cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3368882
title: Collection of global datasets for the study of floods, droughts and their interactions with human societies

description: This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to Lindersson et al. (2020) for further information about review methodology. The collection is a descriptive list, holding the following information for each dataset: Category<em> - as structured in Lindersson et al. (2020).</em> Sub-category<em>- as structured in Lindersson et al. (2020).</em> Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em> Title <em>- full title of dataset.</em> Product(s)<em> - type of product(s) offered by the dataset.</em> Period<em> - time period covered by the dataset, not defined for all datasets.</em> Temporal resolution<em> - not defined for static datasets.</em> Angular spatial resolution<em> - only defined for gridded datasets.</em> Metric spatial resolution <em>- only defined for gridded datasets.</em> Map scale Extent<em> - geographic coverage of dataset given in latitude limits.</em> Description Creating institute(s) Data type<em> - raster, vector or tabular.</em> File format Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em> Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em> Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em> Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em> Citation<em> - dataset reference or credit.</em> Documentation <em>- dataset documentation.</em> Web address<em> - dataset access link.</em> NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.
DOI: 10.7910/dvn/qqza5e/2aqpfq
title: J2348+4900_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/7uhzdd
title: Gl166C_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/a1pyq8
title: J2357+3837_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/d4esps
title: J2356+2305N_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/kvaqrs
title: J0600+0242_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/nwq9hx
title: J2343+3632_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/pexqis
title: J2351+2344_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/vfofqu
title: J0617+0507_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/yjeelb
title: LSPMJ0528+1231_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/yjz4mr
title: J0039+1454S_tc.fits

description: :unav
DOI: 10.25919/5d5758140be45
title: Parkes observations for project P958 semester 2019APRS_70

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until a few months ago, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 28 bursts, two of which have been localised to host galaxies at redshifts &gt; 0.3. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. Over the next six months we expect to detect another 12 FRBs, which will also have sub-arcsecond localisations. Here we propose to search this population for repeats. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections, will either confirm the presence of additional repeating FRBs or the uniqueness of FRB 121102 amongst the population.
DOI: 10.5281/zenodo.3370102
title: Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence

description: This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication: Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3.
DOI: 10.7910/dvn/qqza5e/0bpg0r
title: J0153+0147_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/5swxaq
title: J0417+0849_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/fsphnz
title: J0347+0841_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/h7rokn
title: J0450+2207_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/h9vbwa
title: J0217+3526_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/j4icgp
title: J0501+2237_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/monw51
title: J0313+0446_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/nmwgkv
title: J0237+0021_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/zgaekh
title: J0328+2629_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/baaixi
title: J0202+1334_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/ixlyji
title: 2MASSJ17195815-0553043A.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/jhlird
title: J0015+1333_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/kbkujc
title: J0008+2050_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/nm8lv6
title: J0111+0454_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/vstrth
title: LP858-23_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/yu6v44
title: J0105+2829_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/z2i8l2
title: 2MASSJ17195815-0553043B.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/0lrvww
title: J0918+6037W_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/2eveum
title: J1225+2323_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/ddsfnm
title: J1103+2417_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/dpdhqy
title: J1531+4702_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/fcpku7
title: J1832+2030NW_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/gorlqd
title: J1833+2219_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/lhnvrs
title: J1122+3755_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/p3jex2
title: J1832+2030SE_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/qajt85
title: CE226_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/u5dzbi
title: LSPMJ1248+1204_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/85x6ay
title: J0850+5253_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/btfwpj
title: J0828+2008_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/grjh2o
title: J1709+3909_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/in9l0a
title: J0840+1824_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/jmsosc
title: J0917+5825_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/oewgzu
title: PM_I19321-1119_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/xoy8ak
title: PM_I16277-0104_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/ywv44g
title: Gl806.1B_tc.fits

description: :unav
DOI: 10.18739/a2qj77z28
title: Compilation of Calanus glacialis and Calanus hyperboreus abundance data, Arctic Ocean, 1934-2016

description: This dataset contains Calanus glacialis and Calanus hyperboreus abundance data compiled January 2017-September 2018 for the study Kvile KØ, Ashjian C and Ji R. 2019: Pan-Arctic depth distribution of diapausing Calanus copepods. Biological Bulletin 237, DOI: 10.1086/704694. Data span from 1934 to 2016 and cover the entire Arctic Ocean and surrounding subarctic seas. See DataSources_Kvile2019_BiolBull.xlsx for a full overview of data sources. Specifically, the dataset includes stage-specific abundance data for Calanus glacialis and Calanus hyperboreus copepodid stages (C1-C5) and adults (C6 male or female) given as ind. m-3 (counts per cubic meter) or ind. m-2 (counts per square meter). Data of merged stages (e.g. C1-C3) or of other units (biomass, presence/absence etc.) were not included. Data missing information about sampling date, location (longitude, latitude), depth (upper and lower depth of sample), gear or mesh size were not included. Some data are depth-integrated (surface-bottom), while other are for specific depth layers with multiple layers per station.
DOI: 10.7910/dvn/qqza5e/6jdlgg
title: J1337+4808NS_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/adbcmo
title: J1208+2147N_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/hq7jnx
title: J1048+1909_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/il4e4k
title: HD46375B_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/kzhjeo
title: J0919+3831N_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/ohfn7f
title: J1057+2217_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/oldgvj
title: J1100+2114_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/reg6ha
title: J0919+3831S_tc.fits

