cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1475307
title: Data Supplementing Article "Dramatic Hydrodynamic And Sedimentary Responses In Galveston Bay And Adjacent Inner Shelf To Hurricane Harvey"

description: This is data set used to plotting the figure that describes the relationship between surface landward current at buoy station g06010 and section-averaged landward current at the entrance of Galveston Bay.
DOI: 10.5281/zenodo.1475306
title: Data Supplementing Article "Dramatic Hydrodynamic And Sedimentary Responses In Galveston Bay And Adjacent Inner Shelf To Hurricane Harvey"

description: This is data set used to plotting the figure that describes the relationship between surface landward current at buoy station g06010 and section-averaged landward current at the entrance of Galveston Bay.
DOI: 10.7910/dvn/tehrtn/15cfxs
title: 41-1_0080.dcm
DOI: 10.7910/dvn/tehrtn/h7qowx
title: 41-5_0247.dcm
DOI: 10.7910/dvn/tehrtn/i8kvrv
title: 41-5_0138.dcm
DOI: 10.7910/dvn/tehrtn/mlccik
title: 41-1_0235.dcm
DOI: 10.7910/dvn/tehrtn/yk5oua
title: 41-1_0034.dcm
DOI: 10.17632/y7swgwgckw.1
title: Data for: Theoretical and experimental study on the uniformity of reflective high concentration photovoltaic system with light funnel

description: This appendix includes original models, data and diagrams.
DOI: 10.5281/zenodo.1475135
title: Sunbird Performance Data For Fastsum Openqcd 1.0

description: Strong and weak scaling run data for Fastsum OpenQCD 1.0 on the Sunbird cluster using both the AVX-512 extension and the original AVX2 version. Performance tests of the Dirac operator using the time1 and time2 utilities provided with the code.

Contains the compilation and run parameters, run output and programs used to gather and plot the data.
DOI: 10.5281/zenodo.1475136
title: Sunbird Performance Data For Fastsum Openqcd 1.0

description: Strong and weak scaling run data for Fastsum OpenQCD 1.0 on the Sunbird cluster using both the AVX-512 extension and the original AVX2 version. Performance tests of the Dirac operator using the time1 and time2 utilities provided with the code.

Contains the compilation and run parameters, run output and programs used to gather and plot the data.
DOI: 10.7910/dvn/tehrtn/6nn7bo
title: 41-5_0165.dcm
DOI: 10.7910/dvn/tehrtn/b0lqbv
title: 41-5_0046.dcm
DOI: 10.7910/dvn/tehrtn/beabex
title: 41-1_0105.dcm
DOI: 10.7910/dvn/tehrtn/feibk0
title: 41-5_0169.dcm
DOI: 10.7910/dvn/tehrtn/klbutn
title: 41-5_0022.dcm
DOI: 10.7910/dvn/tehrtn/rl5m7k
title: 41-5_0090.dcm
DOI: 10.7910/dvn/tehrtn/sirlgy
title: 41-5_0234.dcm
DOI: 10.7910/dvn/tehrtn/ukcji3
title: 41-1_0045.dcm
DOI: 10.6073/pasta/f39a2c9d8e78e6d7a949e93af12e9bf9
title: Phoenix Area Social Survey (PASS): 2011

