cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a23t9d74t
title: Modèle Atmosphérique Régional (MAR) version 3.11 regional climate model output, 1979-2019, Greenland domain, 10 kilometer (km) horizontal resolution

description: Modèle Atmosphérique Régional (MAR) is a regional climate model that is fully coupled to a one-dimensional surface-atmosphere energy and mass transfer scheme, SISVAT (Soil Ice Snow Vegetation Atmosphere Transfer) (Fettweis et al., 2005, 2020; Lefebre et al., 2005). SISVAT employs a multilayered snowpack model, CROCUS, that simulates meltwater production, percolation, and refreeze (Brun et al., 1989), while also accounting for changes in albedo due to snow metamorphism (Brun et al., 1992). MAR has been extensively verified over the Greenland Ice Sheet and is therefore particularly well suited for analyses of Greenland ice sheet surface mass balance (Fettweis et al., 2011; Fettweis et al., 2020; Lefebre et al. 2005; Mattingly et al. 2020). Brun, E., Martin, E., Simon, V., Gendre, C., and Coléou, C. (1989). An energy and mass model of snow cover suitable for operational avalanche forecasting. Journal of Glaciology, 35, 333. https://doi.org/10.1017/S0022143000009254 Brun, E., David, P., Sudul, M., and Brunot, G. (1992). A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. Journal of Glaciology, 38(128), 13–22. https://doi.org/10.3189/S0022143000009552 Fettweis, X., Gallée, H., Lefebre, F., and van Ypersele, J.-P. (2005). Greenland surface mass balance simulated by a regional climate model and comparison with satellite-derived data in 1990–1991. Climate Dynamics, 24(6), 623–640. https://doi.org/10.1007/s00382-005-0010-y Fettweis, X., Tedesco, M., van den Broeke, M., and Ettema, J. (2011). Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models. The Cryosphere, 5(2), 359–375. https://doi.org/10.5194/tc-5-359-2011 Fettweis, X., Hofer, S., Krebs-Kanzow, U., Amory, C., Aoki, T., Berends, C. J., et al. (2020). GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet. The Cryosphere, 14(11), 3935–3958. https://doi.org/10.5194/tc-14-3935-2020 Lefebre, F., Fettweis, X., Gallée, H., Van Ypersele, J.-P., Marbaix, P., Greuell, W., and Calanca, P. (2005). Evaluation of a high-resolution regional climate simulation over Greenland. Climate Dynamics, 25(1), 99–116. https://doi.org/10.1007/s00382-005-0005-8 Mattingly, K. S., Mote, T. L., Fettweis, X., van As, D., Van Tricht, K., Lhermitte, S., et al. (2020). Strong summer atmospheric rivers trigger Greenland ice sheet melt through spatially varying surface energy balance and cloud regimes. Journal of Climate, 33(16), 6809–6832. https://doi.org/10.1175/JCLI-D-19-0835.1
DOI: 10.6073/pasta/0f8d9403365e5200cfe21b0fc015033b
title: PBG08 Grasshopper density survey in the Patch-Burn Grazing experiment at Konza Prairie

description: PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on measuring grasshopper density using ring count method (Onsager 1977*) at watersheds C03A, C03B, C03C, C01A, C3SA, C3SB, C3SC, and C01B. Grazing intensity is estimated (Joern 2005) at each site at time of density measurements to model how grasshopper populations respond to grazing.
DOI: 10.6084/m9.figshare.19083407
title: All DATA.xlsx

description: 168 orthopaedic residents, comprising 92 orthopaedic residents in Thailand who answered the questionnaire and 76 in Myanmar who answered the questionnaire, were included in this data file.<br>
DOI: 10.6084/m9.figshare.19083407.v1
title: All DATA.xlsx

description: 168 orthopaedic residents, comprising 92 orthopaedic residents in Thailand who answered the questionnaire and 76 in Myanmar who answered the questionnaire, were included in this data file.<br>
DOI: 10.5519/qd.c98n4evi
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (104 records)
DOI: 10.5519/qd.lfe0v3xp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (41 records)
DOI: 10.5066/f7j964gw
title: USMIN Mineral Resource Data for the U.S. Geological Survey Sagebrush Mineral-Resource Assessment Project

