cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4288736
title: Probing Dark Matter with Disappearing Tracks at the LHC

description: This data set includes: 1. Tables with efficiencies and cross section limits from disappearing charged tracks in ( life time - DM mass) plane for i2HDM (Inert Doublet Model), MFDM (Minimal Fermion DM model) and VTDM (Vector Triplet DM model). These are *_efficiency_and_xs_limit.dat files 2. Tree-level cross sections VS DM mass for Pair DM production: D+D- and D+/- D These are * _prod_xs.dat files. See details in https://arxiv.org/pdf/2008.08581.pdf
DOI: 10.5281/zenodo.4288735
title: Probing Dark Matter with Disappearing Tracks at the LHC

description: This data set includes: 1. Tables with efficiencies and cross section limits from disappearing charged tracks in ( life time - DM mass) plane for i2HDM (Inert Doublet Model), MFDM (Minimal Fermion DM model) and VTDM (Vector Triplet DM model). These are *_efficiency_and_xs_limit.dat files 2. Tree-level cross sections VS DM mass for Pair DM production: D+D- and D+/- D These are * _prod_xs.dat files. See details in https://arxiv.org/pdf/2008.08581.pdf
DOI: 10.15152/qdb.231
title: QDB archive #231

description: This QDB archive contains data and model(s) from the following publication: Fayet, G.; Rotureau, P. Development of simple QSPR models for the impact sensitivity of nitramines. J. Loss Prev. Process Ind. 2014, 30, 1–8.
DOI: 10.34740/kaggle/dsv/1678038
title: US counties COVID 19 dataset
DOI: 10.5517/ccdc.csd.cc23c4d1
title: CCDC 1947122: Experimental Crystal Structure Determination

description: Related Article: R. Kaliammal, G. Parvathy, G. Maheshwaran, K. Sankaranarayanan, M. Arivanandhan, S. Sudhahar|2020|Chinese Journal of Physics|68|436|doi:10.1016/j.cjph.2020.09.027
DOI: 10.5517/ccdc.csd.cc23grdr
title: CCDC 1950594: Experimental Crystal Structure Determination

description: Related Article: Ting-Hong Huang, Fang-Zheng Zhao, Qiao-Long Hu, Qiang Liu, Tian-Cheng Wu, Dan Zheng, Tianyi Kang, Liu-Cheng Gui, Jia Chen|2020|Inorg.Chem.|59|16027|doi:10.1021/acs.inorgchem.0c02582
DOI: 10.5517/ccdc.csd.cc2474n6
title: CCDC 1973077: Experimental Crystal Structure Determination

description: Related Article: Xiang-Long Chen, Bo-Cheng Tang, Cai He, Jin-Tian Ma, Shi-Yi Zhuang, Yan-Dong Wu, An-Xin Wu|2020|Chem.Commun.|56|13653|doi:10.1039/D0CC05800A
DOI: 10.5517/ccdc.csd.cc248z7n
title: CCDC 1974831: Experimental Crystal Structure Determination

description: Related Article: Xiling Liu, Fangyuan Gu, Xinyi Zhou, Wei Zhou, Shuping Zhang, Lei Cui, Ting Guo|2020|RSC Advances|10|38281|doi:10.1039/D0RA06564A
DOI: 10.5517/ccdc.csd.cc24l4zw
title: CCDC 1983658: Experimental Crystal Structure Determination

description: Related Article: Kyle J. Colston, Sara A. Dille, Benjamin Mogesa, Jacilynn Brant, Victor N. Nemykin, Matthias Zeller, Partha Basu|2020|RSC Advances|10|38294|doi:10.1039/D0RA07371G
DOI: 10.5517/ccdc.csd.cc25875w
title: CCDC 2003907: Experimental Crystal Structure Determination

description: Related Article: Ivan A. Skvortsov, Ulyana P. Kovkova, Yuriy A. Zhabanov, Ilya A. Khodov, Nikolay V. Somov, Georgy L. Pakhomov, Pavel A. Stuzhin|2020|Dyes Pigm.|185|108944|doi:10.1016/j.dyepig.2020.108944
DOI: 10.5517/ccdc.csd.cc259r3c
title: CCDC 2005362: Experimental Crystal Structure Determination

description: Related Article: Vagish Channa Basappa, Vivek Hamse Kameshwar, Karthik Kumara, Dileep Kumar Achutha, Lokanath Neratur Krishnappagowda, Ajay Kumar Kariyappa|2020|Heliyon|6|e05290|doi:10.1016/j.heliyon.2020.e05290
DOI: 10.5517/ccdc.csd.cc25j6xv
title: CCDC 2011587: Experimental Crystal Structure Determination

