_INVESTMENT_TYPES.tab

description: :unav
DOI: 10.7910/dvn/wtayll/tdm2js
title: DIM_GEO_SCOPES.tab

description: :unav
DOI: 10.7910/dvn/wtayll/xsjdoh
title: DIM_EXT_PARTNERS.tab

description: :unav
DOI: 10.21227/hwt6-vj04
title: M2M using OPC UA

description: Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems. This dataset was generated by implementing and injecting various attacks on a OPC UA based CPPS testbed, and it aim to enable users to evaluate the effectiveness of different techniques for developed Intrusion Detection Systems (IDS) in the manufacturing context.
DOI: 10.5281/zenodo.3533604
title: Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening

description: Dataset relating to the publication: Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening by Luuk Nico van Oosten and Christian D. Klein Published in the Journal of Medicinal Chemistry, 2020 <strong>Important notice:</strong> <strong>The data are free to use for non-commercial, academic purposes, provided that the original source is<br> cited and the authors and the publication are credited in any derivative work.</strong> <strong>A patent application has been filed for the method described by van Oosten and Klein, which uses mass<br> spectrometry and machine learning to identify the pharmacological or other effects of compounds on cell<br> cultures and other biological systems.</strong> Therefore, a license for the commercial use of the method must be negotiated by contacting either Anke Faller<br> Universität Heidelberg<br> Dezernat Forschung<br> Rechts- und Strukturfragen der Forschungsförderung<br> Seminarstraße 2, 69117 Heidelberg<br> Tel. +49 6221 54-12611<br> anke.faller(at)zuv.uni-heidelberg.de or Prof. Dr. C. Klein; c.klein(at)uni-heidelberg.de<br> Medicinal Chemistry<br> Institute of Pharmacy and Molecular Biotechnology IPMB<br> Heidelberg University, INF 364<br> D-69120 Heidelberg<br> Germany<br> Phone: ++49-6221-54-4875<br> FAX : ++49-6221-54-6430
description: Important notice: The data are free to use for non-commercial, academic purposes, provided that the original source is cited and the authors and the publication are credited in any derivative work. A patent application has been filed for the method described by van Oosten and Klein, which uses mass spectrometry and machine learning to identify the pharmacological or other effects of compounds on cell cultures and other biological systems. Therefore, a license for the commercial use of the method must be negotiated by contacting either Anke Faller Universität Heidelberg Dezernat Forschung Rechts- und Strukturfragen der Forschungsförderung Seminarstraße 2, 69117 Heidelberg Tel. +49 6221 54-12611 anke.faller@zuv.uni-heidelberg.de or Prof. Dr. C. Klein; c.klein(at)uni-heidelberg.de Medicinal Chemistry Institute of Pharmacy and Molecular Biotechnology IPMB Heidelberg University, INF 364 D-69120 Heidelberg Germany Phone: ++49-6221-54-4875 FAX : ++49-6221-54-6430
DOI: 10.7910/dvn/wtayll/0thk5c
title: DIM_SRF.tab

description: :unav
DOI: 10.7910/dvn/wtayll/584hyp
title: BR_SDG_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/9pfswd
title: DIM_STAGE_PROCESSES.tab

description: :unav
DOI: 10.7910/dvn/wtayll/cwyxpt
title: FACT_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/eyifwm
title: BR_EXTPART_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/fhjo6g
title: DIM_FLAGSHIPS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/p6tdy2
title: DIM_CRPS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/vmqkl2
title: DIM_STG_MATURITIES.tab

description: :unav
DOI: 10.7910/dvn/wtayll/etanov
title: DIM_SDG.tab

description: :unav
DOI: 10.7910/dvn/wtayll/hz42lj
title: BR_LOC_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/plkh08
title: BR_GEO_OICS.tab

description: :unav
DOI: 10.7910/dvn/wtayll/tfxl3n
title: BR_FLAGSHIP_OICS.tab

description: :unav
DOI: 10.15468/ccbaii
title: Three new species of Lecteria Osten Sacken, 1888 (Diptera: Tipulidae) from a scientific survey in Mitaraka (French Guiana)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Mederos, Jorge, Pollet, Marc (2019): Three new species of Lecteria Osten Sacken, 1888 (Diptera: Tipulidae) from a scientific survey in Mitaraka (French Guiana). Zoosystema 41 (25): 497-512, DOI: 10.5252/zoosystema2019v41a25
DOI: 10.21227/mzc6-jt52
title: Fetal Heart Rate Features of Healthy and Late IUGR Fetuses

description: The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis. Antepartum CTG recordings were collected at the Azienda Ospedaliera Universitaria Federico II (Napoli, Italy), during daily routine monitoring. Traces were recorded in a controlled clinical environment, with participants lying supine and undergoing a standard non-stress test protocol using Philips Avalon FM30. The acquired traces are of duration equal to 40 minutes and sampled at 2 Hz.Each CTG recording was subdivided in epochs of 120 samples (1-min) or 360 samples (3-min) prior to feature extraction. The choice of 1-min or 3-min subintervals is based upon to the different time scales on which FHR indices are computed. Regarding artifacts identification, we discarded segments including more than five consecutive samples or more than 5% of samples (6 FHR values out of 120 points per subinterval or 18 FHR values out of 360 points per subinterval) for which the FHR could not be detected. Lastly, isolated samples of insufficient quality were substituted by the moving average of the nearest five FHR points. The parameters obtained for all the segments of one recording are then averaged to derive a single value for each parameter for any given recording.
DOI: 10.21227/z8ze-1a02
title: Fetal Heart Rate Features of Healthy and Late IUGR Fetuses

description: The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis. Antepartum CTG recordings were collected at the Azienda Ospedaliera Universitaria Federico II (Napoli, Italy), during daily routine monitoring. Traces were recorded in a controlled clinical environment, with participants lying supine and undergoing a standard non-stress test protocol using Philips Avalon FM30. The acquired traces are of duration equal to 40 minutes and sampled at 2 Hz.Each CTG recording was subdivided in epochs of 120 samples (1-min) or 360 samples (3-min) prior to feature extraction. The choice of 1-min or 3-min subintervals is based upon to the different time scales on which FHR indices are computed. Regarding artifacts identification, we discarded segments including more than five consecutive samples or more than 5% of samples (6 FHR values out of 120 points per subinterval or 18 FHR values out of 360 points per subinterval) for which the FHR could not be detected. Lastly, isolated samples of insufficient quality were substituted by the moving average of the nearest five FHR points. The parameters obtained for all the segments of one recording are then averaged to derive a single value for each parameter for any given recording.
DOI: 10.15468/8nk8ix
title: North Carolina State Museum of Natural Sciences Fishes Collection
DOI: 10.21227/avbd-qf78
title: Fetal Heart Rate Features of Healthy and Late IUGR Fetuses

description: The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis. Antepartum CTG recordings were collected at the Azienda Ospedaliera Universitaria Federico II (Napoli, Italy), during daily routine monitoring. Traces were recorded in a controlled clinical environment, with participants lying supine and undergoing a standard non-stress test protocol using Philips Avalon FM30. The acquired traces are of duration equal to 40 minutes and sampled at 2 Hz.Each CTG recording was subdivided in epochs of 120 samples (1-min) or 360 samples (3-min) prior to feature extraction. The choice of 1-min or 3-min subintervals is based upon to the different time scales on which FHR indices are computed. Regarding artifacts identification, we discarded segments including more than five consecutive samples or more than 5% of samples (6 FHR values out of 120 points per subinterval or 18 FHR values out of 360 points per subinterval) for which the FHR could not be detected. Lastly, isolated samples of insufficient quality were substituted by the moving average of the nearest five FHR points. The parameters obtained for all the segments of one recording are then averaged to derive a single value for each parameter for any given recording.
DOI: 10.21227/h748-pe41
title: Fetal Heart Rate Features of Healthy and Late IUGR Fetuses

description: The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis. Antepartum CTG recordings were collected at the Azienda Ospedaliera Universitaria Federico II (Napoli, Italy), during daily routine monitoring. Traces were recorded in a controlled clinical environment, with participants lying supine and undergoing a standard non-stress test protocol using Philips Avalon FM30. The acquired traces are of duration equal to 40 minutes and sampled at 2 Hz.Each CTG recording was subdivided in epochs of 120 samples (1-min) or 360 samples (3-min) prior to feature extraction. The choice of 1-min or 3-min subintervals is based upon to the different time scales on which FHR indices are computed. Regarding artifacts identification, we discarded segments including more than five consecutive samples or more than 5% of samples (6 FHR values out of 120 points per subinterval or 18 FHR values out of 360 points per subinterval) for which the FHR could not be detected. Lastly, isolated samples of insufficient quality were substituted by the moving average of the nearest five FHR points. The parameters obtained for all the segments of one recording are then averaged to derive a single value for each parameter for any given recording.