description: The point and polygon layers within this geodatabase represent locations of mineral occurrences, mines, mining and mineral districts and sites of active mineral exploration within or near the Department of the Interior (DOI) Sagebrush Focal Areas in Montana, Wyoming and Utah, central Idaho, and the Oregon-Nevada-Idaho border area. The data were compiled by the U.S. Geological Survey (USGS) Mineral Deposit Database project (USMIN) to provide mineral resource information for use in the USGS Sagebrush Mineral Resource Assessment (SaMiRA). This assessment was conducted for the Bureau of Land Management (BLM) and evaluated the mineral resource potential of approximately 10 million acres of Federal lands identified as areas of high-quality sagebrush habitat. The spatial extent of the USMIN mineral resource data includes BLM lands proposed for withdrawal from mineral entry as well as a 25 km buffer beyond the Public Land Survey System (PLSS) townships containing these areas. This extent allowed for a thorough examination of the data and assured that any significant mineral occurrence, mine, or exploration area within or adjacent to BLMs proposed withdrawal areas was considered in the mineral resource assessment. The mineral resource data were compiled as GIS layers including: 1) mine symbols shown on USGS topographic maps; 2) mine sites; 3) active mineral exploration sites; 4) mineral occurrences; 5) mining and mineral districts; and 6) production and resource data for mines and mineral deposits. A full discussion of the compilation methodology and sources used to develop the mineral resource data is available in the section USMIN Project Mineral-Resource Data for the USGS SaMiRA Project in the accompanying report: Day, W.C., Hammarstrom, J.M., Zientek, M.L., and Frost, T.P., eds., 2016 [in prep.], Overview with methods and procedures of the U.S. Geological Survey mineral-resource assessment of the Sagebrush Focal Areas of Idaho, Montana, Nevada, Oregon, Utah, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2016XXXXA, XX p., http://dx.doi.org/10.3133/sir2016XXXXA.
DOI: 10.3886/e114742v1-107962
title: Replication data for: The Effect of Fast Food Restaurants on Obesity and Weight Gain

description: We investigate how changes in the supply of fast food restaurants affect weight outcomes of 3 million children and 3 million pregnant women. Among ninth graders, a fast food restaurant within 0.1 miles of a school results in a 5.2 percent increase in obesity rates. Among pregnant women, a fast-food restaurant within 0.5 miles of residence results in a 1.6 percent increase in the probability of gaining over 20 kilos. The implied effects on caloric intake are one order of magnitude larger for children than for mothers, consistent with smaller travel cost for adults. Non-fast food restaurants and future fast-food restaurants are uncorrelated with weight outcomes. (JEL I12, J13, J16, L83)
DOI: 10.5066/f73r0qzc
title: Water and Air Temperature Throughout the Range of Rio Grande Cutthroat Trout in Colorado and New Mexico; ; 2010-2015 V2

description: This data set characterizes the thermal regime in a number of Colorado and New Mexico streams that contain populations of Rio Grande Cutthroat Trout (Oncorhynchus clarkii virginalis) or have been considered potential restoration areas for the fish. The majority of these streams had no previous record of continual temperature records. When compared to Colorado water temperature criteria (Cold Tier 1), a portion of these populations appeared to be at risk from elevated stream temperatures, as indicated by exceedance of both acute and chronic water quality metrics. Summer water temperature profiles recorded at sites within current Rio Grande Cutthroat Trout habitat indicated that although the majority of currently occupied conservation streams have temperatures that fall well below these biologically based acute and chronic thermal thresholds, several sites may be at or approaching water temperatures considered stressful to Cutthroat Trout.
DOI: 10.5281/zenodo.5934048
title: Data from Schaffter, S.W. and Strychalski, E.A. "Co-transcriptionally encoded RNA strand displacement circuits" Science Advances (2022)

