cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9jjykwd
title: An exploratory Bayesian network for estimating the magnitudes and uncertainties of selected water-quality parameters at streamgage 03374100 White River at Hazleton, Indiana, from partially observed data

description: This U.S. Geological Survey (USGS) data release contains the data used in the USGS Scientific Investigations Report 2018-5053 entitled "An exploratory Bayesian network for estimating the magnitudes and uncertainties of selected water-quality parameters at streamgage 03374100 White River at Hazleton, Indiana, from partially observed data." The four datasets, which contain only ASCII characters in a column-oriented format, are: (1) sel_qw_parm_full_time_series.csv: A comma-delimited file containing an irregular time series of 713 rows of discrete water-quality measurements that start on February 21, 1973 and end on September 14, 2016. (2) baye_network_initialize.cas: This tab-delimited file can be used to initialize a Bayesian network for the water quality parameters analyzed in the subject report. (3) baye_network_training.cas: This tab-delimited file was used to identify the structure (directed acyclic graph) and the conditional probability tables used for estimation in the Bayesian network. (4) baye_network_testing.cas: This tab-delimited file has the same format as the training data set, but includes 117 rows of data that were reserved for testing the accuracy of the Bayesian network. This data release supports the following publication: Holtschlag, D.J., 2018, An exploratory Bayesian network for estimating the magnitudes and uncertainties of selected water-quality parameters at streamgage 03374100 White River at Hazleton, Indiana, from partially observed data: U.S. Geological Survey Scientific Investigations Report 2018-5053, 30 p., https://doi.org/10.3133/sir20185053.
DOI: 10.7910/dvn/h93zkk/a8spai
title: standard-20180416.tar.gz

description: Contains pickled PDB files and tfrecords necessary for model testing using DB5.
DOI: 10.7910/dvn/h93zkk/uv5ghu
title: surfacelets-release.tar.gz

description: Contains source code and scripts to replicate training of reported models.
DOI: 10.7910/dvn/h93zkk/ooqdvi
title: surfacelets-release.tar.gz

description: Contains source code and scripts to replicate training of reported models.
DOI: 10.5063/f1v69gss
title: Subsistence Harvest of Salmon from Glennallen Subdistrict, Alaska, 1988-2016

description: Subsistence fishing is important for the economies and cultures of many families and communities in Alaska. Subsistence uses of wild resources exist alongside other important uses of fish and game in Alaska and are especially important for many rural families who depend on subsistence use for nutrition and traditional cultural practices. Subsistence use is defined as the noncommercial, customary harvest and processing of wild resources for food, raw materials, and other traditional uses. These customs have been a central part of the traditions of many cultural and tribal groups in Alaska for centuries. Today, subsistence and personal use fisheries are open only to Alaska residents that have been living in the state for at least one year. The Board of Fisheries (BOF) has the authority to adopt regulations governing the use of fish resources in Alaska. In order to regulate subsistence fisheries and ensure sustainability, the BOF is directed by Alaskan state law to identify fish stocks that are customarily and traditionally used for subsistence in Alaska. After customary and traditional use has been determined for a stock, the amount reasonably necessary for subsistence is set. Subsistence and personal use fisheries are managed under different regulations and thus regulations vary by fishery and location across the state. For example, only some fisheries require a permit issued by ADFG and some waters are closed to subsistence fishing, other fisheries have season, gear, and/or bag limit restrictions. Finfish other than salmon, rainbow trout, and steelhead trout may be taken for subsistence purposes at any time in any area of the state by any method unless restricted by the subsistence fishing regulations in the Alaska Admin Code. Salmon are more strictly regulated and the fisheries require permits, only allow certain gear types, and are restricted to certain waters in the state. This dataset includes information on subsistence salmon harvest from 1988-2016 in the Glennallen Subdistrict of Alaska. This dataset includes information on the amount of harvest by community and year, additionally broken down by species and the type of gear used. The csv file "Glennallen_Harvest.csv" has been reformatted from the original file ("Glennallen District By Gear and Permit - All Years.csv"). This processing was done using the R Markdown file, "Glennallen_Subsistence.Rmd", which moves the file from wide to long format, and creates some simple plots.
DOI: 10.5066/p9fel6ms
title: Groundwater-Level Altitudes and Long-term Groundwater-Level Changes in the Chicot, Evangeline, and Jasper Aquifers, Houston-Galveston Region, Texas, 2018

