cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6421437
title: OpenStreetMap+ Protected nature areas in continental Europe (IUCN status + Natura 2000)

description: Twelve maps of continental Europe indicating the protected nature area status in 2019 according to Natura 2000 and the International Union for Conservation of Nature (IUCN). The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de. This dataset contains: 3 raster maps representing Natura 2000 protection status (A, B and C), named <strong>Natura2000_[status].tif</strong> 8 raster maps representing OSM-derived IUCN protection status (1a, 1b, 2, 3, 4, 5, 6, and 'other'), named <strong>OSM_IUCN_[status].tif</strong> 1 aggregated map (<strong>adm_protected.area_natura2000.osm_p_30m_0..0cm_2019..2021_eumap_epsg3035_v0.1</strong>) where each of the 11 protection statuses, as well as pixels where multiple statuses apply, are assigned a unique value. This map can also be accessed interactively at maps.opendatascience.eu. All files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files for the aggregated raster are provided in both <strong><em>SLD</em></strong> and <strong><em>QML</em></strong> format.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.6415107
title: OpenStreetMap+ Protected nature areas in continental Europe (IUCN status + Natura 2000)

description: Twelve maps of continental Europe indicating the protected nature area status in 2019 according to Natura 2000 and the International Union for Conservation of Nature (IUCN). The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de. This dataset contains: 3 raster maps representing Natura 2000 protection status (A, B and C), named <strong>Natura2000_[status].tif</strong> 8 raster maps representing OSM-derived IUCN protection status (1a, 1b, 2, 3, 4, 5, 6, and 'other'), named <strong>OSM_IUCN_[status].tif</strong> 1 aggregated map (<strong>adm_protected.area_natura2000.osm_p_30m_0..0cm_2019..2021_eumap_epsg3035_v0.1</strong>) where each of the 11 protection statuses, as well as pixels where multiple statuses apply, are assigned a unique value. This map can also be accessed interactively at maps.opendatascience.eu. All files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files for the aggregated raster are provided in both <strong><em>SLD</em></strong> and <strong><em>QML</em></strong> format.
description: This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5517/ccdc.csd.cc12qp2k
title: CCDC 1004898: Experimental Crystal Structure Determination

description: Related Article: Ruiyuan Liu, Shuyan Yang, Yuqiang Ding|2022|Inorg.Chem.Commun.|142|109596|doi:10.1016/j.inoche.2022.109596
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: 10.5517/ccdc.csd.cc24j21t
title: CCDC 1981645: Experimental Crystal Structure Determination

description: Related Article: J. Srividya, V. Sivamadhavi, G. Anbalagan|2022|Opt.Mater.|129|112503|doi:10.1016/j.optmat.2022.112503
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: 10.5517/ccdc.csd.cc298mbk
title: CCDC 2123448: Experimental Crystal Structure Determination

description: Related Article: Junyi Wang, Penghui Jia, Weicheng Sun, Yuxiang Wei, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|8879|doi:10.1021/acs.inorgchem.2c00944
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: 10.5517/ccdc.csd.cc298mcl
title: CCDC 2123449: Experimental Crystal Structure Determination

description: Related Article: Junyi Wang, Penghui Jia, Weicheng Sun, Yuxiang Wei, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|8879|doi:10.1021/acs.inorgchem.2c00944
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: 10.5517/ccdc.csd.cc298mdm
title: CCDC 2123450: Experimental Crystal Structure Determination

description: Related Article: Junyi Wang, Penghui Jia, Weicheng Sun, Yuxiang Wei, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|8879|doi:10.1021/acs.inorgchem.2c00944
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: 10.5517/ccdc.csd.cc298mfn
title: CCDC 2123451: Experimental Crystal Structure Determination

description: Related Article: Junyi Wang, Penghui Jia, Weicheng Sun, Yuxiang Wei, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|8879|doi:10.1021/acs.inorgchem.2c00944
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: 10.5517/ccdc.csd.cc298mgp
title: CCDC 2123452: Experimental Crystal Structure Determination

description: Related Article: Junyi Wang, Penghui Jia, Weicheng Sun, Yuxiang Wei, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|8879|doi:10.1021/acs.inorgchem.2c00944
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: 10.5517/ccdc.csd.cc298mhq
title: CCDC 2123453: Experimental Crystal Structure Determination

description: Related Article: Junyi Wang, Penghui Jia, Weicheng Sun, Yuxiang Wei, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|8879|doi:10.1021/acs.inorgchem.2c00944
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: 10.5517/ccdc.csd.cc298mjr
title: CCDC 2123454: Experimental Crystal Structure Determination

description: Related Article: Junyi Wang, Penghui Jia, Weicheng Sun, Yuxiang Wei, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|8879|doi:10.1021/acs.inorgchem.2c00944
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: 10.5517/ccdc.csd.cc29bf9d
title: CCDC 2125183: Experimental Crystal Structure Determination

description: Related Article: Ya-Jie Hu, Qian Lan, Bao-Jun Su, Zhen-Feng Chen, Dong Liang|2022|Phytochemistry|201|113255|doi:10.1016/j.phytochem.2022.113255
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.6645483
title: musclesyneRgies - Test dataset

description: Test dataset for the R package musclesyneRgies. The "RAW_DATA.RData" R list consists of 30 elements of S3 class "EMG", each of which is a human locomotion trial of 30 gait cycles (or 60 steps) containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. The name of each trial contains subject ID, locomotion type and trial number in the form "ID0003_TW_01", where TW = treadmill walking and TR = treadmill running.
description: Raw data adapted to fit the requirements of the R package "musclesyneRgies".
DOI: 10.5281/zenodo.6645482
title: musclesyneRgies - Test dataset

description: Test dataset for the R package musclesyneRgies. The "RAW_DATA.RData" R list consists of 30 elements of S3 class "EMG", each of which is a human locomotion trial of 30 gait cycles (or 60 steps) containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. The name of each trial contains subject ID, locomotion type and trial number in the form "ID0003_TW_01", where TW = treadmill walking and TR = treadmill running.
description: Raw data adapted to fit the requirements of the R package "musclesyneRgies".
DOI: 10.48758/ufz.12770
title: Hohes Holz (DE-HoH) daily averages of spatially distributed soil moisture measurements

description: Hohes Holz (DE-HoH) daily aggregates of spatially distributed soil moisture

Soil water content sensors from a distributed monitoring network (SoilNet-WSN with SPADE sensors, sceme.de GmbH, Germany, Bogena et al., 2010) were originally installed in the frame of a trenching experiment (Marañón-Jiménez et al., 2021) in April 2014 and distributed within a fenced research area considering patches with low and high tree (and thus root) density (21 nodes) for 15 locations. The six sensors of a node were installed in vertical profiles ranging from 0.1 m to 0.6 m depth. Additionally, sensors of six more nodes were distributed in the shallow layer between 10 cm and 30 cm to cover the higher soil moisture dynamics of this soil layer. Of the total setup described in Marañón-Jiménez et al. (2021), only sensors without soil treatment were used for our analysis.
 
Soil moisture data from the distributed wireless network were acquired every 10 min via a network coordinator and stored on a field computer. In addition soil moisture was also measured with CS616 sensors (Campbell Scientific Inc., Logan, Utah, USA) in two additional profiles. Those data were also acquired and stored as 10 min averages by a CR1000 data logger (Campbell Scientific Inc., Logan, Utah, USA). Physically unrealistic data were removed by semi-automated procedures that check for limit exceedances (values below 0 or above local average porosity) and spikes unrelated to precipitation. Daily averages were calculated per sensor if more than 20 % of data per day was available. Sensors were only considered if at least 30 measurement days were available to avoid low statistic power. In total, the data used here consists of measurements from 30 sensors in 10cm, 15 sensors in 20cm, 24 sensors in 30cm, 16 sensors in 40cm and 16 sensors in 50cm. The 60cm layer was ignored due to very low data availability. 

Bogena, H., Herbst, M., Huisman, J., Rosenbaum, U., Weuthen, A., & Vereecken, H. (2010). Potential of Wireless Sensor Networks for Measuring Soil Water Content Variability. Vadose Zone Journal, 9 (4), 1002–1013. doi: 10.2136/vzj2009.0173

Marañón-Jiménez, S., Radujković, D., Verbruggen, E., Grau, O., Cuntz, M., Peñuelas, J., ... Rebmann, C. (2021, July).
Shifts in the Abundances of Saprotrophic and Ectomycorrhizal Fungi With Altered Leaf Litter Inputs. Frontiers in Plant Science, 12 , 682142. doi: 10.3389/fpls.2021.682142
DOI: 10.3886/e112854v1-121000
title: Replication data for: Subways, Strikes, and Slowdowns: The Impacts of Public Transit on Traffic Congestion

description: Public transit accounts for 1 percent of U.S. passenger miles traveled but attracts strong public support. Using a simple choice model, we predict that transit riders are likely to be individuals who commute along routes with severe roadway delays. These individuals' choices thus have high marginal impacts on congestion. We test this prediction with data from a strike in 2003 by Los Angeles transit workers. Estimating a regression discontinuity design, we find that average highway delay increases 47 percent when transit service ceases. We find that the net benefits of transit systems appear to be much larger than previously believed.
description: Major freeways in Los Angeles County.
DOI: 10.6084/m9.figshare.20088230
title: Global_IGBP_LULC_projection_dataset_under_eight_SSPs-RCPs.zip

description: Global future land use land cover data projected based on MODIS LC product and LUH2 under eight SSP-RCPs (2015-2100), every five yeas
DOI: 10.15131/shef.data.19723468
title: Data Package for "Revealing localised mechanochemistry of biomaterials using in-situ multiscale chemical analysis"

description: Data Package for 'Revealing localised mechanochemistry of biomaterials using in-situ multiscale chemical analysis', <em>Materials</em>, 2022, 15(10), 3462. https://doi.org/10.3390/ma15103462.
DOI: 10.34740/kaggle/dsv/3818104
title: AutoXGB
DOI: 10.6084/m9.figshare.20087822
title: Global_IGBP_LULC_projection_dataset_under_eight_SSPs-RCPs