description: The <strong>ctRSD_data_upload.xlsx</strong> file contains both the raw and normalized fluorescence data from this manuscript. In the Excel file, rows highlighted in green indicate the times when T7 RNAP was added to the samples. Plots of data in the manuscript designate this time as time = 0 min. The <strong>figure2D_fluorescence_data_plotting.py</strong> file is an example showing how the data was imported from the Excel file and plotted in the manuscript. In the script, the save_loc variable will need to be changed to the path pointing to where the ctRSD_data_upload.xlsx file (Data S1) is saved on a user’s local computer.
DOI: 10.5281/zenodo.5934047
title: Data from Schaffter, S.W. and Strychalski, E.A. "Co-transcriptionally encoded RNA strand displacement circuits" Science Advances (2022)

description: The <strong>ctRSD_data_upload.xlsx</strong> file contains both the raw and normalized fluorescence data from this manuscript. In the Excel file, rows highlighted in green indicate the times when T7 RNAP was added to the samples. Plots of data in the manuscript designate this time as time = 0 min. The <strong>figure2D_fluorescence_data_plotting.py</strong> file is an example showing how the data was imported from the Excel file and plotted in the manuscript. In the script, the save_loc variable will need to be changed to the path pointing to where the ctRSD_data_upload.xlsx file (Data S1) is saved on a user’s local computer.
DOI: 10.5066/f7tb150z
title: Water and Sediment Geochemistry Data from the Vicinity of Yellow Pine, Idaho, 2014-2015

description: Samples were collected at or near baseflow conditions. Water pH and specific conductance were measured in the field, with specific conductance also measured in the laboratory and calculated based on the ionic strength of samples based on laboratory analyses. Water samples were collected for laboratory analyses using a peristaltic pump with silicon tubing. Samples were filtered through a reusable plexiglass filter holder with nylon screws using 0.45m polyethersulfone membranes that were rinsed with approximately 20mL of sample water prior to collection. Splits were also collected unfiltered to determine trace metal concentrations associated with suspended sediments. The filtration apparatus was rinsed with deionized water between samples. Separate filtration apparatuses were used for mining-impacted sites and background sites to minimize sample cross-contamination. Field equipment blanks were collected at the end of a field day using the full sampling apparatus used for mining-impacted sites to determine carryover of constituents between samples. Samples were chilled to approximately 4C and transported by overnight shipping to USGS analytical laboratories. Mercury and methylmercury were analyzed in the USGS Mercury Laboratory in Middleton, WI. Note that mercury concentrations in the field blanks exceeded concentrations in sites considered to be background. Sources of sample contamination may be the filter, which was not pre-cleaned, and the filtration apparatus that was previously used in stream reaches directly impacted by mercury mining. The detection limit for mercury was &lt;0.04 ng/L. Field blanks had 1.96 and 2.45 ng/L Hg. Streambed sediment samples were collected as grab samples and stored in pre-cleaned glass jars, frozen on dry ice to minimize microbial activity associated with the formation and degradation of methylmercury. Sediment samples and shipped to USGS laboratories in Denver, CO for total elemental analysis, including mercury, and to Middleton, WI for methylmercury analysis. Sediment was dominantly fine to coarse sand trapped between cobbles in a high-energy stream environment. Samples were sieved to less than 2mm and hand-crushed to homogenize prior to trace metal analysis in Denver.
DOI: 10.5519/qd.lojo91m0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5066/p9ikg7rt
title: X-Ray diffraction data for bulk sediment and clay separations taken from cores from Bristol Dry Lake, California and geothermal springs from Paoha Island (Mono Lake), California

description: X-Ray diffraction (XRD) data for bulk sediment and clay separations taken from cores from Bristol Dry Lake, California and geothermal springs from Paoha Island (Mono Lake), California. The data were collected for two separate projects: (1) determining the source of lithium in Bristol Dry Lake (BDL), and (2) determining the mechanism of sinter formation on Paoha Island, Mono Lake. The data from BDL were collected in collaboration with Standard Lithium Ltd. who drilled 6 new proprietary wells in 2017 and 2018 on the playa. The wells were not cored, but cuttings were available for sampling a few months after the drilling was completed. The wells were up to 1500 feet deep. Cuttings were kept in the dark in sealed plastic bags in a storage container on site at ambient temperatures. The Paoha Island samples were part of a larger study conducted by the National Research Program of the Water Mission Area to look at the role of microbes in arsenic utilization and growth. The current substudy was to look at how microbes may or may not contribute to sinter growth and preservation at the geothermal springs where the microbes live.
DOI: 10.17608/k6.auckland.19093607
title: Chapter 3 Supplementary tables

