cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34725/dvn/ytjtzq/mm2w30
title: Manure Ref Data.tab

description: :unav
DOI: 10.34725/dvn/ytjtzq/lzextv
title: pXRF Manure Standards Ref data Jul 2018.xlsx

description: :unav
DOI: 10.34725/dvn/ytjtzq/eafhqx
title: ICRAF pXRF Manure Stds on MPA instrument.tab

description: :unav
DOI: 10.34725/dvn/ytjtzq/1zcpqa
title: FTIRManure.quant

description: Spectra are saved in this files in RDS format and they work with open-source code repositories (CloudCal and cloudFTIR, both open github projects). The CloudFTIR can be found at: https://github.com/leedrake5/cloudFTIR."
DOI: 10.34725/dvn/ytjtzq/why00x
title: manureFTIRKBRFull2019_Carbon.tiff

description: Comparison of actual and predicted total carbon (C) content of organic amendment (OA) samples based on whole spectrum forest regressions for DRIFT-MIR spectra from the Bruker Alpha KBr instrument at ICRAF. The dotted line indicates the expected 1:1 ratio forestimates and known values. The bottom graphs show the respective response variables in the spectra.
DOI: 10.34725/dvn/ytjtzq/zpekk1
title: xrf4473XGBoostLight.quant

description: Extreme gradient boosting machine learning (XGBoost) models were used with data from portable XRF instrument serial number 900F4473 for the Manure Light Calibration (10 kV, 70 μA and 90 seconds, No filter. Elemental Range: Na, Mg, Al, Si, P, S, K, Ca, Ti, Cr, Mn, and Fe. (ii) Manure Trace: 35 kV, 35). For example, trees can be built from randomly selected columns (Energies for pXRF spectra) and rows (standards). XGBoost (0.82.1) models were run using 400 rounds with a variable tree depth ranging from 5 to 25. Learning rates (eta) were constrained to values between 0.1 and 0.3, with gamma regularization ranging from 0 to 0.1. The minimal child weight (controls the model complexity) was limited to 1. Unique combinations of these variables were run over 32 iterations with kfold using caret (6.0–82) and the best model was selected using root mean square error (RMSE). Calibrations were created using CloudCal (v3) Spectra are saved in this file in RDS format and they work with open-source code repositories.
DOI: 10.34725/dvn/ytjtzq/wrhnos
title: mirForest.quant

description: Using forest regression machine learning models, excellent calibration functions could be established using the MIR spectra. Forest models for there DRIFT-MIR spectra were run using 1500 trees with 200 resampling events using k-fold cross validation over 25 iterations based on the R package randomForest (4.6–14). The best forest models were selected using root mean square error (RMSE) in the caret package (6.0–84) using the R language (3.6.0). Spectra are saved in this files in RDS format and they work with open-source code repositories.
DOI: 10.34725/dvn/ytjtzq/py7avb
title: xrf4473ForestLight.quant

description: Forest models for pXRF spectra from the instrument serial number 900F4473 Light Calibration (10 kV, 70 μA and 90 seconds, No filter. Elemental Range: Na, Mg, Al, Si, P, S, K, Ca, Ti, Cr, Mn, and Fe) were run using 1500 trees with 200 resampling events using k-fold cross validation over 25 iterations based on the R package randomForest (4.6–14). The best forest models were selected using root mean square error (RMSE) in the caret package (6.0–84) using the R language (3.6.0).
DOI: 10.34725/dvn/ytjtzq/70osiq
title: mirXGBoost.quant

description: Extreme gradient boosting machine learning (XGBoost) models were used with data from DRIFT-MIR spectroscopy and these differ from forest in that trees can be weighted differently, have fixed depths, and different resembling. XGBoost (0.82.1) models were run using 400 rounds with a variable tree depth ranging from 5 to 25. Learning rates (eta) were constrained to values between 0.1 and 0.3, with gamma regularization ranging from 0 to 0.1. The minimal child weight (controls the model complexity) was limited to 1. Unique combinations of these variables were run over 32 iterations with kfold using caret (6.0–82) and the best model was selected using root mean square error (RMSE). Calibrations were created using CloudCal (v3) Spectra are saved in this files in RDS format and they work with open-source code repositories.
DOI: 10.34725/dvn/ytjtzq/cespwo
title: xrf4473XGBoostTrace.quant