description: Related Article: Yulia M. Litvinova, Yakov M. Gayfulin, Konstantin A. Brylev, Dmitry A. Piryazev, Jan van Leusen, Paul Kögerler, Yuri V. Mironov|2020|CrystEngComm|22|7935|doi:10.1039/D0CE01240H
DOI: 10.5517/ccdc.csd.cc25jw2p
title: CCDC 2012212: Experimental Crystal Structure Determination

description: Related Article: Pierre-Luc T. Boudreault, Miguel A. Esteruelas, Ana M. López, Enrique Oñate, Esther Raga, Jui-Yi Tsai|2020|Inorg.Chem.|59|15877|doi:10.1021/acs.inorgchem.0c02395
DOI: 10.5517/ccdc.csd.cc25jw3q
title: CCDC 2012213: Experimental Crystal Structure Determination

description: Related Article: Pierre-Luc T. Boudreault, Miguel A. Esteruelas, Ana M. López, Enrique Oñate, Esther Raga, Jui-Yi Tsai|2020|Inorg.Chem.|59|15877|doi:10.1021/acs.inorgchem.0c02395
DOI: 10.5517/ccdc.csd.cc25jw4r
title: CCDC 2012214: Experimental Crystal Structure Determination

description: Related Article: Pierre-Luc T. Boudreault, Miguel A. Esteruelas, Ana M. López, Enrique Oñate, Esther Raga, Jui-Yi Tsai|2020|Inorg.Chem.|59|15877|doi:10.1021/acs.inorgchem.0c02395
DOI: 10.5517/ccdc.csd.cc25k913
title: CCDC 2012614: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Liu, Bin Liu, Man-Man Ding, Yin-Shan Meng, Jia-Hui Jing, Yi-Quan Zhang, Xincheng Wang, Shijing Lin|2020|CrystEngComm|22|7929|doi:10.1039/D0CE01147A
DOI: 10.5517/ccdc.csd.cc25k924
title: CCDC 2012615: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Liu, Bin Liu, Man-Man Ding, Yin-Shan Meng, Jia-Hui Jing, Yi-Quan Zhang, Xincheng Wang, Shijing Lin|2020|CrystEngComm|22|7929|doi:10.1039/D0CE01147A
DOI: 10.5517/ccdc.csd.cc25k935
title: CCDC 2012616: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Liu, Bin Liu, Man-Man Ding, Yin-Shan Meng, Jia-Hui Jing, Yi-Quan Zhang, Xincheng Wang, Shijing Lin|2020|CrystEngComm|22|7929|doi:10.1039/D0CE01147A
DOI: 10.5517/ccdc.csd.cc25k946
title: CCDC 2012617: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Liu, Bin Liu, Man-Man Ding, Yin-Shan Meng, Jia-Hui Jing, Yi-Quan Zhang, Xincheng Wang, Shijing Lin|2020|CrystEngComm|22|7929|doi:10.1039/D0CE01147A
DOI: 10.5517/ccdc.csd.cc25lw4t
title: CCDC 2014136: Experimental Crystal Structure Determination

description: Related Article: Surabhi Lahkar, Rakhimoni Borah, Naranarayan Deori, Sanfaori Brahma|2020|Polyhedron|192|114848|doi:10.1016/j.poly.2020.114848
DOI: 10.5517/ccdc.csd.cc25qc4f
title: CCDC 2017484: Experimental Crystal Structure Determination

description: Related Article: Zhenzhen Jiang, Lihui Fan, Ping Zhou, Tingting Xu, Jingxian Chen, Simin Hu, De-Li Chen, Yabing He|2020|Dalton Trans.|49|15672|doi:10.1039/D0DT03067H
DOI: 10.5517/ccdc.csd.cc25sk5q
title: CCDC 2019593: Experimental Crystal Structure Determination

description: Related Article: Dionisio Poveda, Ángela Vivancos, Delia Bautista, Pablo González-Herrero|2020|Chemical Science|11|12095|doi:10.1039/D0SC04879H
DOI: 10.5517/ccdc.csd.cc25tfwb
title: CCDC 2020452: Experimental Crystal Structure Determination

description: Related Article: Xin Feng, Yating Chen, Yunxiang Lei, Yunbing Zhou, Wenxia Gao, Miaochang Liu, Xiaobo Huang, Huayue Wu|2020|Chem.Commun.|56|13638|doi:10.1039/D0CC05538G
DOI: 10.5517/ccdc.csd.cc25vf1j
title: CCDC 2021387: Experimental Crystal Structure Determination