description: The U.S. Geological Survey (USGS) in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, and Lone Star Groundwater Conservation District has produced this dataset of groundwater-level altitudes and groundwater-level altitude changes in the Chicot, Evangeline, and Jasper aquifers in the Houston-Galveston region, Texas. This dataset shows current-year (2018) groundwater-level altitudes for each aquifer, 5-year (2013-18) groundwater-level changes for each aquifer, long-term (1990-2018 and 1977-2018) groundwater-level changes for the Chicot and Evangeline aquifers, and long-term (2000-2018) groundwater-level change for the Jasper aquifer. The groundwater-level measurements from which the dataset were built are stored in the USGS National Water Information System (NWIS) (http://dx.doi.org/10.5066/F7P55KJN) and are accessible through the NWIS Web Interface. In 2018, groundwater-level-altitude contours for the Chicot aquifer ranged from 200 feet (ft) below the North American Vertical Datum of 1988 to 200 ft above datum. Groundwater-level-altitude contours for the Evangeline aquifer ranged from 250 ft below datum to 200 ft above datum. Groundwater-level-altitude contours for the Jasper aquifer ranged from 200 ft below datum to 200 ft above datum. In each aquifer, the highest and lowest groundwater-level altitudes were measured in relatively small, isolated areas. This dataset was created by the U.S. Geological Survey to provide resource managers, public officials, researchers, and the general public with ready access to accurate, impartial, scientific information regarding groundwater-level changes and altitudes in Harris, Galveston, Fort Bend, Montgomery, Brazoria, Chambers, Grimes, Liberty, San Jacinto, Walker, and Waller Counties, Texas. These digital data accompany Shah, S.D., Ramage, J.K, and Braun, C.L., 2018, Status of Groundwater-Level Altitudes and Long-term Groundwater-Level Changes in the Chicot, Evangeline, and Jasper Aquifers, Houston-Galveston Region, Texas, 2018: U.S. Geological Survey Scientific Investigations Report 2018-XXXX, xx p., https://doi.org/10.xxxx/sir2018xxxx.
DOI: 10.5066/p9wqj5eh
title: 2015 high resolution seismic acquisition at Dos Palmas Preserve, Mecca, California

description: In March 2015, the U.S. Geological Survey acquired seismic reflection and refraction data along an approximately 2.8-km-long profile across northwest-trending San Andreas Fault splays located at the Dos Palmas Preserve east of Salton Sea. To acquire the reflection and refraction data, we collocated shots and geophones, spaced every 3 m along the profile. We used 933 SercelTM L40A P-wave (40-Hz vertical-component) geophones with a sensitivity of 22.34 volts/meter/second to record 925 P-wave shots. We generated P-wave data using one of two active sources: 400-grain Betsy-SeisgunTM shots at approximately every 90 m and a 3.5-kg sledgehammer and steel plate combination at every 3 m between the seisgun shots. All data were recorded using four 60-channel Geometrics Stratavisor NX-60TM seismographs with a 24-bit analog-to-digital converter (Subcommittee of the SEG Engineering and Groundwater Geophysics Committee, 1990); the seismographs were connected to the P-wave geophones via refraction cables. Each shot was recorded for two seconds, with data recording starting 100 ms before the actual time of the shot. Data were recorded at a sampling rate of 0.5 ms, or 2000 samples per second. This report provides the metadata needed to utilize the seismic data. Reference: Subcommittee of the SEG Engineering and Groundwater Geophysics Committee, Pullan, S. E., Chairman, 1990, Recommended standard for seismic (/radar) data files in the personal computer environment: Geophysics, vol. 55, no. 9, p. 1260-1271.
DOI: 10.7910/dvn/7yc8ub
title: Empowering Future Farmers School Programme

description: This dataset was moved to: https://doi.org/10.34725/DVN/7YC8UBEmpowering Future Farmers School Programme
DOI: 10.7910/dvn/7yc8ub/4colwm
title: ICRAF_foodtrees_baselinesurvey__LaikipiaFeedTheChildrenICRAF.7z

description: Questionnaire to Children
DOI: 10.7910/dvn/7yc8ub/bvqhby
title: HHBaselineSurveyFruitingAfrica_LaikipiaFeedTheChildrenICRAF.7z

description: Questionnaire to Parents
DOI: 10.7910/dvn/7yc8ub/o3fpym
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.7910/dvn/bksoxc
title: Food Trees Project FGD Data in Arua, Uganda

description: This dataset was moved to: https://doi.org/10.34725/DVN/BKSOXCFood Trees FGD Data in Arua, Uganda
DOI: 10.7910/dvn/bksoxc/ojxiug
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.7910/dvn/bksoxc/xmlaah
title: FoodTreesAruaUgandaFGDData.7z