description: Extreme gradient boosting machine learning (XGBoost) models were used with data from portable XRF instrument serial number 900F4473 for the Manure Trace Calibration (35 kV, 35 μA and 90 seconds, Filter- Cu 75um:Ti 25um:Al 200um. Elemental Range: K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, and P). For example, trees can be built from randomly selected columns (Energies for pXRF spectra) and rows (standards). XGBoost (0.82.1) models were run using 400 rounds with a variable tree depth ranging from 5 to 25. Learning rates (eta) were constrained to values between 0.1 and 0.3, with gamma regularization ranging from 0 to 0.1. The minimal child weight (controls the model complexity) was limited to 1. Unique combinations of these variables were run over 32 iterations with kfold using caret (6.0–82) and the best model was selected using root mean square error (RMSE). Calibrations were created using CloudCal (v3) Spectra are saved in this file in RDS format and they work with open-source code repositories.
DOI: 10.34725/dvn/ytjtzq/qh5rxb
title: Averaged MIR spectra of the manure standards run on the MPA instrument.tab

description: Averaged MIR spectra of the manure standards run on the MPA instrument
DOI: 10.34725/dvn/ytjtzq/ugnd4q
title: Averaged MIR spectra of the manure standards run on the HTS-XT instrument.tab

description: Averaged MIR spectra of the manure standards run on the HTS-XT instrument
DOI: 10.34725/dvn/ytjtzq/na4rre
title: Manure Standards Reference Data.tab

description: Manure standards reference data
DOI: 10.34725/dvn/ytjtzq/29egod
title: Variables Description.tab

description: Variables description file
DOI: 10.34725/dvn/ytjtzq/kuiok4
title: MIR spectra of manure stds Alpha KBr 2018_spectra.tab

description: MIR spectra of the manure standards using Alpha KBr instrument
DOI: 10.34725/dvn/ytjtzq/vqzgsh
title: MIR spectra of manure stds Alpha ZnSe 2018_spectra.tab

description: MIR spectra of the manure standards using Alpha ZnSe instrument
DOI: 10.34725/dvn/ytjtzq
title: Replication Data for: Comprehensive Nutrient Analysis in Agricultural Organic Amendments Through Non-Destructive Assays Using Machine Learning

description: Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning methods, forest regression and extreme gradient boosting, were used with data from both pXRF and DRIFT-MIR spectroscopy. Cross-validation trials were run to evaluate generalizability of models produced on each instrument. Both methods demonstrated similar broad capabilities in estimating nutrients using machine learning, with pXRF being suitable for nutrients and contaminants. The results make portable spectrometry in combination with machine learning a scalable solution to provide comprehensive nutrient analysis for organic amendments.
DOI: 10.6084/m9.figshare.14813151.v1
title: UNAIDS economics and modelling methods in NSPs review data

description: Tools, data and results from chapter 3 of the report 'A review of country-level HIV planning and resource allocation tools and processes: the development of National Strategic Plans and their links to Global Fund applications' by Paul Revill, Sakshi Mohan, Simon Walker, Jeff Eaton, Cindy Carlson, Gemma Oberth, Fern Terris-Prestholt, Iris Semini.
DOI: 10.6084/m9.figshare.14813151
title: UNAIDS economics and modelling methods in NSPs review data

description: Tools, data and results from chapter 3 of the report 'A review of country-level HIV planning and resource allocation tools and processes: the development of National Strategic Plans and their links to Global Fund applications' by Paul Revill, Sakshi Mohan, Simon Walker, Jeff Eaton, Cindy Carlson, Gemma Oberth, Fern Terris-Prestholt, Iris Semini.
DOI: 10.6084/m9.figshare.14813154
title: PLUMEX current data

description: Current data measured by a shipboard Teledyne RDI Ocean Surveyor and a Mariner Workhorse Acoustic Doppler Current Profiler sensors (ADCP) aboard R/V Sally Ride during the discharge and tracking of the CCFZ sediment plume.<br>
DOI: 10.6084/m9.figshare.14813085
title: Phased Array Doppler Sonar (PADS) data

description: Raw Phased Array Doppler Sonar backscatter intensity data (Figure 2).<br>
DOI: 10.6084/m9.figshare.14813166
title: Phased Array Doppler Sonar data