description: :unav
DOI: 10.7910/dvn/qqza5e/ykvuxc
title: J1031+5705_tc.fits

description: :unav
DOI: 10.7910/dvn/zsqef8/hamm7k
title: Vertigo_11-26.tab

description: :unav
DOI: 10.15121/1557419
title: Utah FORGE: Earth Model Native State Simulation Results

description: Utah FORGE phase 2C Native State Simulation zip contains the data used for the boundary conditions and subsequent native state simulation results obtained using the simulation code FALCON. Data are from the nodes of the simulation domain, with used a uniform 50m spacing over a 2500 X 2500 X 2750m domain approximately centered on the FORGE footprint. There is also a read me text file, that is included, containing metadata. The Reservoir Porosity and Upscale DFN Permeability zip contains the data used for the spatial distribution of the anisotropic permeability and porosity used in the native state simulation of the Utah FORGE site. Please contact Robert Podgorney at the Idaho National Laboratory with questions, robert.podgorney@inl.gov. There is also a read me text file included containing metadata.
DOI: 10.15468/er5taj
title: Moss checklist of Prioksko-Terrasnyi Biosphere Reserve, European Russia

description: Present checklist contains data on 156 moss species, located in the natural landscape on the territory of the core area of the Prioksko-Terrasnyi biosphere reserve (Russian Federation, South of Moscow region) 4960 ha. This area includes the left bank of the Oka river, on the border of the boreal and nemoral forests and dry meadows. The list is compiled based on over 10 years of observations of flora and actual for the period 1980-1990.
DOI: 10.5286/isis.e.rb1920633
title: Deformation mechanisms in several simple alloys to underpin creep damage characterisation

description: The proposal is to study three alloys, both at high temperature and room temperature, to corroborate strain results obtained through high resolution digital image correlation. The collected data will provide information on the grain scale mechanical properties which will then be used in expanding and developing creep models.
DOI: 10.5286/isis.e.rb1920633-1
title: Deformation mechanisms in several simple alloys to underpin creep damage characterisation

description: The proposal is to study three alloys, both at high temperature and room temperature, to corroborate strain results obtained through high resolution digital image correlation. The collected data will provide information on the grain scale mechanical properties which will then be used in expanding and developing creep models.
DOI: 10.7910/dvn/dx7mwy/fsye6r
title: 20190817data set for HIV-related stigma.tab

description: Table 1 Variables in HIV-related stigma Variables in Model Variables in data set of this study Name Definition Dependent Variable Y1 Fear 1 =Higher Stigma, 0 =Lower Stigma Y2 Moral 1 =Higher Stigma, 0 =Lower Stigma Y3 Personal 1 =Higher Stigma, 0 =Lower Stigma Y4 Community 1 =Higher Stigma, 0 =Lower Stigma C1-C24 Item 1=correct,0=incorrect Independent Variable X1 Gender 0=Female, 1=Male X2 Residential areas 0=Rural,1=Urban X3 Major 0=Non-medical, 1=Medical X4 Grade 0=Non-freshmen, 1= Freshmen) X5 knowledge 0=Low, 1=High X6 Risky sexual behavior 0=No, 1=Yes X7 Having ever being tested 0=No, 1=Yes X8 Willingness to utilize HTC service 0=No, 1=Yes X9 Awareness of AIDS policy 0=No, 1=Yes
DOI: 10.5281/zenodo.3369715
title: BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report

description: <em>A Metabolomics unknown feature identification report industry standards from BioNovoGene corporation.</em> 2019.08.16# at Suzhou, China There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way. This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification. Some terms in this article that address to: feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result. annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc. alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification. score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is. Our metabolite identification report consist with two parts of data which present to our user: Report excel table that contains the raw sample information and the meta annotation information of the metabolite. Data visual plot for the mass spectrum alignment details.
description: A Metabolomics unknown feature identification report industry standards from BioNovoGene corporation.
description: {"references": ["MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition. DOI: 10.1021/acs.analchem.6b02122", "KAAS: an automatic genome annotation and pathway reconstruction server. DOI: 10.1093/nar/gkm321"]}
DOI: 10.5281/zenodo.3369716
title: BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report

description: <em>A Metabolomics unknown feature identification report industry standards from BioNovoGene corporation.</em> 2019.08.16# at Suzhou, China There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way. This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification. Some terms in this article that address to: feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result. annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc. alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification. score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is. Our metabolite identification report consist with two parts of data which present to our user: Report excel table that contains the raw sample information and the meta annotation information of the metabolite. Data visual plot for the mass spectrum alignment details.
description: A Metabolomics unknown feature identification report industry standards from BioNovoGene corporation.
description: {"references": ["MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition. DOI: 10.1021/acs.analchem.6b02122", "KAAS: an automatic genome annotation and pathway reconstruction server. DOI: 10.1093/nar/gkm321"]}
DOI: 10.7910/dvn/ioobmk/b8ra6g
title: party_voter_experiment_psrm.tab

description: Dataset
DOI: 10.7910/dvn/ioobmk/ppvuiv
title: party_voter_experiment_psrm.do

description: Stata do-file
DOI: 10.25919/5d580862282d3
title: Parkes observations for project P958 semester 2019APRS_71

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until a few months ago, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 28 bursts, two of which have been localised to host galaxies at redshifts &gt; 0.3. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. Over the next six months we expect to detect another 12 FRBs, which will also have sub-arcsecond localisations. Here we propose to search this population for repeats. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections, will either confirm the presence of additional repeating FRBs or the uniqueness of FRB 121102 amongst the population.
DOI: 10.7910/dvn/mrfpe3/q5fb86
title: regret_sub_sample_puch.do

description: Secondary do file
DOI: 10.7910/dvn/mrfpe3/vle4j8
title: regret_puch.do

description: Main do-file
DOI: 10.7910/dvn/mrfpe3/xxmwvw
title: medw_canada_2015_withresults.tab

description: Dataset
DOI: 10.5281/zenodo.3370215
title: Reactive Transport in Heterogeneous Porous Media Under Different Peclet Numbers

description: In our recent paper “Reactive Transport in Heterogeneous Porous Media Under Different Peclet Numbers“, we study the synergistic effects of the Peclet number and the length scale of medium heterogeneity on the evolution of bimolecular reactive transport between mobile and immobile species. We performed a suite of numerical simulations at the Darcy scale that quantify the instantaneous, irreversible bimolecular reaction , under various transport conditions (Peclet numbers) and porous media configurations (correlation lengths). <strong>Numerical Methods</strong> We used the following steps to obtain the results: 1. Generation of the hydraulic conductivity fields – we used a widely tested sequential Gaussian simulator (Gómez-Hernández & Journel, 1993). The software is written in C and available to download via https://wiki.52north.org/AI_GEOSTATS/SWGCOSIM3D. In the file ‘real20.mat’, we show an example of the generated 20 realizations, from the software, for \(\ell\)=1 ,\(\sigma^2\)=1. 2. Determination of the flow field – we solve the Darcy equation (for each of the hydraulic conductivity fields), using an open-source code MRST (Lie, 2016). The software is available to download via https://www.sintef.no/projectweb/mrst/. In the file ‘real20_solutions.mat’, we show the flow field solutions (for the different realizations from ‘real20.mat’) under different Peclet numbers. For each realization and Peclet, we store the solution of the velocity components (first column), the velocity magnitude (second column) and the hydraulic conductivity field (third column). 3. Chemical transport – was modeled through the Langevin equation, where movement by advection and diffusion were taken into account. Chemical reaction between the different chemical species was modeled via the reaction-radius approach (Edery, Porta, Guadagnini, Scher, & Berkowitz, 2016). <br> <strong>Results</strong> The data of the published figures can be download from the following files: Fig2.txt -- processed simulation results shown in Figure 2. Fig3.txt -- processed simulation results shown in Figure 3a,b. Fig4.txt -- processed simulation results shown in Figure 4. Fig5.txt -- processed simulation results shown in Figure 5. <strong>References</strong> [1] Edery, Y., Porta, G. M., Guadagnini, A., Scher, H., & Berkowitz, B. (2016). Characterization of Bimolecular Reactive Transport in Heterogeneous Porous Media. Transport in Porous Media. https://doi.org/10.1007/s11242-016-0684-0 [2] Gómez-Hernández, J. J., & Journel, A. G. (1993). Joint Sequential Simulation of MultiGaussian Fields. https://doi.org/10.1007/978-94-011-1739-5_8 [3] Lie, K.-A. (2016). User Guide for the MATLAB Reservoir Simulation Toolbox (MRST). In A. Soares (Ed.), An Introduction to Reservoir Simulation Using MATLAB. Oslo, Norway: SINTEF ICT, Department of Applied Mathematics.