description: Related Article: Derek Cressy, Cristian Zavala, Anthony Abshire, William Sheffield, Ampofo Darko|2020|Dalton Trans.|49|15779|doi:10.1039/D0DT03019H
DOI: 10.5517/ccdc.csd.cc23grfs
title: CCDC 1950595: Experimental Crystal Structure Determination

description: Related Article: Ting-Hong Huang, Fang-Zheng Zhao, Qiao-Long Hu, Qiang Liu, Tian-Cheng Wu, Dan Zheng, Tianyi Kang, Liu-Cheng Gui, Jia Chen|2020|Inorg.Chem.|59|16027|doi:10.1021/acs.inorgchem.0c02582
DOI: 10.5281/zenodo.4021984
title: Archived GBIF Dataset: Endemic plants of the Congo basin

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> Specimens of plants endemic to either Burundi, Democratic Republic of the Congo<br> or Rwanda <strong>Temporal scope</strong> January 1, 1890 - December 31, 2014 <strong>Geographic scope</strong> Rwanda, Democratic Republic of the Congo & Burundi
DOI: 10.5281/zenodo.4021997
title: Archived GBIF Dataset: Central African Plants

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> Reference collections of plant material from Central Africa (DR Congo, Rwanda and Burundi). Digitizing specimen information is the first crucial step in the realisation of a widely accessible ‘working list of known plant species’ for this region and is an important tool for repatriation of knowledge to the countries of origin. In this first stage, we focussed on three families: Rubiaceae, Balsaminaceae and Orchidaceae. <strong>Geographic scope</strong> DR Congo, Rwanda and Burundi
DOI: 10.5281/zenodo.4021983
title: Archived GBIF Dataset: Endemic plants of the Congo basin

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> Specimens of plants endemic to either Burundi, Democratic Republic of the Congo<br> or Rwanda <strong>Temporal scope</strong> January 1, 1890 - December 31, 2014 <strong>Geographic scope</strong> Rwanda, Democratic Republic of the Congo & Burundi
DOI: 10.5281/zenodo.4021996
title: Archived GBIF Dataset: Central African Plants

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> Reference collections of plant material from Central Africa (DR Congo, Rwanda and Burundi). Digitizing specimen information is the first crucial step in the realisation of a widely accessible ‘working list of known plant species’ for this region and is an important tool for repatriation of knowledge to the countries of origin. In this first stage, we focussed on three families: Rubiaceae, Balsaminaceae and Orchidaceae. <strong>Geographic scope</strong> DR Congo, Rwanda and Burundi
DOI: 10.5281/zenodo.4022030
title: Archived GBIF Dataset: National Botanic Garden Belgium - Martius

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> Martius database is part of the ENBI WP13 feasibility study (Collaborative project between the Royal Belgian Institute of Natural Sciences, the Royal Museum for Central Africa and the National Botanical Garden). The data portal for the ENBI WP13 study can be found at http://projects.bebif.be/enbi
DOI: 10.5281/zenodo.4021999
title: Archived GBIF Dataset: National Botanic Garden Belgium - Myxomycetes

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> A project which linked and extented five ongoing initiatives (Herbaria BR, M, MA, LE & UARK) to database myxomycete collections to create a global virtual herbarium using these databases via the GBIF network with an estimated total of 90,000 specimens, containing almost one third of all types. This joint virtual herbarium enables taxonomists to access sufficiently large series of specimens. For BR all the data of the Herbarium of Mrs Nannenga Bremekamp was made accessible, also the collections of Dr J. Rammeloo and other important collections were put on the web. In total 22.857 records are made accessible, of which 11.583 records with geographical coordinates.
DOI: 10.5281/zenodo.4022029
title: Archived GBIF Dataset: National Botanic Garden Belgium - Martius

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> Martius database is part of the ENBI WP13 feasibility study (Collaborative project between the Royal Belgian Institute of Natural Sciences, the Royal Museum for Central Africa and the National Botanical Garden). The data portal for the ENBI WP13 study can be found at http://projects.bebif.be/enbi
DOI: 10.5281/zenodo.4021998
title: Archived GBIF Dataset: National Botanic Garden Belgium - Myxomycetes

description: This dataset was archived from GBIF on 2020-09-10. More up to date data on the specimens inside can be found in the Meise Botanic Garden Herbarium dataset. <strong>Description</strong> A project which linked and extented five ongoing initiatives (Herbaria BR, M, MA, LE & UARK) to database myxomycete collections to create a global virtual herbarium using these databases via the GBIF network with an estimated total of 90,000 specimens, containing almost one third of all types. This joint virtual herbarium enables taxonomists to access sufficiently large series of specimens. For BR all the data of the Herbarium of Mrs Nannenga Bremekamp was made accessible, also the collections of Dr J. Rammeloo and other important collections were put on the web. In total 22.857 records are made accessible, of which 11.583 records with geographical coordinates.
DOI: 10.15152/qdb.213
title: QDB archive #213

description: This QDB archive contains data and model(s) from the following publication: Saliner, A. G.; Netzeva, T. I.; Worth, A. P. Prediction of estrogenicity: validation of a classification model. SAR QSAR Environ. Res. 2006, 17, 195–223.
DOI: 10.6084/m9.figshare.13280795
title: Potassium_counting_experiment.csv

description: The data includes a 241 Americium calibration count, a background count and a Potassium Chloride sample count. The channel number and equivalent energy per channel following a linear model is also included. All data were taken for over 250000s (approximately 2.9 days). <br>In this experiment for the calibration, background and sample we use a thin Be-window Ge detector (Ortec LoAx). The source and detector were housed in lead brick containment. <br><br>
DOI: 10.21227/fg62-bp39
title: From Real Data of Wireless Sensor Networks based on TSCH, to a Prediction of Reliability, Power Consumption, and Latency (dataset)

description: Performance of Wireless Sensor Networks (WSN) based on IEEE 802.15.4 and Time Slotted Channel Hopping (TSCH) has been shown to be mostly predictable in typical real-world operating conditions. This is especially true for performance indicators like reliability, power consumption, and latency. This article provides and describes a database (i.e., a set of data acquired with real devices deployed in a real environment) about measurements on OpenMote B devices, implementing the 6TiSCH protocol, made in different experimental configurations. A post-analysis Python script for calculating the above performance indicators from values stored in the database is additional provided. The results obtained by applying the script to the included database were published in [1], which contains more details than those reported in this short presentation of the dataset. Data and software are useful for two main reasons: on the one hand the dataset can be further processed to obtain new performance indices, so as to support, e.g., new ideas about possible protocol modifications; on the other hand, they constitute a simple yet effective example of measurement technique (based on the ping tool and on the accompanying script), which can be customized at will and reused to analyze the performance of other real TSCH installations.
DOI: 10.21227/1nbj-xr77
title: From Real Data of Wireless Sensor Networks based on TSCH, to a Prediction of Reliability, Power Consumption, and Latency (dataset)

description: Performance of Wireless Sensor Networks (WSN) based on IEEE 802.15.4 and Time Slotted Channel Hopping (TSCH) has been shown to be mostly predictable in typical real-world operating conditions. This is especially true for performance indicators like reliability, power consumption, and latency. This article provides and describes a database (i.e., a set of data acquired with real devices deployed in a real environment) about measurements on OpenMote B devices, implementing the 6TiSCH protocol, made in different experimental configurations. A post-analysis Python script for calculating the above performance indicators from values stored in the database is additional provided. The results obtained by applying the script to the included database were published in [1], which contains more details than those reported in this short presentation of the dataset. Data and software are useful for two main reasons: on the one hand the dataset can be further processed to obtain new performance indices, so as to support, e.g., new ideas about possible protocol modifications; on the other hand, they constitute a simple yet effective example of measurement technique (based on the ping tool and on the accompanying script), which can be customized at will and reused to analyze the performance of other real TSCH installations.
DOI: 10.6073/pasta/4a6e258fb49c31e222ecbbcfd128967f
title: Lizard pitfall trap data from 11 NPP study locations at the Jornada Basin LTER site, 1989-2006

description: This data package contains data on lizards sampled by pitfall traps located at 11 consumer plots at Jornada Basin LTER site from 1989-2006. The objective of this study is to observe how shifts in vegetation resulting from desertification processes in the Chihuahaun desert have changed the spatial and temporal availability of resources for consumers. Desertification changes in the Jornada Basin include changes from grass to shrub dominated communities and major soil changes. If grassland systems respond to rainfall without significant lags, but shrub systems do not, then consumer species should reflect these differences. In addition, shifts from grassland to shrubland results in greater structural heterogeneity of the habitats. We hypothesized that consumer populations, diversity, and densities of some consumers will be higher in grasslands than in shrublands and will be related to the NPP of the sites. Lizards were captured in pitfall traps at the 11 LTER II/III consumer plots (a subset of NPP plots) quarterly for 2 weeks per quarter. Variables measured include species, sex, recapture status, snout-vent length, total length, weight, and whether tail is broken or whole. This study is complete.
DOI: 10.6084/m9.figshare.13280845.v1
title: Raw data tables 1 and 2 from Electroretinographic study of the magnetic compass in European robins