description: Phased Array Doppler Sonar datasets of Saltwater 1, Saltwater 2 and CCZF sediment dynamic plume discharges.<br>
DOI: 10.5281/zenodo.5000318
title: What are the characteristics of highly-used packages? A case study on the npm ecosystem

description: With the popularity of software ecosystems, the number of open source components (a.k.a. “packages”) has been growing rapidly. Identifying high-quality and well-maintained packages from a large pool of packages to depend on is a basic and important problem, as it is beneficial for various applications, such as package recommendation, package search, etc. However, there is no systematic and comprehensive work so far that focuses on addressing this problem except in online discussions or in informal literature and interviews. To fill this gap, in this paper, we conduct a mixed qualitative and quantitative analysis to understand how developers identify and select relevant open source packages. In particular, we start by surveying 118 JavaScript developers from the npm ecosystem to qualitatively understand the factors that make a package to be highly-used within the ecosystem. The survey results show that JavaScript developers believe that highly-used packages are well-documented, receive a high number of stars on GitHub, have a large number of downloads, and do not suffer from vulnerabilities. Then, we conduct an experiment to quantitatively validate the developers' perception of the factors that make a highly-used package. In this analysis, we collect and mine historical data from 2,427 packages divided into highly-used and low-used packages. For each package in the dataset, we collect quantitative data to present the factors studied in the developers' survey. Next, we use regression analysis to quantitatively explain which of the studied factors are the most important. Our regression analysis support developers' believe about highly-used packages. In particular, the results show that highly-used packages tend to be impacted by the number of downloads, stars, and how larger is readme file of the package.
DOI: 10.6084/m9.figshare.14813139.v1
title: CCFZ daily average current data

description: 6-year time series of daily average current velocity and heading measured at depths between 1193 m and 1,427 m in the Korean exploration licensed area in the CCFZ (10º 30’N 131º 20’W). The maximum observed daily average velocity was 0.11m s-1, and the mean was 0.04m s-1 over the 6-year period. The current velocity and heading were measured using a single point current meter SeaGuard RCM DW (Aanderaa)
DOI: 10.6084/m9.figshare.14813097
title: Epsilometer profile data

description: Conductivity, temperature,depth, turbulence, sediment and tracer concentration data from the epsilometer profile shown in Figure 3 and Supplementary Figure 6. Conductivity temperature and depth were measured by an SBE49 CTD sensor and used to calculate the density and buoyancy frequency. Turbulence was measured by a custom turbulence sensor developed by the MOD group at Scripps Institution of Oceanography.<br>
DOI: 10.6084/m9.figshare.14813097.v2
title: Epsilometer profile data

description: Conductivity, temperature,depth, turbulence, sediment and tracer concentration data from the epsilometer profile shown in Figure 3 and Supplementary Figure 6. Conductivity temperature and depth were measured by an SBE49 CTD sensor and used to calculate the density and buoyancy frequency. Turbulence was measured by a custom turbulence sensor developed by the MOD group at Scripps Institution of Oceanography.<br>
DOI: 10.6084/m9.figshare.14813097.v1
title: Epsilometer profile data

description: Conductivity, temperature,depth, turbulence, sediment and tracer concentration data from the epsilometer profile shown in Figure 3 and Supplementary Figure 6. Conductivity temperature and depth were measured by an SBE49 CTD sensor and used to calculate the density and buoyancy frequency. Turbulence was measured by a custom turbulence sensor developed by the MOD group at Scripps Institution of Oceanography.<br>
DOI: 10.6084/m9.figshare.14813106
title: Ambient plume temperature and salinity evolution

description: Ambient plume temperature and salinity measurements conducted during the plume tracking tow-yo. It was measured by an SBE911plus CTD.<br>
DOI: 10.6084/m9.figshare.14813106.v2
title: Ambient plume temperature and salinity evolution

description: Ambient plume temperature and salinity measurements conducted during the plume tracking tow-yo. It was measured by an SBE911plus CTD.<br>
DOI: 10.6084/m9.figshare.14813106.v1
title: Ambient plume temperature and salinity evolution

description: Ambient plume temperature and salinity measurements conducted during the plume tracking tow-yo. It was measured by an SBE911plus CTD.<br>
DOI: 10.6084/m9.figshare.14813160
title: Discharge temperature.