description: The Phoenix Area Social Survey (PASS) parallels the Ecological Survey of Central Arizona (formerly, Survey 200) as a long-term monitoring program of the CAP LTER. Every five years, the PASS research team surveys households in selected neighborhoods in the metropolitan Phoenix area to better understand perceptions, values, and behaviors on several key environmental issues, including water conservation, urban growth, air pollution, land conservation, biodiversity and urban climate change, as well as perceptions about their neighborhoods. The survey was piloted in 2001-2002 in eight neighborhoods in Phoenix with 302 respondents and grew to 40 neighborhoods and 800 households in 2005. The PASS research team added five new neighborhoods to the survey design in 2011 to incorporate areas co-located with other CAP research endeavors and to expand the sample of low-income neighborhoods and included additional questions relevant to the housing crisis and recession and access to healthy food in the city.
DOI: 10.7910/dvn/tehrtn/5lhhi4
title: 41-1_0156.dcm
DOI: 10.7910/dvn/tehrtn/7gakxs
title: 41-5_0100.dcm
DOI: 10.7910/dvn/tehrtn/efl66c
title: 41-1_0081.dcm
DOI: 10.7910/dvn/tehrtn/fjtxtd
title: 41-1_0093.dcm
DOI: 10.7910/dvn/tehrtn/htp8m2
title: 41-1_0157.dcm
DOI: 10.7910/dvn/tehrtn/janseo
title: 41-5_0245.dcm
DOI: 10.7910/dvn/tehrtn/kuy7zf
title: 41-1_0020.dcm
DOI: 10.7910/dvn/tehrtn/oceg2k
title: 41-5_0237.dcm
DOI: 10.7910/dvn/tehrtn/zuy52l
title: 41-5_0243.dcm
DOI: 10.5523/bris.3lfkl8guhfjf2ain57w18p7ue
title: Reaction Kinetics of Criegee Intermediates with Ammonia and Methylamine
title: Criegee Intermediate reactions with amines

description: Data for a NERC funded project NE/P013104/1 entitled Kinetic Studies of Reactive Intermediates from the Oxidation of Atmospheric Alkenes
DOI: 10.7910/dvn/tehrtn/2o7951
title: 41-5_0184.dcm
DOI: 10.7910/dvn/tehrtn/a5qjo1
title: 41-5_0131.dcm
DOI: 10.7910/dvn/tehrtn/bvjubd
title: 41-5_0028.dcm
DOI: 10.7910/dvn/tehrtn/jq1jjd
title: 41-1_0048.dcm
DOI: 10.7910/dvn/tehrtn/lfg3xl
title: 41-1_0241.dcm
DOI: 10.7910/dvn/tehrtn/smmyki
title: 41-1_0092.dcm
DOI: 10.7910/dvn/tehrtn/vqierc
title: 41-5_0045.dcm
DOI: 10.7910/dvn/tehrtn/xwaumj
title: 41-5_0013.dcm
DOI: 10.7910/dvn/tehrtn/xxu8v4
title: 41-5_0206.dcm
DOI: 10.7910/dvn/tehrtn/z7ok8l
title: 41-5_0189.dcm
DOI: 10.5255/ukda-sn-6702-13
title: Monthly Wages and Salaries Survey, 2000-2018: Secure Access
DOI: 10.5281/zenodo.1475304
title: The Simulation Dataset Of Gript: A Novel Case-Control Analysis Method For Mendelian Disease Gene Discovery

description: The simulation dataset of GRIPT: a novel case-control analysis method for Mendelian disease gene discovery

To uncompress the data:

tar -xvf Simulation_data.tar.gz

The meaning of the datasets:

sim_X_Y.tar.gz means the simulation is generated with the maximum population frequency cutoff of X and the sample size of Y.

sim_X_Y.tar.gz is generated based on the average allele frequency in population.

sim_X_Y_AMR.tar.gz is generated based on the allele frequency in Latino population.

sim_X_Y_500_Z.tar.gz is mixed of Latino population with a proportion of (500-Z)/500, and African population with a proportion of Z/500.

The allele frequency is based on the ExAC database (Lek et al Nature 2016)

Within each folder, there are case or control folders. The case folder contains the simulation spiked in the HGMD mutation of the given gene (i.e. RPE65 or TINF2) in the given percentage of individuals (e.g. 0.5%, 1%, 2%, 3%). The control folder contains the simulation without HGMD mutation spiked in.
DOI: 10.5281/zenodo.1475305
title: The Simulation Dataset Of Gript: A Novel Case-Control Analysis Method For Mendelian Disease Gene Discovery

description: The simulation dataset of GRIPT: a novel case-control analysis method for Mendelian disease gene discovery

To uncompress the data:

tar -xvf Simulation_data.tar.gz

The meaning of the datasets:

sim_X_Y.tar.gz means the simulation is generated with the maximum population frequency cutoff of X and the sample size of Y.

sim_X_Y.tar.gz is generated based on the average allele frequency in population.

sim_X_Y_AMR.tar.gz is generated based on the allele frequency in Latino population.

sim_X_Y_500_Z.tar.gz is mixed of Latino population with a proportion of (500-Z)/500, and African population with a proportion of Z/500.