description: Food Trees FGD Data in Uganda
DOI: 10.7910/dvn/btb1ds/5sfzwp
title: Socio-economic_FoodProduction_FoodTreeDiversityRichness_RawData.7z

description: Raw Data for socioeconomic data, food production data, food trees diversity and richness data
DOI: 10.7910/dvn/btb1ds/hr1qei
title: FoodTreesData.7z

description: Food Trees Datasets from Laikipia, Tharaka-Nithi, Kitui and Kwale
DOI: 10.7910/dvn/btb1ds/kk6txj
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.7910/dvn/btb1ds/mgjpa4
title: 0.Disclaimer.pdf

description: :unav
DOI: 10.7910/dvn/btb1ds/vnzwhl
title: FoodTreesMatrixLaikipiaTharakaKitui.xlsx

description: Food Trees Species Matrix in Laikipia, Tharaka-Nithi and Kitui Counties
DOI: 10.7910/dvn/i0eiir/ozschb
title: FoodTreesProjectMapLaikipiaTharakaKituiFinal.pdf

description: Food Trees Project Study Map: Laikipia, Kitui, Tharaka Nithi
DOI: 10.7910/dvn/i0eiir/qnc5zz
title: FoodTreesMapFinal.png

description: Food Trees Final Map for Project Sites in Kenya
DOI: 10.7910/dvn/i0eiir/uftirm
title: 0.Disclaimer.pdf

description: :unav
DOI: 10.7910/dvn/qpaj8i
title: Food Trees project Map in Arua, Uganda

description: This dataset was moved to: https://doi.org/10.34725/DVN/QPAJ8IFood Trees Project Map for Arua, Uganda
DOI: 10.7910/dvn/qpaj8i/ffbxga
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.7910/dvn/qpaj8i/i6ingm
title: arua_map.html

description: Food Trees project Map for Sites in Uganda
DOI: 10.7910/dvn/r6ag7o
title: Food Trees Nutrition Data Scooping Study in Uganda

description: This dataset was moved to: https://doi.org/10.34725/DVN/R6AG7OFood Trees Nutrition Data Scooping Study in Arua, Uganda
DOI: 10.7910/dvn/r6ag7o/ertiil
title: FoodTreeBaselineNutritionConsumptionUGANDA.7z

description: Nutrition Scooping Study Data in Uganda for the Food Trees Project
DOI: 10.7910/dvn/r6ag7o/yjncdi
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.7910/dvn/vwijng/71trhq
title: NutritionConsumptionRawData.7z

description: Raw Data for the Food Trees Project Nutrition and Consumption Data for Laikipia County, Tharaka-Nithi County, Kitui County
DOI: 10.7910/dvn/vwijng/jmmcdm
title: 0.Disclaimer.pdf

description: :unav
DOI: 10.7910/dvn/vwijng/km8gfv
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.7910/dvn/vwijng/njioqv
title: NutritionFoodTrees.7z

description: Food Trees All Data: Laikipia, Tharaka-nithi, Kitui and Kwale
DOI: 10.7910/dvn/xifzie/dk5lad
title: FoodTreesProject_Documentation.Rmd

description: Update R Script for Data Documentation of the Food Trees Project Data: data cleaning, transformation and analysis and visualization
DOI: 10.7910/dvn/xifzie/vkcndm
title: FoodTreesProjectFarmProductionDataDocumentation.R

description: R Script for the Food Trees Project Socioeconomic, farm production and food tree species diversity and richness: Preliminary data cleaning, development of indicators and data analysis: Laikipia County, Tharaka-Nithi County and Kitui County
DOI: 10.7910/dvn/xifzie/vxclhv
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.7910/dvn/xifzie/x4ha0t
title: 0.Disclaimer.pdf

description: :unav
DOI: 10.7910/dvn/3qzuzi/6sodtx
title: Informal Fish Trade Research Handbook.docx

description: Informal Fish Trade Research Handbook
DOI: 10.7910/dvn/3qzuzi/fkrrxu
title: Corridor Analysis Methodology Guidance Notes (14.04.2015).docx

description: :unav
DOI: 10.7910/dvn/3qzuzi/fmddx7
title: Determinants of Fish Trade Flows in Africa.docx

description: Determinants of Fish Trade Flows in Africa
DOI: 10.7910/dvn/3qzuzi/ksztrx
title: CodeBook - Determinants of Fish Trade Flows in Africa.tab

