cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8095498
title: OLID I: An Open Leaf Image Dataset of Bangladesh's Major Crops

description: Artificial Intelligence (AI) has taken the globe by storm since its inception, and the enormous agriculture sector is no exception. The progress of any AI-assisted mechanism is heavily reliant on massive training data. Although the application of AI in plant leaf management has garnered prominence in recent years, there is still a dearth of data, especially in the case of tropical and subtropical crops. In light of this, we present a public dataset containing 4,749 leaf images which include healthy, nutritionally deficient, and pest-infested leaves of tomato (<em>Solanum lycopersicum</em>), eggplant (<em>Solanum melongena</em>), cucumber (<em>Cucumis sativus</em>), bitter gourd (<em>Momordica charantia</em>), snake gourd (<em>Trichosanthes cucumerina</em>), ridge gourd (<em>Luffa acutangula</em>), ash gourd (<em>Benincasa hispida)</em>, and bottle gourd (<em>Lagenaria siceraria</em>). The dataset comprises 57 unique classes with high-resolution photos (3024 x 3024). The images have been captured at three different sites in Bangladesh in natural field settings and arduously labeled by an expert panel. This collection features the highest number of plant stress classes and the first multi-label classification problem in the agro-domain. The effective utilization of our dataset will result in an abundance of leaf disease diagnosis algorithms, pest identification and classification tools, and nutritional deficiency estimation strategies, to highlight a few.
description: The dataset is split into 19 zip files for easier access. The excel file comprises a detailed class distribution.
DOI: 10.5281/zenodo.8105154
title: OLID I: An Open Leaf Image Dataset of Bangladesh's Major Crops

description: Artificial intelligence (AI) has taken the globe by storm since its inception, and the enormous agriculture sector is no exception. The progress of any AI-assisted mechanism is heavily reliant on massive training data. Although the application of AI in plant leaf management has garnered prominence in recent years, there is still a dearth of data, especially in the case of tropical and subtropical crops. In light of this, we present a public dataset containing 4,749 leaf images which include healthy, nutritionally deficient, and pest-infested leaves of tomato (<em>Solanum lycopersicum</em>), eggplant (<em>Solanum melongena</em>), cucumber (<em>Cucumis sativus</em>), bitter gourd (<em>Momordica charantia</em>), snake gourd (<em>Trichosanthes cucumerina</em>), ridge gourd (<em>Luffa acutangula</em>), ash gourd (<em>Benincasa hispida)</em>, and bottle gourd (<em>Lagenaria siceraria</em>). The dataset comprises 57 unique classes with high-resolution photos (3024 x 3024). The images have been captured at three different sites in Bangladesh in natural field settings and arduously labeled by an expert panel. This collection features the highest number of plant stress classes and the first multi-label classification problem in the agro-domain. The effective utilization of our dataset will result in an abundance of leaf disease diagnosis algorithms, pest identification and classification tools, and nutritional deficiency estimation strategies, to highlight a few.
description: The dataset is split crop-wise into 18 zip files for easier access. The excel file comprises a detailed class distribution.
DOI: 10.6084/m9.figshare.24131985.v1
title: Data and code share for NGS

description: 123423
DOI: 10.6084/m9.figshare.24131985
title: Data and code share for NGS

