cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/f7ht2mfk
title: Evapotranspiration (ET) data at Dead River forested wetland site, Hillsborough County, Florida, December 2009 - February 2016

description: The data set consists of evapotranspiration measurements made at the USGS Dead River forested wetland climate station beginning November 21, 2009 and ending February 29, 2016. Annual ET rates corrected to a near-surface energy-budget varied from 1448 mm (2012) to 1614 mm (2010). The eddy-covariance method was used, with high-frequency sensors installed above the forest canopy to measure sensible and latent heat fluxes. Ancillary meteorological data are also included in the data set: net radiation, soil temperature and moisture, air temperature, relative humidity, wind speed and direction, rainfall, and ground-water levels. Data were collected at 30-minute resolution, with evapotranspiration corrected to the near-surface energy-budget at a daily timescale. The study was conducted at an undeveloped mixed hardwood swamp within Dead River Wilderness Park, located at the end of Dead River Rd., west of U.S. Highway 301, 27 km northeast of Tampa in Hillsborough County, Florida (latitude 28 07 43 N longitude 82 15 44 W, Section 13, Township 27S, Range 20E). Study instruments were installed in November 2009. The 46-m instrument tower was located about 90 m northeast of the final pullout before the parking area, which is 2.6 km from the park entrance gate on Dead River Rd. The station is within the Hillsborough River floodplain, and is inundated at high river stages (Lewelling, 2004). The site is classified as a freshwater palustrine forested broad-leaf deciduous wetland that is seasonally flooded (U.S. Fish and Wildlife Service, 2010). Dominant tree species are cabbage palm (Sabal palmetto), red maple (Acer rubrum), sweet-gum (Liquidambar styraciflua), live oak (Quercus virginiana), elm (Ulmus americana), with occasional slash pine, magnolia, and hickory. Canopy height is 20-24 m. The site is located near the middle of the floodplain at least 1 km away from different land cover types in all directions. The floodplain extends farther to the northeast and southwest along the path of the river. The soils at the site are Chobee sandy loam, frequently flooded, which are nearly level, very poorly drained bottomland soils with a surface layer of black sandy loam. The subsoil is dark to very dark gray mottled sandy clay loam (Soil Conservation Service, 1989). Clay dominates in the subsoil to the east of the station based on holes dug for the tower guy cable bases. Lewelling, B.R., 2004. Extent of areal inundation of riverine wetlands along five river systems in the Upper Hillsborough River Watershed, west-central Florida: U.S. Geological Survey Scientific Investigations Report 2004-5133, 49 p., plus appendixes. Soil Conservation Service, 1989. Soil survey of Hillsborough County, Florida: U.S. Department of Agriculture, Soil Conservation Service, 168 p. U.S. Fish and Wildlife Service, 2010. National Wetlands Inventory website: U.S. Department of Interior, Washington, D.C. http://www.fws.gov/wetlands/
DOI: 10.5066/p9tkldnd
title: Evapotranspiration within Blue Cypress Marsh, Vero Beach, Florida, 2015 to 2018

description: he dataset consists of a table in Excel format of daily evapotranspiration (ET) from 12/1/2015 to 6/13/2018 at ET station within the Blue Cypress Marsh Conservation Area near Vero Beach, Florida. Evapotranspiration was estimated using eddy-covariance methods. Data processing followed methods outlined by Shoemaker and others (2011). Ancillary data includes net radiation, latent heat, sensible heat, relative humidity, air temperature, water depth above land surface or depth to water below land surface (-), and Bowen's ratio. The site location is 27 41'46", -80 42'43". Released data include mean daily latent heat (LE, in watts per square meter), available energy (Ae, in watts per square meter), and sensible heat (H, in watts per square meter), minimum and maximum daily relative humidity and air temperature (RH_max, RH_min in percent; AT_max, AT_min in degrees Celsius), water distance above (+) and below (-) land surface (WD, in meters), evapotranspiration (ET, in millimeters), and Bowen's ratio (BR, unit-less).
DOI: 10.5066/p9pyvpft
title: Point-Source Nutrient Loads to Streams of the Conterminous United States, 2012