description: Temperature measured at the discharge nozzle during the discharge of the Saltwater 1, CCFZ sediment and Saltwater 2 plumes. Temperature was measured by an SBE56 thermistor.<br>
DOI: 10.6084/m9.figshare.14813160.v2
title: Discharge temperature.

description: Temperature measured at the discharge nozzle during the discharge of the Saltwater 1, CCFZ sediment and Saltwater 2 plumes. Temperature was measured by an SBE56 thermistor.<br>
DOI: 10.6084/m9.figshare.14813160.v1
title: Discharge temperature.

description: Temperature measured at the discharge nozzle during the discharge of the Saltwater 1, CCFZ sediment and Saltwater 2 plumes. Temperature was measured by an SBE56 thermistor.<br>
DOI: 10.6084/m9.figshare.14813148
title: CTD profiles

description: Salinity, temperature and depth profiles measured during PLUMEX field studies (Supplementary Figure 2) by an SBE 911plus CTD sensor. The calculated buoyancy frequency is also included. The profiles were measured before the release of Saltwater plume 1, CCFZ sediment plume, and Saltwater plume 2.<br>
DOI: 10.6084/m9.figshare.14813148.v1
title: CTD profiles

description: Salinity, temperature and depth profiles measured during PLUMEX field studies (Supplementary Figure 2) by an SBE 911plus CTD sensor. The calculated buoyancy frequency is also included. The profiles were measured before the release of Saltwater plume 1, CCFZ sediment plume, and Saltwater plume 2.<br>
DOI: 10.6084/m9.figshare.14813139
title: CCFZ daily average current data

description: 6-year time series of daily average current velocity and heading measured at depths between 1193 m and 1,427 m in the Korean exploration licensed area in the CCFZ (10º 30’N 131º 20’W). The maximum observed daily average velocity was 0.11m s-1, and the mean was 0.04m s-1 over the 6-year period. The current velocity and heading were measured using a single point current meter SeaGuard RCM DW (Aanderaa)
DOI: 10.6084/m9.figshare.14813139.v2
title: CCFZ daily average current data

description: 6-year time series of daily average current velocity and heading measured at depths between 1193 m and 1,427 m in the Korean exploration licensed area in the CCFZ (10º 30’N 131º 20’W). The maximum observed daily average velocity was 0.11m s-1, and the mean was 0.04m s-1 over the 6-year period. The current velocity and heading were measured using a single point current meter SeaGuard RCM DW (Aanderaa)
DOI: 10.6084/m9.figshare.14813133
title: Water samples Particle Size Distribution

description: Particle size distribution of samples shown in Figure 6. , The PSD was determined with an Accusizer780/780AD, which is based on the single particle optical sensing technique.<br>
DOI: 10.6084/m9.figshare.14813133.v1
title: Water samples Particle Size Distribution

description: Particle size distribution of samples shown in Figure 6. , The PSD was determined with an Accusizer780/780AD, which is based on the single particle optical sensing technique.<br>
DOI: 10.6084/m9.figshare.14813115
title: Ambient plume tracer and sediment concentration

description: Tracer and sediment concentration measured during the ambient plume tracking. The data was measured by a tow-yo'ed ECOTriplet Scattering Fluorescence sensor. The background stratification was measured by a tow-yo'ed SBE911plus CTD sensor.<br>
DOI: 10.6084/m9.figshare.14813115.v1
title: Ambient plume tracer and sediment concentration

description: Tracer and sediment concentration measured during the ambient plume tracking. The data was measured by a tow-yo'ed ECOTriplet Scattering Fluorescence sensor. The background stratification was measured by a tow-yo'ed SBE911plus CTD sensor.<br>
DOI: 10.25548/17-dswp
title: Human nanoCT

description: High resolution nanoCT images and 3D reconstructions of human embryos.
DOI: 10.5281/zenodo.4993636
title: EvoBench: Benchmarking Schema Evolution in NoSQL

description: Docker containers for reproducing the proof of concept measurements with our NoSQL Schema Evolution Benchmark.
DOI: 10.5281/zenodo.4993635
title: EvoBench: Benchmarking Schema Evolution in NoSQL

description: Docker containers for reproducing the proof of concept measurements with our NoSQL Schema Evolution Benchmark.
DOI: 10.5281/zenodo.4991961
title: Pano3D: GibsonV2 Tiny-Medium-Fullplus High Resolution

description: Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. To achieve that we generate a new dataset and integrate evaluation metrics that capture not only depth performance, but also secondary traits like boundary preservation and smoothness. Moreover, Pano3D takes a step beyond typical intra-dataset evaluation schemes to inter-dataset performance assessment. By disentangling generalization to three different axes, Pano3D facilitates proper extrapolation assessment under different out-of-training data conditions. Relying on the Pano3D holistic benchmark for 360 depth estimation we perform an extended analysis and derive a solid baseline for the task.
description: Dataset with coupled color and depth maps.(normals are not included but may be provided upon request)
DOI: 10.5281/zenodo.4986012
title: Pano3D: GibsonV2 Full High Resolution

description: Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. To achieve that we generate a new dataset and integrate evaluation metrics that capture not only depth performance, but also secondary traits like boundary preservation and smoothness. Moreover, Pano3D takes a step beyond typical intra-dataset evaluation schemes to inter-dataset performance assessment. By disentangling generalization to three different axes, Pano3D facilitates proper extrapolation assessment under different out-of-training data conditions. Relying on the Pano3D holistic benchmark for 360 depth estimation we perform an extended analysis and derive a solid baseline for the task.
description: Dataset with coupled color and depth maps.(normals are not included but may be provided upon request)
DOI: 10.5281/zenodo.4991960
title: Pano3D: GibsonV2 Tiny-Medium-Fullplus High Resolution

description: Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. To achieve that we generate a new dataset and integrate evaluation metrics that capture not only depth performance, but also secondary traits like boundary preservation and smoothness. Moreover, Pano3D takes a step beyond typical intra-dataset evaluation schemes to inter-dataset performance assessment. By disentangling generalization to three different axes, Pano3D facilitates proper extrapolation assessment under different out-of-training data conditions. Relying on the Pano3D holistic benchmark for 360 depth estimation we perform an extended analysis and derive a solid baseline for the task.
description: Dataset with coupled color and depth maps.(normals are not included but may be provided upon request)
DOI: 10.5281/zenodo.4957413
title: Pano3D: Matterport3D (/w Filmic) High Resolution

description: Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. To achieve that we generate a new dataset and integrate evaluation metrics that capture not only depth performance, but also secondary traits like boundary preservation and smoothness. Moreover, Pano3D takes a step beyond typical intra-dataset evaluation schemes to inter-dataset performance assessment. By disentangling generalization to three different axes, Pano3D facilitates proper extrapolation assessment under different out-of-training data conditions. Relying on the Pano3D holistic benchmark for 360 depth estimation we perform an extended analysis and derive a solid baseline for the task.
description: Dataset with coupled color, depth and normal maps.
DOI: 10.5281/zenodo.4986011
title: Pano3D: GibsonV2 Full High Resolution

description: Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. To achieve that we generate a new dataset and integrate evaluation metrics that capture not only depth performance, but also secondary traits like boundary preservation and smoothness. Moreover, Pano3D takes a step beyond typical intra-dataset evaluation schemes to inter-dataset performance assessment. By disentangling generalization to three different axes, Pano3D facilitates proper extrapolation assessment under different out-of-training data conditions. Relying on the Pano3D holistic benchmark for 360 depth estimation we perform an extended analysis and derive a solid baseline for the task.
description: Dataset with coupled color and depth maps.(normals are not included but may be provided upon request)
DOI: 10.5281/zenodo.4966684
title: Pano3D: GibsonV2 Tiny-Medium-Fullplus (/w Filmic) Low Resolution

description: Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. To achieve that we generate a new dataset and integrate evaluation metrics that capture not only depth performance, but also secondary traits like boundary preservation and smoothness. Moreover, Pano3D takes a step beyond typical intra-dataset evaluation schemes to inter-dataset performance assessment. By disentangling generalization to three different axes, Pano3D facilitates proper extrapolation assessment under different out-of-training data conditions. Relying on the Pano3D holistic benchmark for 360 depth estimation we perform an extended analysis and derive a solid baseline for the task.
description: Dataset with coupled color, depth and normal maps.