description: :unav
DOI: 10.7910/dvn/3qzuzi/s1sial
title: Determinants of Fish Trade Flows in Africa.xlsx

description: :unav
DOI: 10.7910/dvn/3qzuzi/tuu1ow
title: An investigation of women participation in fish value chains in Malawi.doc

description: :unav
DOI: 10.7910/dvn/3qzuzi/vl8gon
title: FishTrade VC Questionnaire_ FINAL edited.docx

description: :unav
DOI: 10.5281/zenodo.1341601
title: Webis Text Reuse Corpus 2012

description: The Webis Text Reuse Corpus 2012 (Webis-TRC-12) compiles manually written documents obtained from a completely controlled, yet representative environment that emulates the web. Each document in the corpus is about one of the 150 topics used at the TREC Web Tracks 2009–2011, thus forming a strong connection with existing evaluation efforts. Writers, hired at the crowdsourcing platform oDesk, had to retrieve sources for a given topic and to reuse text from what they found. Part of the corpus are detailed interaction logs that consistently cover the search for sources as well as the creation of documents. This will allow for in-depth analyses of how text is composed if a writer is at liberty to reuse texts from a third party.
description: {"references": ["Potthast, M., Hagen, M., V\u00f6lske, M., and Stein, B. (2013). Crowdsourcing Interaction Logs to Understand Text Reuse from the Web. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 13), P. Fung, and M. Poesio, eds. (Association for Computational Linguistics), pp. 1212\u20131221.", "Potthast, M., Hagen, M., V\u00f6lske, M., and Stein, B. (2013). Exploratory Search Missions for TREC Topics. In 3rd European Workshop on Human-Computer Interaction and Information Retrieval (EuroHCIR 2013), M.L. Wilson, T. Russell-Rose, B. Larsen, P. Hansen, and K. Norling, eds. (CEUR-WS.org), pp. 11\u201314.", "Hagen, M., Potthast, M., V\u00f6lske, M., Gomoll, J., and Stein, B. (2016). How Writers Search: Analyzing the Search and Writing Logs of Non-fictional Essays. In Proceedings of the 1st ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 16), D. Kelly, R. Capra, N. Belkin, J. Teevan, and P. Vakkari, eds. (ACM), pp. 193\u2013202."]}
DOI: 10.5281/zenodo.1341602
title: Webis Text Reuse Corpus 2012

description: The Webis Text Reuse Corpus 2012 (Webis-TRC-12) compiles manually written documents obtained from a completely controlled, yet representative environment that emulates the web. Each document in the corpus is about one of the 150 topics used at the TREC Web Tracks 2009–2011, thus forming a strong connection with existing evaluation efforts. Writers, hired at the crowdsourcing platform oDesk, had to retrieve sources for a given topic and to reuse text from what they found. Part of the corpus are detailed interaction logs that consistently cover the search for sources as well as the creation of documents. This will allow for in-depth analyses of how text is composed if a writer is at liberty to reuse texts from a third party.
description: {"references": ["Potthast, M., Hagen, M., V\u00f6lske, M., and Stein, B. (2013). Crowdsourcing Interaction Logs to Understand Text Reuse from the Web. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 13), P. Fung, and M. Poesio, eds. (Association for Computational Linguistics), pp. 1212\u20131221.", "Potthast, M., Hagen, M., V\u00f6lske, M., and Stein, B. (2013). Exploratory Search Missions for TREC Topics. In 3rd European Workshop on Human-Computer Interaction and Information Retrieval (EuroHCIR 2013), M.L. Wilson, T. Russell-Rose, B. Larsen, P. Hansen, and K. Norling, eds. (CEUR-WS.org), pp. 11\u201314.", "Hagen, M., Potthast, M., V\u00f6lske, M., Gomoll, J., and Stein, B. (2016). How Writers Search: Analyzing the Search and Writing Logs of Non-fictional Essays. In Proceedings of the 1st ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 16), D. Kelly, R. Capra, N. Belkin, J. Teevan, and P. Vakkari, eds. (ACM), pp. 193\u2013202."]}
DOI: 10.5281/zenodo.1321879
title: Continuous rotation electron diffraction data for Zeolite Mordenite