description: The U.S. Geological Survey (USGS) developed a spatial water-quality model called SPAtially Referenced Regressions On Watershed attributes (SPARROW) to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in the Nation's streams. The SPARROW model relates in-stream water-quality data to spatially referenced characteristics of watersheds, including contaminant sources and factors influencing terrestrial and aquatic transport. Based on SPARROW modeling, one of the main nutrient sources to streams is point-source facilities such as municipal waste-water treatment plants that discharge directly to streams. This dataset was developed to assist with SPARROW models developed to assess supply, transport and fate of total nitrogen and phosphorous in streams of the conterminous United States (2012). This dataset documents discharge information from point sources in the conterminous United States and was obtained from the EPA Integrated Compliance Information System (ICIS) and Permit Compliance System (PCS). When available, nutrient concentrations were used to calculate point source loads. However, in many cases measured concentration data were not available in the ICIS or PCS data base and so &amp;amp;quot;typical pollutant concentrations&amp;amp;quot; (TPC's) were developed using data from similar facilities. A new method for calculating TPC's was implemented that allows varying amounts of nutrient concentration data and/or varying numbers of facilities to determine TPC's. This dataset contains the EPA facility, flow, and concentration data and all updates to the data, along with the TPC tables and resultant total nitrogen and phosphorous loads calculated from the input datasets.
DOI: 10.5066/p9nf0l2i
title: Aircraft-Borne Thermal Imagery and Derived Terrain Analysis Layers, Pisgah Lava Field, California

description: This dataset is one of many used in the development of the manuscript 'Advancing Cave Detection using Terrain Analysis Techniques and Thermal Imagery' by Wynne et al. 2021. Manuscript Abstract: Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) determine the utility of methods designed for terrain analysis and applied to thermal imagery; (2) analyze the usefulness of predawn and midday imagery for detecting caves; and, (3) determine which imagery type (predawn, midday, or the difference between the two) was most useful. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index [TPI], and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. We then compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: predawn - Slope, TPI, Curvature at 0 m from cave entrance, as well as Slope at 1 m from cave entrance; midday - Slope, TPI, and unenhanced thermal at 0 m from cave entrance; and, difference - TPI and Slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.
DOI: 10.5066/p9td4thx
title: Dataset for temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river

description: The trace element selenium is an essential element with a narrow window between concentrations needed to support life and those that cause toxicity to egg laying organisms. Selenium bioaccumulation in aquatic organisms is primarily the result of trophic transfer through food webs and is poorly predicted by dissolved concentrations in freshwater bodies. To better understand the hydrologic and biological dynamics that control selenium accumulation into fishes of the Lower Gunnison River Basin (Colorado), ecosystem scale selenium accumulation models were developed from data collected between June 2015 and October 2016.
DOI: 10.5066/p9d8vdhi
title: Mean annual no flow, climate and watershed properties for 540 non-perennial USGS gages in the contiguous U.S.

description: This dataset contains mean-annual metrics quantifying intermittent streamflow, climate, topography, land cover and geology for 540 USGS GAGES-2 watersheds experiencing non-perennial flow.
DOI: 10.5281/zenodo.5501702
title: La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México (Datos de Investigación)

description: Research data from the article "The role of the prison library in the social reintegration of inmates of the Puente Grande Penitentiary Complex, Jalisco, Mexico" are shown.<br> <br> <br> Se muestran los datos de investigación del artículo "La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México"
description: {"references": ["Ochoa-Garc\u00eda, J., & Mart\u00ednez-Camacho, H. (2020). La funci\u00f3n de la biblioteca penitenciaria en la reinserci\u00f3n social de los internos del Complejo Penitenciario Puente Grande, Jalisco, M\u00e9xico. Informaci\u00f3n, Cultura Y Sociedad, (43), 161-176. https://doi.org/10.34096/ics.i43.8063"]}
DOI: 10.5281/zenodo.5501704
title: bert-cased.zip