The allele frequency is based on the ExAC database (Lek et al Nature 2016)

Within each folder, there are case or control folders. The case folder contains the simulation spiked in the HGMD mutation of the given gene (i.e. RPE65 or TINF2) in the given percentage of individuals (e.g. 0.5%, 1%, 2%, 3%). The control folder contains the simulation without HGMD mutation spiked in.
DOI: 10.7910/dvn/tehrtn/3nbcra
title: 41-5_0093.dcm
DOI: 10.7910/dvn/tehrtn/eqvz5a
title: 41-1_0132.dcm
DOI: 10.7910/dvn/tehrtn/feuuok
title: 41-5_0229.dcm
DOI: 10.7910/dvn/tehrtn/nt4tuo
title: 41-5_0009.dcm
DOI: 10.7910/dvn/tehrtn/pfstsu
title: 41-5_0042.dcm
DOI: 10.7910/dvn/tehrtn/uhpvwv
title: 41-5_0244.dcm
DOI: 10.7910/dvn/tehrtn/vzw79q
title: 41-5_0025.dcm
DOI: 10.7910/dvn/tehrtn/1dwvea
title: 41-5_0078.dcm
DOI: 10.7910/dvn/tehrtn/3ggzqg
title: 41-5_0133.dcm
DOI: 10.7910/dvn/tehrtn/4c55gu
title: 41-1_0201.dcm
DOI: 10.7910/dvn/tehrtn/4l6y6f
title: 41-5_0111.dcm
DOI: 10.7910/dvn/tehrtn/ebyzj0
title: 41-1_0137.dcm
DOI: 10.7910/dvn/tehrtn/gw9lgn
title: 41-5_0147.dcm
DOI: 10.7910/dvn/tehrtn/iqz7by
title: 41-5_0197.dcm
DOI: 10.7910/dvn/tehrtn/uaonim
title: 41-5_0050.dcm
DOI: 10.7910/dvn/tehrtn/wclxa3
title: 41-5_0083.dcm
DOI: 10.7910/dvn/tehrtn/xhygwn
title: 41-5_0194.dcm
DOI: 10.17638/datacat.liverpool.ac.uk/584
title: Fluorene and Fluorenone raw STM data
DOI: 10.5287/bodleian:n1abay0ez
title: Spin dynamics of coupled spin ladders near quantum criticality in Ba2CuTeO6
DOI: 10.7910/dvn/tehrtn/6qr1pi
title: 41-1_0094.dcm
DOI: 10.7910/dvn/tehrtn/8al99s
title: 41-5_0136.dcm
DOI: 10.7910/dvn/tehrtn/acami6
title: 41-1_0184.dcm
DOI: 10.7910/dvn/tehrtn/dltbxt
title: 41-5_0071.dcm
DOI: 10.7910/dvn/tehrtn/eaqyo5
title: 41-5_0055.dcm
DOI: 10.7910/dvn/tehrtn/f16ojk
title: 41-1_0144.dcm
DOI: 10.7910/dvn/tehrtn/lgv2qu
title: 41-5_0145.dcm
DOI: 10.7910/dvn/tehrtn/oeswz2
title: 41-1_0035.dcm
DOI: 10.7910/dvn/tehrtn/svyqr8
title: 41-1_0131.dcm
DOI: 10.7910/dvn/tehrtn/4qipjj
title: 41-5_0024.dcm
DOI: 10.7910/dvn/tehrtn/9yvgwc
title: 41-1_0098.dcm
DOI: 10.7910/dvn/tehrtn/bfrosd
title: 41-1_0107.dcm
DOI: 10.7910/dvn/tehrtn/kh8mr5
title: 41-5_0202.dcm
DOI: 10.7910/dvn/tehrtn/kubvyc
title: 41-5_0103.dcm
DOI: 10.7910/dvn/tehrtn/lm8q6f
title: 41-1_0176.dcm
DOI: 10.7910/dvn/tehrtn/no350w
title: 41-1_0181.dcm
DOI: 10.7910/dvn/tehrtn/vnmj1z
title: 41-5_0005.dcm
DOI: 10.7910/dvn/tehrtn/xnbvp0
title: 41-1_0145.dcm
DOI: 10.5281/zenodo.1473831
title: Ergebnisse Der Umfrage Zum Thema 3D-Digitalisierung Für Wissenschaftliche Sammlungen