description: <strong>Raw continuous rotation electron diffraction data for mordenite:</strong><br> <br> - mordenite_cRED_1.zip<br> - mordenite_cRED_2.zip The zip file contains 3 directories<br> <br> - SMV: Diffraction data (stretch correction applied) in SMV format<br> - Tiff: Raw diffraction data in 16-bit unsigned integer TIFF format<br> - Defocused images in 16-bit unsigned integer TIFF format Experimental parameters are stored in the header files of the SMV images, and in the file cRED_log.txt<br> The SMV data can be processed using XDS. <br> <strong>Raw serial electron diffraction data sets for mordenite:</strong> - mordenite_SerialED.zip The zip file contains at least 3 directories: - calib: contains the calibration files for the experiment<br> - data: contains the raw diffraction data for all the identified crystals in hdf5 format<br> - images: contains the image data used to locate crystals in hdf5 format Experimental parameters (such as the crystal coordinates) are stored in the attributes on the data files. The Python code to process the data can be found in the problematic-0.1.0.zip folder or on http://github.com/stefsmeets/problematic<br> Prediction scores for all diffraction patterns are given in `learning.csv`<br>
DOI: 10.5281/zenodo.1321880
title: Continuous rotation electron diffraction data for Zeolite Mordenite

description: <strong>Raw continuous rotation electron diffraction data for mordenite:</strong><br> <br> - mordenite_cRED_1.zip<br> - mordenite_cRED_2.zip The zip file contains 3 directories<br> <br> - SMV: Diffraction data (stretch correction applied) in SMV format<br> - Tiff: Raw diffraction data in 16-bit unsigned integer TIFF format<br> - Defocused images in 16-bit unsigned integer TIFF format Experimental parameters are stored in the header files of the SMV images, and in the file cRED_log.txt<br> The SMV data can be processed using XDS. <br> <strong>Raw serial electron diffraction data sets for mordenite:</strong> - mordenite_SerialED.zip The zip file contains at least 3 directories: - calib: contains the calibration files for the experiment<br> - data: contains the raw diffraction data for all the identified crystals in hdf5 format<br> - images: contains the image data used to locate crystals in hdf5 format Experimental parameters (such as the crystal coordinates) are stored in the attributes on the data files. The Python code to process the data can be found in the problematic-0.1.0.zip folder or on http://github.com/stefsmeets/problematic<br> Prediction scores for all diffraction patterns are given in `learning.csv`<br>
DOI: 10.7278/s5n29v4f
title: Supplementary data to Tree carbon allocation explains forest drought-kill and recovery patterns
DOI: 10.5066/p9rdpwxo
title: Dataset of trace dissolved hydrocarbons in surface water and groundwater in North Dakota, Pennsylvania, Virginia, and West Virginia between 2014 and 2017

description: This dataset contains measurements of dissolved hydrocarbons in various water sources, as well as ancillary raw calibration data showing the stability of the gas chromatograph with an atomic emission detector and flame ionization detector (GC-AED-FID) analytical system over time. Across multiple studies, samples from tap water, groundwater, surface water, springs, mine outflows, and blank materials were analyzed using this system over a period from 2014 to 2017, comprising 172 samples analyzed. In addition to water samples, 183 calibrations conducted over the same period of time are included to document the stability of the GC-AED-FID system over time. The target analytes in this study were: methane (CH4), ethane (C2H6), ethene (C2H4), ethyne (C2H2), propane (C3H8), propene (C3H6), i-butane (C4H10), n-butane (C4H10), 1-butene (C4H8), propyne (C3H4), i-pentane (C5H12), n-pentane (C5H12), 2-methyl-pentane (C6H14), 3-methyl-pentane (C6H14), hexane (C6H14), benzene (C6H6).
DOI: 10.21223/p3/wwesei
title: Replication Data for: Baseline survey report of orange-fleshed sweet potato knowledge, farming and consumption, and dietary practices among households with children aged 6-59 months in selected districts of Morogoro, Iringa and Mbeya regions of Tanzania

description: Orange-fleshed sweetpotato (OFSP) as a staple food besides providing necessary body energy in human diet provides significant amounts of Vitamin A addressing both Vitamin A deficiency (VAD) and body energy needs. The Viable Sweetpotato Technologies in Africa (VISTA) – Tanzania project was designed to expand the production and utilization of nutritious OFSP into seven districts in Mbeya, Iringa and Morogoro regions which were part of USAID’s Feed the Future (FTF) zones of influence (ZOI). As part of initial project activities, a baseline survey was implemented to provide a better understanding of the prevailing OFSP knowledge, farming and consumption and dietary practices of caregivers of households with children aged between 6 and 59 months in the project intervention districts. This provided up-to-date data on key indicators to better understand the context and to be able to use these as a basis for progress monitoring over the three year project period.