description: bert-cased.zip
DOI: 10.5281/zenodo.5501703
title: bert-cased.zip

description: bert-cased.zip
DOI: 10.5517/ccdc.csd.cc244zw5
title: CCDC 1971007: Experimental Crystal Structure Determination

description: Related Article: zheyu zhang|2019|CSD Communication|||
description: Related Article: Xiao-bo Zhao, Yi-meng Wang, Hai-feng Yu, Yuan-cheng Lv, Si-ao Jiang, Nan Wang|2021|Tetrahedron|97|132427|doi:10.1016/j.tet.2021.132427
DOI: 10.5517/ccdc.csd.cc25ny2w
title: CCDC 2016118: Experimental Crystal Structure Determination

description: Related Article: Min Chen, Zhuo-Wei Wang|2021|Polyhedron|208|115438|doi:10.1016/j.poly.2021.115438
DOI: 10.5517/ccdc.csd.cc26k5hg
title: CCDC 2042295: Experimental Crystal Structure Determination

description: Related Article: Xiaoyun Zhao, Bai Li, Jingyao Xu, Qinglin Tang, Zhengjun Cai, Xianxing Jiang|2021|Chem.-Eur.J.|27|12540|doi:10.1002/chem.202101982
DOI: 10.5517/ccdc.csd.cc26k7jk
title: CCDC 2042358: Experimental Crystal Structure Determination

description: Related Article: Xiaoyun Zhao, Bai Li, Jingyao Xu, Qinglin Tang, Zhengjun Cai, Xianxing Jiang|2021|Chem.-Eur.J.|27|12540|doi:10.1002/chem.202101982
DOI: 10.5517/ccdc.csd.cc27jq7r
title: CCDC 2071644: Experimental Crystal Structure Determination

description: Related Article: Ting-Hong Huang, Cheng Luo, Dan Zheng, Jing-Yang Tan, Yong Liu, Jing Tang|2022|J.Mol.Struct.|1247|131375|doi:10.1016/j.molstruc.2021.131375
DOI: 10.5517/ccdc.csd.cc27jq8s
title: CCDC 2071645: Experimental Crystal Structure Determination

description: Related Article: Ting-Hong Huang, Cheng Luo, Dan Zheng, Jing-Yang Tan, Yong Liu, Jing Tang|2022|J.Mol.Struct.|1247|131375|doi:10.1016/j.molstruc.2021.131375
DOI: 10.5517/ccdc.csd.cc28ln2m
title: CCDC 2103290: Experimental Crystal Structure Determination

description: Related Article: Sarika Verma, Ashoke Sharon|2021|J.Indian Chem.Soc.|98|100135|doi:10.1016/j.jics.2021.100135
DOI: 10.5517/ccdc.csd.cc28ln3n
title: CCDC 2103291: Experimental Crystal Structure Determination

description: Related Article: Sarika Verma, Ashoke Sharon|2021|J.Indian Chem.Soc.|98|100135|doi:10.1016/j.jics.2021.100135
DOI: 10.34740/kaggle/dsv/2607279
title: Nykaa App Review Sentiment
DOI: 10.6084/m9.figshare.14864427.v1
title: Arango_etal_OR DATA.xlsx

description: Blood measurements from olive ridley turtles nesting in arribada and solitary. We measured reproductive and metabolic hormones (progesterone, estradiol, testosterone, thyroxine, and triiodothyronine), triglycerides (TG), non-esterified fatty acids (NEFA) and markers of oxidative damage (4-hydroxynonenal, malondialdehyde, protein carbonyls, and nitrotyrosine).
DOI: 10.5281/zenodo.5501735
title: AOD-dry PM2.5 Relationship with Consideration of Aerosol Type and Hygroscopicity

description: The "dataset1_NGAI training" file is AERONET data for training NGAI index. The "dataset2_AOD_PM2.5" for raw relationships between AOD and PM2.5 The "dataset2_AOD_PM2.5_RH" for improving the relationships through filtering aerosol types and RH consideration.
DOI: 10.5281/zenodo.5501734
title: AOD-dry PM2.5 Relationship with Consideration of Aerosol Type and Hygroscopicity