description: Raw data table for a- and b-wave amplitudes of European robins’ ERG responses. For each preparation data for “dim” and “bright” flash responses to both 470 and 630 nm stimuli are presented. Three amplitude values correspond to responses recorded under different magnetic field. The last sheet contains raw amplitude data, which were used for comparison of retinal quadrants. Raw data table for a- and b-wave times to peak of European robins’ ERG responses. For each preparation data for “dim” and “bright” flash responses to both 470 and 630 nm stimuli are presented. Three time interval values correspond to responses recorded under different magnetic field.
DOI: 10.6084/m9.figshare.13280845
title: Raw data tables 1 and 2 from Electroretinographic study of the magnetic compass in European robins

description: Raw data table for a- and b-wave amplitudes of European robins’ ERG responses. For each preparation data for “dim” and “bright” flash responses to both 470 and 630 nm stimuli are presented. Three amplitude values correspond to responses recorded under different magnetic field. The last sheet contains raw amplitude data, which were used for comparison of retinal quadrants. Raw data table for a- and b-wave times to peak of European robins’ ERG responses. For each preparation data for “dim” and “bright” flash responses to both 470 and 630 nm stimuli are presented. Three time interval values correspond to responses recorded under different magnetic field.
DOI: 10.21227/nmsb-9s45
title: From Real Data of Wireless Sensor Networks based on TSCH, to a Prediction of Reliability, Power Consumption, and Latency (dataset)

description: Performance of Wireless Sensor Networks (WSN) based on IEEE 802.15.4 and Time Slotted Channel Hopping (TSCH) has been shown to be mostly predictable in typical real-world operating conditions. This is especially true for performance indicators like reliability, power consumption, and latency. This article provides and describes a database (i.e., a set of data acquired with real devices deployed in a real environment) about measurements on OpenMote B devices, implementing the 6TiSCH protocol, made in different experimental configurations. A post-analysis Python script for calculating the above performance indicators from values stored in the database is additional provided. The results obtained by applying the script to the included database were published in [1], which contains more details than those reported in this short presentation of the dataset. Data and software are useful for two main reasons: on the one hand the dataset can be further processed to obtain new performance indices, so as to support, e.g., new ideas about possible protocol modifications; on the other hand, they constitute a simple yet effective example of measurement technique (based on the ping tool and on the accompanying script), which can be customized at will and reused to analyze the performance of other real TSCH installations.
DOI: 10.6084/m9.figshare.13130171
title: Supplementary Files

description: Contains two files:<br>Table S1: Comma separated file with columns for sequence name, taxon, FUSTr family identity, Drukewitz et al./Walker et al. annotation, whether positive selection was detected in the family, whether the family was inferred to be novel to Asilidae, toxify prediction score, to which phylogenetic node the taxon belongs, and how many sequences are present in the family.<br>FUSTR cds: Complete coding sequences for 11 asiloid taxa produced by FUSTr analysis<br><br><br><br>
DOI: 10.6084/m9.figshare.13130171.v1
title: Supplementary Files

description: Contains two files:<br>Table S1: Comma separated file with columns for sequence name, taxon, FUSTr family identity, Drukewitz et al./Walker et al. annotation, whether positive selection was detected in the family, whether the family was inferred to be novel to Asilidae, toxify prediction score, to which phylogenetic node the taxon belongs, and how many sequences are present in the family.<br>FUSTR cds: Complete coding sequences for 11 asiloid taxa produced by FUSTr analysis<br><br><br><br>
DOI: 10.21227/nngh-1867
title: From Real Data of Wireless Sensor Networks based on TSCH, to a Prediction of Reliability, Power Consumption, and Latency (dataset)

description: Performance of Wireless Sensor Networks (WSN) based on IEEE 802.15.4 and Time Slotted Channel Hopping (TSCH) has been shown to be mostly predictable in typical real-world operating conditions. This is especially true for performance indicators like reliability, power consumption, and latency. This article provides and describes a database (i.e., a set of data acquired with real devices deployed in a real environment) about measurements on OpenMote B devices, implementing the 6TiSCH protocol, made in different experimental configurations. A post-analysis Python script for calculating the above performance indicators from values stored in the database is additional provided. The results obtained by applying the script to the included database were published in [1], which contains more details than those reported in this short presentation of the dataset. Data and software are useful for two main reasons: on the one hand the dataset can be further processed to obtain new performance indices, so as to support, e.g., new ideas about possible protocol modifications; on the other hand, they constitute a simple yet effective example of measurement technique (based on the ping tool and on the accompanying script), which can be customized at will and reused to analyze the performance of other real TSCH installations.