description: Um einen aktuellen Überblick über die 3D-Digitalisierung in der Arbeit mit wissenschaftlichen Sammlungen in Deutschland zu gewinnen, wurde für die Datenerhebung eine Online-Umfrage durchgeführt. Dabei ging es insbesondere um die Fragen, welche Arten von Objekten digitalisiert werden, welche Verfahren zum Einsatz kommen und für welche Forschungs- und Nutzungsszenarien 3D-Scans und -Modelle erzeugt werden. Darüber hinaus wurden Probleme und Herausforderungen in der Nutzung von 3D-Technologien und -Verfahren abgefragt.

<br>
Die Umfrage startete am 07.05.2018 und endete am 20.05.2018. Zum Starttag wurde die Umfrage auf unterschiedlichen Wegen annonciert, um möglichst viele Ansprechpartner von wissenschaftlichen Sammlungen zu erreichen. Insgesamt nahmen 121 Personen an der Umfrage teil.<br>
 

Der vorliegende Datensatz enthält die Rohdatensätze und die prozessierten Datensätze (Befragungsdaten, Auswertungstabellen, Diagramme, Kategorien und Listen) der Umfrage sowie den verwendeten Online-Fragebogen (Google Forms Layout). Die dazugehörige Publikation „3D-Digitalisierung in der Arbeit mit wissenschaftlichen Sammlungen: aktuelle Tendenzen und Herausforderungen“ (2018) kann unter folgendem Link abgerufen werden urn:nbn:de:kobv:525-22882.
DOI: 10.5281/zenodo.1473832
title: Ergebnisse Der Umfrage Zum Thema 3D-Digitalisierung Für Wissenschaftliche Sammlungen

description: Um einen aktuellen Überblick über die 3D-Digitalisierung in der Arbeit mit wissenschaftlichen Sammlungen in Deutschland zu gewinnen, wurde für die Datenerhebung eine Online-Umfrage durchgeführt. Dabei ging es insbesondere um die Fragen, welche Arten von Objekten digitalisiert werden, welche Verfahren zum Einsatz kommen und für welche Forschungs- und Nutzungsszenarien 3D-Scans und -Modelle erzeugt werden. Darüber hinaus wurden Probleme und Herausforderungen in der Nutzung von 3D-Technologien und -Verfahren abgefragt.

<br>
Die Umfrage startete am 07.05.2018 und endete am 20.05.2018. Zum Starttag wurde die Umfrage auf unterschiedlichen Wegen annonciert, um möglichst viele Ansprechpartner von wissenschaftlichen Sammlungen zu erreichen. Insgesamt nahmen 121 Personen an der Umfrage teil.<br>
 

Der vorliegende Datensatz enthält die Rohdatensätze und die prozessierten Datensätze (Befragungsdaten, Auswertungstabellen, Diagramme, Kategorien und Listen) der Umfrage sowie den verwendeten Online-Fragebogen (Google Forms Layout). Die dazugehörige Publikation „3D-Digitalisierung in der Arbeit mit wissenschaftlichen Sammlungen: aktuelle Tendenzen und Herausforderungen“ (2018) kann unter folgendem Link abgerufen werden urn:nbn:de:kobv:525-22882.
DOI: 10.6073/pasta/af8e909e5c1f9a45ab0b4eca4180fab3
title: Lake Water Level observations for 1036 lakes in Wisconsin, 1900 - 2015

description: This dataset contains the daily lake level observations and other lake attributes in Wisconsin. It covers 1036 lakes including 461 seepage lakes and 575 drainage lakes. It has about 342,300 observations. The time span of this dataset is between January 1st, 1900 and December 31st, 2015. The data sources include USGS, Wisconsin Department of Natural Resources, North Temperate Lakes-Long Term Ecological Research (NTL-LTER), North Lakeland Discovery Center, Waushara County, and City of Shell. Wisconsin Department of Natural Resources has two data sources, the historical lake levels recorded in paper files and the data from a recently-initiated citizen monitoring program. The latter are stored in Wisconsin Surface Water Integrated Monitoring System (SWIMS).
The data compilation consists of four major steps. First, the data were retrieved from different data sources. Then the data from different sources but for the same lakes were tied together by the datum information. Third, three rounds of quality assurance and quality control (QAQC) were conducted. Finally, more attributes such as lake area, lake depth, and lake type were added to the lakes.
The project that compiles the data is funded by Wisconsin Groundwater Joint Solicitation.
DOI: 10.7910/dvn/tehrtn/5ixbml
title: 41-5_0008.dcm
DOI: 10.7910/dvn/tehrtn/7utgyp
title: 41-1_0242.dcm
DOI: 10.7910/dvn/tehrtn/9o3xyq
title: 41-1_0147.dcm
DOI: 10.7910/dvn/tehrtn/c4w1dc
title: 41-5_0159.dcm
DOI: 10.7910/dvn/tehrtn/md6w1z
title: 41-1_0135.dcm
DOI: 10.7910/dvn/tehrtn/or7jzg
title: 41-1_0072.dcm
DOI: 10.7910/dvn/tehrtn/p2nefv
title: 41-1_0029.dcm
DOI: 10.7910/dvn/tehrtn/tgh5ar
title: 41-5_0123.dcm
DOI: 10.7910/dvn/tehrtn/77utdo
title: 41-5_0107.dcm
DOI: 10.7910/dvn/tehrtn/h4nzka
title: 41-1_0150.dcm
DOI: 10.7910/dvn/tehrtn/lkmgfn
title: 41-5_0081.dcm
DOI: 10.7910/dvn/tehrtn/ojnemm
title: 41-1_0119.dcm
DOI: 10.7910/dvn/tehrtn/peqv3k
title: 41-1_0103.dcm
DOI: 10.7910/dvn/tehrtn/r5n9kq
title: 41-1_0071.dcm
DOI: 10.7910/dvn/tehrtn/u6zp6t
title: 41-5_0128.dcm
DOI: 10.7910/dvn/tehrtn/yjttsg
title: 41-5_0031.dcm
DOI: 10.7910/dvn/tehrtn/yygtgi
title: 41-5_0076.dcm
DOI: 10.17632/p3fncfj7dh.1
title: Data for: Confidence and Varieties of Bias

description: Definitions for data file accompanying “Confidence and Varieties of Bias” Andrew Heathcote, Eleanor Holloway and James Sauer email: andrew.heathcote@utas.edu.au RData file (bring into R with "load" command) with the following columns s # Subject column, 32 subjects Levels: "0" "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" BLK # Type of block (bias manipulation type or none in control) Levels: "payoff" "proportion" "control" S # stimulus Levels: "blue" "orange" D # stimulus difficulty Levels: "easy" "hard" B # bias favors Levels: "none" "blue" "orange" BF # bias for, i.e., bias favors stimulus, NA for control condition Levels: "no" "yes" R # Response Levels: "blue" "orange" C # accuracy of responce Levels: "FALSE" "TRUE" RT # response time in seconds (RT &lt; .2 not censored as done for paper analysis)
DOI: 10.17638/datacat.liverpool.ac.uk/569
title: Plasma Polymerisation Using Helium Atmospheric-Pressure Plasma Jet with Heptylamine Monomer
DOI: 10.23721/109/1479494
title: internet_outage_adaptive_a33all-20180701 (07/01/2018 to 10/01/2018)

description: Combined reachability information for over 3.5M /24 subnets; this dataset is created by post-processing data from datasets internet_outage_adaptive_a33c-20180701 internet_outage_adaptive_a33e-20180701 internet_outage_adaptive_a33g-20180701 internet_outage_adaptive_a33j-20180701 internet_outage_adaptive_a33n-20180701 internet_outage_adaptive_a33w-20180701
DOI: 10.23721/109/1479495
title: internet_outage_adaptive_a33c-20180701 (07/01/2018 to 10/01/2018)

description: To collect this data, all allocated and responsive Internet IP address blocks were pinged by sending ICMP ECHO_REQUEST (PING) packet. The block status was recorded in this data set. Probe was repeated every 11 minutes, and more quickly when uncertain about block status. In all, approximately 3.5M /24 subnets were periodically probed.
DOI: 10.7910/dvn/tehrtn/3sogpp
title: 41-5_0231.dcm
DOI: 10.7910/dvn/tehrtn/alohke
title: 41-1_0130.dcm
DOI: 10.7910/dvn/tehrtn/bxys9o
title: 41-5_0010.dcm
DOI: 10.7910/dvn/tehrtn/hrjnf5
title: 41-1_0100.dcm
DOI: 10.7910/dvn/tehrtn/jezwlx
title: 41-1_0245.dcm
DOI: 10.7910/dvn/tehrtn/kkbjpi
title: 41-5_0015.dcm
DOI: 10.7910/dvn/tehrtn/sj6bdv
title: 41-5_0227.dcm
DOI: 10.7910/dvn/tehrtn/ufnnce
title: 41-5_0226.dcm
DOI: 10.7910/dvn/tehrtn/vgjdr9
title: 41-5_0109.dcm
DOI: 10.7910/dvn/tehrtn/w60cno
title: 41-5_0067.dcm
DOI: 10.17632/948n98trm6.1
title: Data for: Factors affecting sexual function and body image of early stage breast cancer survivors in Poland: A short-term observation.

description: Date set used for statistical analysis
DOI: 10.23721/109/1479496
title: internet_outage_adaptive_a33e-20180701 (07/01/2018 to 10/01/2018)

description: To collect this data, all allocated and responsive Internet IP address blocks were pinged by sending ICMP ECHO_REQUEST (PING) packet. The block status was recorded in this data set. Probe was repeated every 11 minutes, and more quickly when uncertain about block status. In all, approximately 3.5M /24 subnets were periodically probed.
DOI: 10.23721/109/1479497
title: internet_outage_adaptive_a33g-20180701 (07/01/2018 to 10/01/2018)

description: To collect this data, all allocated and responsive Internet IP address blocks were pinged by sending ICMP ECHO_REQUEST (PING) packet. The block status was recorded in this data set. Probe was repeated every 11 minutes, and more quickly when uncertain about block status. In all, approximately 3.5M /24 subnets were periodically probed.
DOI: 10.23721/109/1479498
title: internet_outage_adaptive_a33j-20180701 (07/01/2018 to 10/01/2018)

description: To collect this data, all allocated and responsive Internet IP address blocks were pinged by sending ICMP ECHO_REQUEST (PING) packet. The block status was recorded in this data set. Probe was repeated every 11 minutes, and more quickly when uncertain about block status. In all, approximately 3.5M /24 subnets were periodically probed.
DOI: 10.23721/109/1479499
title: internet_outage_adaptive_a33n-20180701 (07/01/2018 to 10/01/2018)

description: To collect this data, all allocated and responsive Internet IP address blocks were pinged by sending ICMP ECHO_REQUEST (PING) packet. The block status was recorded in this data set. Probe was repeated every 11 minutes, and more quickly when uncertain about block status. In all, approximately 3.5M /24 subnets were periodically probed.
DOI: 10.23721/109/1479500
title: internet_outage_adaptive_a33w-20180701 (07/01/2018 to 10/01/2018)

description: To collect this data, all allocated and responsive Internet IP address blocks were pinged by sending ICMP ECHO_REQUEST (PING) packet. The block status was recorded in this data set. Probe was repeated every 11 minutes, and more quickly when uncertain about block status. In all, approximately 3.5M /24 subnets were periodically probed.
DOI: 10.7910/dvn/tehrtn/80s0i3
title: 41-5_0144.dcm
DOI: 10.7910/dvn/tehrtn/ed3eum
title: 41-5_0016.dcm
DOI: 10.7910/dvn/tehrtn/guh5te
title: 41-1_0104.dcm
DOI: 10.7910/dvn/tehrtn/irnbqu
title: 41-1_0055.dcm
DOI: 10.7910/dvn/tehrtn/z9etdx
title: 41-5_0241.dcm
DOI: 10.7910/dvn/tehrtn/zbcsb2
title: 41-1_0051.dcm
DOI: 10.7910/dvn/tehrtn/zjo9jj
title: 41-5_0178.dcm
DOI: 10.7910/dvn/tehrtn/zsftvc
title: 41-5_0114.dcm
DOI: 10.6073/pasta/b4e060225d118511f3f6df0ceb9bb1a7
title: Ground water levels for 964  monitoring wells Wisconsin, 1929 - 2015

description: This dataset contains the daily groundwater level observations and other monitoring well attributes in Wisconsin. It covers 964 groundwater level monitoring wells and has 400,812 observations. The time span of this dataset is between February 2nd, 1929 and December 31st, 2015. The data sources include United States Geological Survey (USGS), Wisconsin Department of Natural Resources (WDNR) / University of Wisconsin Extension / Counties in Central Sands area, and North Temperate Lakes - Long-Term Ecological Research (NTL-LTER).
The data compilation consists of three major steps. First, the data were retrieved from different data sources. Then the data from different sources were pooled together. No well was monitored by more than one entities so none of the wells’ records were merged.  Third, two rounds of quality assurance and quality control (QAQC) were conducted.
All the wells in the confined aquifers are not in this dataset.
The values of the USGS and Central Sands data are the depth to the water while LTER values are the actually mean sea levels of the groundwater levels. These data could not be directly compared with each other.
The project that complies the data is funded by Wisconsin Groundwater Joint Solicitation
DOI: 10.7910/dvn/tehrtn/cmk2bn
title: 41-1_0086.dcm
DOI: 10.7910/dvn/tehrtn/k3ay7a
title: 41-5_0223.dcm
DOI: 10.7910/dvn/tehrtn/mn66hu
title: 41-5_0129.dcm
DOI: 10.7910/dvn/tehrtn/mot4wb
title: 41-1_0096.dcm
DOI: 10.7910/dvn/tehrtn/vhnwt8
title: 41-5_0182.dcm
DOI: 10.7910/dvn/tehrtn/wc2aqu
title: 41-1_0068.dcm
DOI: 10.7910/dvn/tehrtn/ytlzgp
title: 41-5_0094.dcm
DOI: 10.7910/dvn/tehrtn/ztflkc
title: 41-1_0194.dcm
DOI: 10.7910/dvn/tehrtn/zvzv8i
title: 41-1_0182.dcm
DOI: 10.7910/dvn/tehrtn/18r9sa
title: 41-5_0026.dcm
DOI: 10.7910/dvn/tehrtn/2jdeq6
title: 41-1_0120.dcm
DOI: 10.7910/dvn/tehrtn/7jesxk
title: 41-1_0166.dcm
DOI: 10.7910/dvn/tehrtn/hlhqrt
title: 41-5_0084.dcm
DOI: 10.7910/dvn/tehrtn/lg8vyz
title: 41-5_0061.dcm
DOI: 10.7910/dvn/tehrtn/mb8z2m
title: 41-5_0069.dcm
DOI: 10.7910/dvn/tehrtn/nbehzm
title: 41-5_0181.dcm
DOI: 10.7910/dvn/tehrtn/y8ugki
title: 41-5_0180.dcm
DOI: 10.17632/5x7x6jwhjt.1
title: Data for: Inflammation and CB2 signaling drive novel changes in the ocular lipidome and regulate immune cell activity in the eye

description: Ocular lipidomics of 26 lipids in 6 treatment groups. Data presented as numeric means, SEM
DOI: 10.21227/284c-p879
title: Detecting Damaged Buildings onPost-Hurricane Satellite Imagery Based onCustomized Convolutional Neural Networks

description: After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms. From the building coordinates, we extract their aerial-view windows of appropriate size and classify whether a building is damaged or not. We demonstrate the result of our method in the case study of 2017 Hurricane Harvey.
DOI: 10.25919/5bd92beef236a
title: Myalup AEM interpretation and target selection: Project data

description: A collection of GIS data for the Myalup AEM interpretation and target selection report, this repository contains all shape files and grids that were used for the interpretation of the depth and extent of the Leederville Formation, as well as the grids used to select preferred targets for exploratory drilling.