description: The "dataset1_NGAI training" file is AERONET data for training NGAI index. The "dataset2_AOD_PM2.5" for raw relationships between AOD and PM2.5 The "dataset2_AOD_PM2.5_RH" for improving the relationships through filtering aerosol types and RH consideration.
DOI: 10.6084/m9.figshare.16608169.v1
title: Effects of neck-arm restraint suspension of beef carcasses on meat quality and proteome of different muscles during post-mortem aging

description: The proteome of 18 longissimus dorsi samples with NR and AT suspensions across three post-mortem stages of 1 d, 7d and 14 by TMT.
DOI: 10.6084/m9.figshare.16608169
title: Effects of neck-arm restraint suspension of beef carcasses on meat quality and proteome of different muscles during post-mortem aging

description: The proteome of 18 longissimus dorsi samples with NR and AT suspensions across three post-mortem stages of 1 d, 7d and 14 by TMT.
DOI: 10.25934/00005055.1
title: IPD datapackage for study 'A Randomized Trial Of Temsirolimus Versus Sorafenib As Second-Line Therapy In Patients With Advanced Renal Cell Carcinoma Who Have Failed First-Line Sunitinib Therapy' and research request: 'Inferential reproducibility of therapeutic research: a registered report for a cross-sectional study of RCTs available on major data-sharing platforms'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.5501686
title: goosynthtrain1

description: goosynthtrain1
DOI: 10.5281/zenodo.5449808
title: goosynthtrain1

description: goosynthtrain1
DOI: 10.12751/g-node.f2vqud
title: Dataset for Lakosa et al, The gut microbiome influences nicotine’s rewarding properties and glial cells in the ventral tegmental area

description: This dataset concerns the manuscript Lakosa et al where we demonstrate a role for gut microbiota in nicotine reward and glial cells in the reward system.
description: IsSupplementTo: Lakosa A, Rahimian A, Tomasi F, Tahraoui S, Marti F, Reynolds L, David V, Canonne C, Danckaert A, Tochon L, De Chaumont F, Forget B, Maskos U and Besson M. The gut microbiome influences nicotine’s rewarding properties and glial cells in the ventral tegmental area. Submitted. (https://doi.org/:tba)
DOI: 10.5281/zenodo.5501768
title: Data For Prague Tomography

description: This data file including: The 3D Vp and Vs models obtained by our new work; 3D earthquake locations base on our new 3D velocity model; Tarvel-times of P wave and S wave used in the work; The station ans injection wells information in Prague area.
DOI: 10.5281/zenodo.5501767
title: Data For Prague Tomography

description: This data file including: The 3D Vp and Vs models obtained by our new work; 3D earthquake locations base on our new 3D velocity model; Tarvel-times of P wave and S wave used in the work; The station ans injection wells information in Prague area.
DOI: 10.5281/zenodo.5501760
title: Sceliphron biotic interactions extracted from the literature

description: No description provided.
DOI: 10.5281/zenodo.5441457
title: Sceliphron biotic interactions extracted from the literature

description: No description provided.
DOI: 10.6084/m9.figshare.16608250.v1
title: Figure 4. Simulated data

description: Simulated gold standard and uncorrected batches to recreate the Figure 4 results of Canek's main manuscript.
DOI: 10.6084/m9.figshare.16608250
title: Figure 4. Simulated data

description: Simulated gold standard and uncorrected batches to recreate the Figure 4 results of Canek's main manuscript.
DOI: 10.5281/zenodo.5501754
title: goosynthpart1

description: goosynthpart1
DOI: 10.5281/zenodo.5501753
title: goosynthpart1

description: goosynthpart1
DOI: 10.15161/oar.it/74089
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.5501782
title: La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México (Datos de Investigación)