description: future land use land cover under eight coupled ssp-rcp scenarios (2015-2100), every five year intervals
DOI: 10.6084/m9.figshare.19404139
title: Molecularly imprinted polymers: preparation, characterisation, and application in drug delivery systems

description: Molecular imprinting technology defines the creation of molecularly imprinted polymer (MIP) molecules in which template molecules can place in a key-lock relationship through shape, diameter, and functional groups. Although molecular imprinting technology has been employed in different fields, its applications in drug delivery systems (DDSs) have gained momentum recently. The high loading efficiency, high stability, and controlled drug release are the primary advantages of MIPs. Here, the main components, preparation methods, and characterisation tests of MIPs are summarised, and their applications in DDSs administered by different routes are evaluated in detail. The review offers a perspective on molecular imprinting technology and applications of MIPs in drug delivery by surveying the literature approximately 1998–2021 together with the outlined prospects.
DOI: 10.6084/m9.figshare.19404139.v3
title: Molecularly imprinted polymers: preparation, characterisation, and application in drug delivery systems

description: Molecular imprinting technology defines the creation of molecularly imprinted polymer (MIP) molecules in which template molecules can place in a key-lock relationship through shape, diameter, and functional groups. Although molecular imprinting technology has been employed in different fields, its applications in drug delivery systems (DDSs) have gained momentum recently. The high loading efficiency, high stability, and controlled drug release are the primary advantages of MIPs. Here, the main components, preparation methods, and characterisation tests of MIPs are summarised, and their applications in DDSs administered by different routes are evaluated in detail. The review offers a perspective on molecular imprinting technology and applications of MIPs in drug delivery by surveying the literature approximately 1998–2021 together with the outlined prospects.
DOI: 10.6084/m9.figshare.19404139.v2
title: Molecularly imprinted polymers: preparation, characterisation, and application in drug delivery systems

description: Molecular imprinting technology defines the creation of molecularly imprinted polymer (MIP) molecules in which template molecules can place in a key-lock relationship through shape, diameter, and functional groups. Although molecular imprinting technology has been employed in different fields, its applications in drug delivery systems (DDSs) have gained momentum recently. The high loading efficiency, high stability, and controlled drug release are the primary advantages of MIPs. Here, the main components, preparation methods, and characterisation tests of MIPs are summarised, and their applications in DDSs administered by different routes are evaluated in detail. The review offers a perspective on molecular imprinting technology and applications of MIPs in drug delivery by surveying the literature approximately 1998–2021 together with the outlined prospects.
DOI: 10.6084/m9.figshare.19898424
title: Matlab codes for generating the figures in the main text, as well as those in the supplement from The transition between acute and chronic infections in light of energy control: a mathematical model of energy flow in response to infection

description: Background: Different parts of an organism like the gut, endocrine, nervous and immune systems constantly exchange information. Understanding the pathogenesis of various systemic chronic diseases increasingly relies on understanding how these subsystems orchestrate their activities. Methods: We started from the working hypothesis that energy is a fundamental quantity that governs activity levels of all subsystems and that interactions between subsystems control the distribution of energy according to acute needs. Based on physiological knowledge, we constructed a mathematical model for the energy flow between subsystems and analysed the resulting organismal responses to <i>in silico</i> infections. Results: The model reproduces common behaviour in acute infections and suggests several host parameters that modulate infection duration and therapeutic responsiveness. Moreover, the model allows the formulation of conditions for the induction of chronic infections and predicts that alterations in energy released from fat can lead to the transition from clearance of acute infections to a chronic inflammatory state. Impact: These results suggest a fundamental role for brain and fat in controlling immune response through systemic energy control. In particular, it suggests that lipolysis resistance, which is known to be involved in obesity and ageing, might be a survival programme for coping with chronic infections.
DOI: 10.6084/m9.figshare.19898424.v2
title: Matlab codes for generating the figures in the main text, as well as those in the supplement from The transition between acute and chronic infections in light of energy control: a mathematical model of energy flow in response to infection

description: Background: Different parts of an organism like the gut, endocrine, nervous and immune systems constantly exchange information. Understanding the pathogenesis of various systemic chronic diseases increasingly relies on understanding how these subsystems orchestrate their activities. Methods: We started from the working hypothesis that energy is a fundamental quantity that governs activity levels of all subsystems and that interactions between subsystems control the distribution of energy according to acute needs. Based on physiological knowledge, we constructed a mathematical model for the energy flow between subsystems and analysed the resulting organismal responses to <i>in silico</i> infections. Results: The model reproduces common behaviour in acute infections and suggests several host parameters that modulate infection duration and therapeutic responsiveness. Moreover, the model allows the formulation of conditions for the induction of chronic infections and predicts that alterations in energy released from fat can lead to the transition from clearance of acute infections to a chronic inflammatory state. Impact: These results suggest a fundamental role for brain and fat in controlling immune response through systemic energy control. In particular, it suggests that lipolysis resistance, which is known to be involved in obesity and ageing, might be a survival programme for coping with chronic infections.