description: This repository contains the supplementary tables for Chapter 3: Network-based identification of pleiotropic genes for Parkinson's disease and Type II Diabetes<br>Supplementary Table 1: GWAS SNP sets used for analyses - T2D and PDSupplementary Table 2: PD GWAS: Sig. eQTLS CoDeS3D output - CNS tissues onlySupplementary Table 3: PD GWAS: Sig. eQTLS CoDeS3D output - All non-CNS tissuesSupplementary Table 4: T2D GWAS: Sig. eQTLS CoDeS3D output - CNS tissues onlySupplementary Table 5: T2D GWAS: Sig. eQTLS CoDeS3D output - All non-CNS tissuesSupplementary Table 6: gProfiler pathway analysis of all PD genes - non-CNS tissuesSupplementary Table 7: gProfiler pathway analysis of all T2D genes - non-CNS tissuesSupplementary Table 8: gProfiler pathway combined analysis - CNS tissues onlySupplementary Table 9: gProfiler pathway combined analysis - All non-CNS tissuesSupplementary Table 10: T2D and PD eQTLs converge on 23 shared target genes in CNS tissuesSupplementary Table 11: Literature search of 23 pleiotropic target genes in brainSupplementary Table 12: T2D and PD eQTLs converge on 96 pleiotropic target genes in non-CNS tissuesSupplementary Table 13: gProfiler pathway analysis of the 96 pleiotropic target genesSupplementary Table 14: Literature search of 96 pleiotropic target genes in non-CNS tissuesSupplementary Table 15: DGIdb druggability analysis of the 96 non-CNS pleiotropic target genes<br><br>
DOI: 10.17608/k6.auckland.19093607.v1
title: Chapter 3 Supplementary tables

description: This repository contains the supplementary tables for Chapter 3: Network-based identification of pleiotropic genes for Parkinson's disease and Type II Diabetes<br>Supplementary Table 1: GWAS SNP sets used for analyses - T2D and PDSupplementary Table 2: PD GWAS: Sig. eQTLS CoDeS3D output - CNS tissues onlySupplementary Table 3: PD GWAS: Sig. eQTLS CoDeS3D output - All non-CNS tissuesSupplementary Table 4: T2D GWAS: Sig. eQTLS CoDeS3D output - CNS tissues onlySupplementary Table 5: T2D GWAS: Sig. eQTLS CoDeS3D output - All non-CNS tissuesSupplementary Table 6: gProfiler pathway analysis of all PD genes - non-CNS tissuesSupplementary Table 7: gProfiler pathway analysis of all T2D genes - non-CNS tissuesSupplementary Table 8: gProfiler pathway combined analysis - CNS tissues onlySupplementary Table 9: gProfiler pathway combined analysis - All non-CNS tissuesSupplementary Table 10: T2D and PD eQTLs converge on 23 shared target genes in CNS tissuesSupplementary Table 11: Literature search of 23 pleiotropic target genes in brainSupplementary Table 12: T2D and PD eQTLs converge on 96 pleiotropic target genes in non-CNS tissuesSupplementary Table 13: gProfiler pathway analysis of the 96 pleiotropic target genesSupplementary Table 14: Literature search of 96 pleiotropic target genes in non-CNS tissuesSupplementary Table 15: DGIdb druggability analysis of the 96 non-CNS pleiotropic target genes<br><br>
DOI: 10.6084/m9.figshare.16752733
title: The Arabidopsis gene co-expression network

description: Supplementary FilesModule-Gene annotationsRaw network file
DOI: 10.6084/m9.figshare.16752733.v3
title: The Arabidopsis gene co-expression network

description: Supplementary FilesModule-Gene annotationsProcessed network
DOI: 10.25505/fiz.icsd.cc20xz97
title: CSD 1874889: Experimental Crystal Structure Determination

description: Related Article: R. Vishnupriya, C. Selva Meenatchi, J. Suresh, R.V. Sumesh, R.R. Kumar, P.L.N. Lakshman|2018|Acta Crystallogr.,Sect.E:Cryst.Commun.|74|660|doi:10.1107/S2056989018005455
description: Related Article: P. Fabrykiewicz, R. Przeniosło, I. Sosnowska, F. Fauth|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|660|doi:10.1107/S2052520618014968
DOI: 10.5517/ccdc.csd.cc211gtd
title: CSD 1878253: Experimental Crystal Structure Determination

description: Related Article: Kelly M. Powderly, Shu Guo, Karoline Stolze, Elizabeth M. Carnicom, R.J. Cava|2019|J.Solid State Chem.|272|113|doi:10.1016/j.jssc.2019.02.008
DOI: 10.25505/fiz.icsd.cc213bp6
title: CSD 1880047: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213bq7
title: CSD 1880048: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213br8
title: CSD 1880049: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213bs9
title: CSD 1880050: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213btb
title: CSD 1880051: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213bvc
title: CSD 1880052: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213bxf
title: CSD 1880054: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213byg
title: CSD 1880055: Experimental Crystal Structure Determination

description: Related Article: O. Siidra, M.S. Kozin, W. Depmeier, R.A. Kayukov, V.M. Kovrugin|2018|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|74|712|doi:10.1107/S2052520618016542
DOI: 10.25505/fiz.icsd.cc213z16
title: CSD 1880647: Experimental Crystal Structure Determination

description: Related Article: Lingyun Zhang, Dajiang Mei, Yuanwang Wu, Chenfei Shen, Wenxin Hu, Lujia Zhang, Jinjin Li, Yuandong Wu, Xiao He|2019|J.Solid State Chem.|272|69|doi:10.1016/j.jssc.2019.01.024
DOI: 10.25505/fiz.icsd.cc213z27
title: CSD 1880648: Experimental Crystal Structure Determination

description: Related Article: Lingyun Zhang, Dajiang Mei, Yuanwang Wu, Chenfei Shen, Wenxin Hu, Lujia Zhang, Jinjin Li, Yuandong Wu, Xiao He|2019|J.Solid State Chem.|272|69|doi:10.1016/j.jssc.2019.01.024
DOI: 10.25505/fiz.icsd.cc213z38
title: CSD 1880649: Experimental Crystal Structure Determination

description: Related Article: Lingyun Zhang, Dajiang Mei, Yuanwang Wu, Chenfei Shen, Wenxin Hu, Lujia Zhang, Jinjin Li, Yuandong Wu, Xiao He|2019|J.Solid State Chem.|272|69|doi:10.1016/j.jssc.2019.01.024
DOI: 10.25505/fiz.icsd.cc213z49
title: CSD 1880650: Experimental Crystal Structure Determination

description: Related Article: Lingyun Zhang, Dajiang Mei, Yuanwang Wu, Chenfei Shen, Wenxin Hu, Lujia Zhang, Jinjin Li, Yuandong Wu, Xiao He|2019|J.Solid State Chem.|272|69|doi:10.1016/j.jssc.2019.01.024
DOI: 10.25505/fiz.icsd.cc214ltm
title: CSD 1881260: Experimental Crystal Structure Determination

description: Related Article: M. Sonni, M.F. Zid, C. Issaoui|2019|J.Solid State Chem.|272|244|doi:10.1016/j.jssc.2019.02.009
DOI: 10.17608/k6.auckland.19093595
title: Chapter 5 Supplementary tables

description: This repository contains the supplementary tables for chapter 5: Mapping enhancer activity for loci marked by Parkinson’s disease variants using a Massively Parallel Reporter Assay<br>Supplementary table 5.1: MPRA variants that have already been tested in previous MPRA studies<br>Supplementary table 5.2: Additional GWAS SNPs included in the assay, an extension from table 5.1 <br>Supplementary table 5.3: All candidate regulatory elements included in the synthesised library (Agilent technologies)<br>Supplementary table 5.4: Sequencing results of the inert library to determine the barcode/oligo pairings<br>Supplementary table 5.5: Deepsea Expecto variant prediction results for PD-associated SNPs included in the MPRA. Described fully in section 5.3.3.2