description: Research data from the article "The role of the prison library in the social reintegration of inmates of the Puente Grande Penitentiary Complex, Jalisco, Mexico" are shown.<br> <br> <br> Se muestran los datos de investigación del artículo "La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México"
description: {"references": ["Ochoa-Garc\u00eda, J., & Mart\u00ednez-Camacho, H. (2020). La funci\u00f3n de la biblioteca penitenciaria en la reinserci\u00f3n social de los internos del Complejo Penitenciario Puente Grande, Jalisco, M\u00e9xico. Informaci\u00f3n, Cultura Y Sociedad, (43), 161-176. https://doi.org/10.34096/ics.i43.8063"]}
DOI: 10.5281/zenodo.5501797
title: La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México (Datos de Investigación)

description: Research data from the article "The role of the prison library in the social reintegration of inmates of the Puente Grande Penitentiary Complex, Jalisco, Mexico" are shown.<br> <br> <br> Se muestran los datos de investigación del artículo "La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México"
description: {"references": ["Ochoa-Garc\u00eda, J., & Mart\u00ednez-Camacho, H. (2020). La funci\u00f3n de la biblioteca penitenciaria en la reinserci\u00f3n social de los internos del Complejo Penitenciario Puente Grande, Jalisco, M\u00e9xico. Informaci\u00f3n, Cultura Y Sociedad, (43), 161-176. https://doi.org/10.34096/ics.i43.8063"]}
DOI: 10.5281/zenodo.5501701
title: La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México (Datos de Investigación)

description: Research data from the article "The role of the prison library in the social reintegration of inmates of the Puente Grande Penitentiary Complex, Jalisco, Mexico" are shown.<br> <br> <br> Se muestran los datos de investigación del artículo "La función de la biblioteca penitenciaria en la reinserción social de los internos del Complejo Penitenciario Puente Grande, Jalisco, México"
description: {"references": ["Ochoa-Garc\u00eda, J., & Mart\u00ednez-Camacho, H. (2020). La funci\u00f3n de la biblioteca penitenciaria en la reinserci\u00f3n social de los internos del Complejo Penitenciario Puente Grande, Jalisco, M\u00e9xico. Informaci\u00f3n, Cultura Y Sociedad, (43), 161-176. https://doi.org/10.34096/ics.i43.8063"]}
DOI: 10.6084/m9.figshare.16608050
title: Resident waves in the ionosphere before the M6.1 Dali and M7.3 Qinghai earthquakes of 20–21 May 2021

description: TEC data from the geostationary satellites of the BDS in China.<br>The seismic data in the LESH station.
DOI: 10.6084/m9.figshare.16608050.v1
title: Resident waves in the ionosphere before the M6.1 Dali and M7.3 Qinghai earthquakes of 20–21 May 2021

description: TEC data from the geostationary satellites of the BDS in China.<br>The seismic data in the LESH station.
DOI: 10.21227/yyqv-1293
title: Wangyx_2021-P-16224_MMD-LCS_datanet

description: We consider the problem of distinguishing direct causes from direct effects of a targetvariable from multiple manipulated datasets with unknown manipulated variables and nonidenticaldata distributions. Recent studies have shown that datasets attained from manipulated experiments(i.e. manipulated data) contain richer causal information than observational data for causalstructure learning. Thus in this paper, we propose a new local causal structure learning algorithmwhich makes full use of the interventional properties of a causal model to find the parents andchildren of a target variable from multiple datasets with different manipulations. It is more suitedto real-world cases, and is also a challenge to be addressed in this paper. First, we apply thebackward framework to learn PC (parents and children) of a given target from multiplemanipulated datasets. Second, we orient some edges connected to the target in advance throughthe assumption that the target is not manipulated, and then orient the remaining undirected edgesby finding invariant V-structures from multiple datasets. Third, we analyze the correctness of ourproposed algorithm. To the best of our knowledge, the proposed algorithm is the first that canidentify the local causal structure of a given target from multiple manipulated datasets withunknown manipulated variables. Experimental results on standard Bayesian networks show thatour algorithm outperforms existing algorithms.
DOI: 10.25646/9019
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq