cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/7fa76918712fff9f4f1dfff23d0e0c9e
title: Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing

description: This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
DOI: 10.7910/dvn/fnvnna/dalz1f
title: VOICE Data Dictionary for upload Aug 2019.xlsx

description: Data dictionary for Project VOICE
DOI: 10.7910/dvn/yr4eqh/1gyeng
title: conjointanalysis.tab

description: Experimental data for skin color and vote buying (source: original conjoint)
DOI: 10.7910/dvn/yr4eqh/azyyvc
title: vbpaper_2019version.tab

description: Observational data: skin color and vote buying (source: LAPOP)
DOI: 10.7910/dvn/yr4eqh/gxvm0n
title: electoraldiscrimination_analysis.do

description: Do file for all analyses included in the paper and supplementary information
DOI: 10.7910/dvn/yr4eqh/ow0k6a
title: conjointdescriptives_wide.tab

description: For descriptives of conjoint sample and voter profiles (source: original conjoint)
DOI: 10.7910/dvn/yr4eqh/woes4j
title: partysystems_DALP.tab

description: Data to plot ethnic patronage by party system (source: DALP)
DOI: 10.7910/dvn/yr4eqh/xcnmjl
title: LAPOP_cleaningfile.do

description: Data cleaning and merging to construct the "vbpaper_2019version" data file from the raw LAPOP data.
DOI: 10.7910/dvn/yr4eqh/uq0we3
title: Explanatory File for Analysis Replication.pdf

description: Explanatory file for replicating the analysis in the main manuscript and supplementary information
DOI: 10.17632/ftxhzgjsvy.1
title: Data for: Radiocarbon and geologic evidence reveal Ilopango volcano as source of the colossal 'mystery' eruption of 539/40 CE

description: AMS 14C data from three subfossil trees from El Salvador. These trees come from two sampling sites - El Mico and Los Chorros.
DOI: 10.5281/zenodo.3369446
title: DWD European Weather 2

description: opendata.dwd.de - OpenData by Deutscher Wetter Dienst Conditions: https://www.dwd.de/EN/service/copyright/copyright_node.html dates and times are UTC.
DOI: 10.18430/m36op1
title: X-ray diffraction data for the selenium incorporated, carbon monoxide inhibited FeMo-cofactor of azotobacter vinelandii (6OP1)
DOI: 10.18430/m36op2
title: X-ray diffraction data for the selenium incorporated FeMo-cofactor of nitrogenase from azotobacter vinelandii at high concentration of selenium (6OP2)
DOI: 10.21227/4eaq-hc60
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.18430/m36op3
title: X-ray diffraction data for the selenium incorporated FeMo-cofactor of nitrogenase from Azotobacter vinelandii with low concentration of selenium (6OP3)
DOI: 10.18430/m36op4
title: X-ray diffraction data for the selenium-incorporated, carbon monoxide-inhibited, reactivated FeMo-cofactor of nitrogenase from Azotobacter vinelandii (6OP4)
DOI: 10.5281/zenodo.3362524
title: A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003–2018

description: In this project, "A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003–2018", we built a model to predict the mean, maximum, and minimum temperature on each day at each square in a 1-km grid for an area around Mexico City. See the README at https://github.com/justlab/Mexico_temperature/blob/master/README.rst for more information and instructions.
DOI: 10.7910/dvn/ycsfsc/codzpb
title: Morse_FATF_AllCrim.tab

description: :unav
DOI: 10.7910/dvn/ycsfsc/lh0ese
title: Morse_GPI_FATF_Appendix.R

description: :unav
DOI: 10.7910/dvn/ycsfsc/r2jxb8
title: Morse_FATF_banklending.tab

description: :unav
DOI: 10.7910/dvn/ycsfsc/u1zidv
title: Morse_FATF_DescriptivePlot

description: :unav
DOI: 10.7910/dvn/ycsfsc/zad1n9
title: Morse_GPI_FATF_PaperCode.R

description: :unav
DOI: 10.14272/awgpupyyxpmwaf-mdjpiyfqsa-l.1
title: C52H40N4O10Zn
title: InChI=1S/C52H42N4O10.Zn/c1-61-41-23-31(24-42(62-2)49(41)65-5)47-37-19-15-33(53-37)45(27-7-11-29(12-8-27)51(57)58)35-17-21-39(55-35)48(32-25-43(63-3)50(66-6)44(26-32)64-4)40-22-18-36(56-40)46(34-16-20-38(47)54-34)28-9-13-30(14-10-28)52(59)60;/h7-26H,1-6H3,(H4,53,54,55,56,57,58,59,60);/q;+2/p-2/b45-33-,45-35-,46-34-,46-36-,47-37-,47-38-,48-39-,48-40-;

description: This dataset contains experimental data from a sample (InChI=1S/C52H42N4O10.Zn/c1-61-41-23-31(24-42(62-2)49(41)65-5)47-37-19-15-33(53-37)45(27-7-11-29(12-8-27)51(57)58)35-17-21-39(55-35)48(32-25-43(63-3)50(66-6)44(26-32)64-4)40-22-18-36(56-40)46(34-16-20-38(47)54-34)28-9-13-30(14-10-28)52(59)60;/h7-26H,1-6H3,(H4,53,54,55,56,57,58,59,60);/q;+2/p-2/b45-33-,45-35-,46-34-,46-36-,47-37-,47-38-,48-39-,48-40-;). The datasets from the following experiments are available: mass spectrometry (MS)
DOI: 10.14272/awgpupyyxpmwaf-mdjpiyfqsa-l/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C52H42N4O10.Zn/c1-61-41-23-31(24-42(62-2)49(41)65-5)47-37-19-15-33(53-37)45(27-7-11-29(12-8-27)51(57)58)35-17-21-39(55-35)48(32-25-43(63-3)50(66-6)44(26-32)64-4)40-22-18-36(56-40)46(34-16-20-38(47)54-34)28-9-13-30(14-10-28)52(59)60;/h7-26H,1-6H3,(H4,53,54,55,56,57,58,59,60);/q;+2/p-2/b45-33-,45-35-,46-34-,46-36-,47-37-,47-38-,48-39-,48-40-;

description: dataset for mass spectrometry (MS) HRMS (C52H40N4O10Zn): calcd 944.2036, found 944.2016.
DOI: 10.25387/g3.8309594.v2
title: Supplemental Material for Rotelli et al., 2019

description: Table S1. A list of the shRNA strains screened for growth of Drosophila wing tissue.<br>
DOI: 10.7910/dvn/igijgg/6rislg
title: WP Replication_El Salvador_3.tab

description: Survey Data El Salvador
DOI: 10.7910/dvn/igijgg/niuoow
title: WP Replication_El Salvador_4.tab

description: Survey Data- El Salvador
DOI: 10.7910/dvn/igijgg/zqenmp
title: WP Replication_El Salvador_2.tab

description: Sub-national El Salvador
DOI: 10.7910/dvn/ycsfsc/kesmgr
title: Morse_Thailand_BankLiabilities.tab

description: :unav
DOI: 10.7910/dvn/ycsfsc/xgb16j
title: Morse_FATF_banklendingplacebo.tab

description: :unav
DOI: 10.7910/dvn/ycsfsc/xjsl7q
title: Morse_FATF_CrimTF.tab

description: :unav
DOI: 10.7910/dvn/igijgg/dh9luv
title: CWSP DatasetV1.tab

description: Cross-National Civil War Successor Party dataset
DOI: 10.7910/dvn/igijgg/vlfu0y
title: Voting for Victors_DO.do

description: Do File
DOI: 10.5066/p9lupb9n
title: Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2015 habitat change, East Chenier Region

description: The Barrier Island Comprehensive Monitoring (BICM) program was developed by Louisiana's Coastal Protection and Restoration Authority (CPRA) and is implemented as a component of the System Wide Assessment and Monitoring Program (SWAMP). The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic elevations, single-beam and swath bathymetry, and sediment grab samples. For more information about the BICM program, see Kindinger and others (2013). The U.S. Geological Survey, Wetland and Aquatic Research Center provides support to the BICM program through the development of habitat map products using aerial imagery and lidar elevation data and assessing change in habitats over time. These data provide a snapshot of barrier island habitats and can be combined with other past and/or future maps to monitor these valuable natural resources over time. The current effort of this habitat mapping program includes developing habitat maps for 2008 and 2015-2016 for the following BICM regions: 1) West Chenier; 2) East Chenier; 3) Acadiana Bays (only Marsh Island); 4) Early Lafourche Delta; 5) Late Lafourche Delta; 6) Modern Delta (only Chaland Headland and Shell Island); and 7) Chandeleur Islands. The BICM program has developed two habitat classification schemes which include a detailed 15-class habitat scheme and a general eight-class habitat scheme. The detailed scheme was used for this habitat mapping effort and builds off the general scheme used in previous BICM habitat mapping efforts (Fearnley and others, 2009). The additional classes developed in the detailed scheme are primarily used to further delineate various dune habitats, separate marsh and mangrove, and distinguish between beach and unvegetated barrier flat habitats. The habitat change analyses in this product depict and summarize habitat change between 2008 and 2015-2016 per BICM reach. The results from these analyses are summarized in two habitat change products. The first product depicts change in land and water coverage and the second product highlights changes based on inundation zones (for example, water, intertidal-unvegetated, intertidal-vegetated, supratidal, and developed/shoreline protection). This product also includes recommended symbology for these data layers. Please see the Entity and Attribute Information section in this metadata file for the BICM barrier island habitat and change class descriptions and the Processing Steps section for information on methodology for map development.
DOI: 10.21227/ev0k-8x31
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.5281/zenodo.3367643
title: Using Noninvasive Genetic Sampling to Survey Rare Butterfly Population

description: <strong>Supplementary Table 1.</strong> Sanger sequencing and NCBI BLAST results for exemplar amplicons from each treatment of the proof-of concept study. From: Using Noninvasive Genetic Sampling to Survey Rare Butterfly Populations
DOI: 10.5281/zenodo.3367644
title: Using Noninvasive Genetic Sampling to Survey Rare Butterfly Population

description: <strong>Supplementary Table 1.</strong> Sanger sequencing and NCBI BLAST results for exemplar amplicons from each treatment of the proof-of concept study. From: Using Noninvasive Genetic Sampling to Survey Rare Butterfly Populations
DOI: 10.5281/zenodo.3369194
title: UNFCCC Emissions data from the Detailed Data By Party interface (Download from 2019-08-01)

description: updated download, includes 2017 data fixes a bug with year selection
DOI: 10.21227/z13j-qx67
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.17632/sjgr3w28n7.1
title: Discrete Choice Experiment data for street-level urban greening in Berlin

description: The data presented here are the outcome of a survey implemented in a Berlin neighborhood from January to March 2018. To begin to put a monetary value on street-level aspects of urban green, we used a discrete choice experiment to explore people’s preferences and willingness to pay for green features in an urban Neighborhood Management development zone in Berlin. An analysis was performed on the survey results using conditional logit and latent class logit models done in R. This dataset is complementary to the full research article, “Economic valuation of street-level urban greening: A case study from an evolving mixed-use area in Berlin” (Fruth et al., 2019). The analysis provided in the full article can be used to guide decision-making regarding urban greening and green building practices at both policy- and project-level, while the dataset available here can be used to provide insight about how our sample population responded and how the experiment could be replicated in context or elsewhere in Berlin. “Deutsch_potsdamer_covariates” = IP address and user comments have been removed to protect respondents’ confidentiality; used in our sociodemographic analysis “Deutsch_potsdamer_experiment_15” = raw choice data from the DCE portion of the Geman-language survey; used in the logistic regression analysis to establish WTP “English_potsdamer_covariates” = IP address and user comments have been removed to protect respondents’ confidentiality; used in our sociodemographic analysis “English_postdamer_experiment_15” = raw choice data from the DCE portion of the English-language survey; used in the logistic regression analysis to establish WTP “SB_O02-03-00_2013j05_BE” = household income data; source file for the Berlin (parent population) income data points in our sociodemographic analysis “table_2019-07-27_11-28-18” = age/gender/population figures from StatIS-BBB; source file for the Berlin (parent population) age/gender/population data points in our sociodemographic analysis
DOI: 10.6084/m9.figshare.21918828
title: Workflow Dataset

description: This folder contains data collected from Galaxy for our reusablity challenges analysis.
DOI: 10.34683/sr2393
title: StoneRounds Case: SR2393
title: AD Retinitis Pigmentosa

description: StoneRounds Case SR2393 was diagnosed with AD Retinitis Pigmentosa likely caused by mutations in the gene: RHO.
DOI: 10.17632/8hdnzv23x7.1
title: SARS-CoV-2-RBV (1-3)

description: Only individuals over the age of 18 were included in the study.  There are no individual characteristics (name-surname, identification number, etc.) that define the patients in our data set. Routine blood values (RBV) include various biochemical, hematological and immunological parameter properties.

The dataset contains 3 files.
File 1: SARS-CoV-2-RBV1.sav (IBM SPSS Statistics format).
First SARS-CoV-2-RBV1 data set includes the information of 2648 patients diagnosed with COVID-19 and receiving outpatient treatment in hospital on the specified dates, and the same number of patients (control group) whose COVID-19 tests were negative.
First column: Diagnosis.
In this data set, positive COVID-19 test results were coded as 1 and negative as 0 (COVID-19 = 1, non-COVID-19 = 0). This data set includes 51 RBV features. The first dataset can be used to detect COVID-19.

File 2: SARS-CoV-2-RBV2.sav (IBM SPSS Statistics format).
This dataset contains information on n = 203 severely infected (ICU) COVID-19 patients and n = 3696 mildly infected (non-ICU) COVID-19 patients. First column: ICU or non-ICU. Severely COVID-19 patients were coded as 1, while mildly COVID-19 patients were coded as 0. This dataset  includes 51  RBV features. This dataset can be used for the prognosis/severity of COVID-19.

File 3: SARS-CoV-2-RBV3.sav (IBM SPSS Statistics format). 
SARS-CoV-2-RBV3 dataset includes the information of 233 patients who died in the hospital from COVID-19 and 2364 patients living in the hospital on the specified dates. First column: Contains the patient's output information (survived or non-survived). Patients who died from COVID-19 were coded as 1, and surviving from COVID-19 were coded as 0. This dataset includes 38 RBV features. This dataset can be used for mortality/severity of COVID-19.

We grant you a non-exclusive, non-transferable, revocable license to use the SARS-CoV-2-RBV dataset solely for your non-commercial, educational, and research purposes only, but without any right to copy or reproduce, publish or otherwise make available to the public or communicate to the public, sell, rent or lend the whole or any constituent part of the dataset thereof.

Please cite the following four articles when you use any of these datasets:
1) Huyut, M.T.; Velichko, A. Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network. Sensors 2022, 22. 
2) Huyut, M.T.; Velichko, A.; Belyaev, M. Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers. Appl. Sci. 2022, 12, 12180. 
3) Velichko, A.; Huyut, M.T.; Belyaev, M.; Izotov, Y.; Korzun, D. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application. Sensors 2022, 22, 7886. 
4) Huyut, M.T. Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models. IRBM 2022, 1, 1–12.
DOI: 10.17632/yj4d6x2f7d.1
title: Data for "Labeled loans and human capital investments"

description: Codes and database originated for the manuscript published in the DOI article: 10.1016/j.jdeveco.2023.103053
DOI: 10.6084/m9.figshare.21918849
title: RAW DATA.xlsx

description: results of in vitro and in vivo experimentation using a rodent model of klebsiella pneumoniae infection and MSC therapy
DOI: 10.6084/m9.figshare.21918849.v1
title: RAW DATA.xlsx

description: results of in vitro and in vivo experimentation using a rodent model of klebsiella pneumoniae infection and MSC therapy