description: Code and data for the paper “Global crop production increase by soil organic carbon”Code authors: Dominic Woolf (domwoolf@gmail.com) and Yuqing Ma (yuqingma0114@gmail.com)Data contact by Mingsheng Fan (fanms@cau.edu.cn)Description: R scripts and data to quantify the causal effect of soil organic carbon (SOC) on crop yield of the three major crops (maize, wheat and rice). The R script and data aim to achieve three contents: (1) quantify the causal effect of SOC on crop yield; (2) analyze the major drivers of soil organic carbon yield gap; (3) make global extrapolation of SOC yield gap for the three studied crops.***Please install the necessary packages using the R script “Load Packages.R”.1. Causal effect of SOC on crop yield(1) “Causal effect of SOC on crop yield by Random Forest.R”: R script used to establish the causal effect of SOC on crop yield by partial dependence using Random Forest.(2) “Causal effect of SOC on crop yield by Causal Forest.R”: R script used to estimate the effect size of SOC on crop yield by casual forest.(3) “Causal effect of SOC on crop yield under different N levels.R”: R script used to establish the casual effect of SOC on crop yield under different nitrogen input levels.(4) “Causal effect of SOC on crop yield under climate zones”: R script used to establish the casual effect of SOC on crop yield under global climate zones.(5) “data.csv”: CSV data used in this section.<br>2. Drivers of SOC yield gap(1) “Calculating SOC yield gap.R”: R script used to estimate SOC yield gap for each field trial.(2) “Determination of drivers for the SOC yield gaps.R”: R script used to quantify the driver of SOC yield gap based on field trial data by Random Forest.(3) “data.csv”: CSV data used in this section.<br>3. Global extrapolation of SOC yield gap(1) “Determination of coefficient and index of agreement for global extrapolation model.R”: R script used to analyze the model performance for global extrapolation(2) “Leave one out cross-validation to estimate prediction uncertainty for global extrapolation.R”: R script used to estimate the standard errors of predicted SOC yield gaps derived from global extrapolation models.(3) “Technical potential for global extrapolation.R”: R script used to estimate global yield increase under technical potential to increase SOC.(4) ”10.Unconstrained increase for global extrapolation.R”: R script used to estimate global yield increase under unconstrained increase in SOC.(5) “data.csv”: CSV data used in this section.(6) “Maize global data.csv”: The distribution of global maize cropping area used to estimate global maize yield increase derived from SOC.(7) “Wheat global data.csv”: The distribution of global wheat cropping area used to estimate global wheat yield increase derived from SOC.(8) “Rice global data.csv”: The distribution of global rice cropping area used to estimate global rice yield increase derived from SOC.
DOI: 10.6084/m9.figshare.24131985.v2
title: Data and code share for NGS

description: Code and data for the paper “Global crop production increase by soil organic carbon”Code authors: Dominic Woolf and Yuqing MaCode contact by: Yuqing Ma (yuqingma0114@gmail.com)Data contact by Mingsheng Fan (fanms@cau.edu.cn)Description: R scripts and data to quantify the causal effect of soil organic carbon (SOC) on crop yield of the three major crops (maize, wheat and rice). The R script and data aim to achieve three contents: (1) quantify the causal effect of SOC on crop yield; (2) analyze the major drivers of soil organic carbon yield gap; (3) make global extrapolation of SOC yield gap for the three studied crops.***Please install the necessary packages using the R script “Load Packages.R”.1. Causal effect of SOC on crop yield(1) “Causal effect of SOC on crop yield by Random Forest.R”: R script used to establish the causal effect of SOC on crop yield by partial dependence using Random Forest.(2) “Causal effect of SOC on crop yield by Causal Forest.R”: R script used to estimate the effect size of SOC on crop yield by casual forest.(3) “Causal effect of SOC on crop yield under different N levels.R”: R script used to establish the casual effect of SOC on crop yield under different nitrogen input levels.(4) “Causal effect of SOC on crop yield under climate zones”: R script used to establish the casual effect of SOC on crop yield under global climate zones.(5) “data.csv”: CSV data used in this section.<br>2. Drivers of SOC yield gap(1) “Calculating SOC yield gap.R”: R script used to estimate SOC yield gap for each field trial.(2) “Determination of drivers for the SOC yield gaps.R”: R script used to quantify the driver of SOC yield gap based on field trial data by Random Forest.(3) “data.csv”: CSV data used in this section.<br>3. Global extrapolation of SOC yield gap(1) “Determination of coefficient and index of agreement for global extrapolation model.R”: R script used to analyze the model performance for global extrapolation(2) “Leave one out cross-validation to estimate prediction uncertainty for global extrapolation.R”: R script used to estimate the standard errors of predicted SOC yield gaps derived from global extrapolation models.(3) “Technical potential for global extrapolation.R”: R script used to estimate global yield increase under technical potential to increase SOC.(4) ”10.Unconstrained increase for global extrapolation.R”: R script used to estimate global yield increase under unconstrained increase in SOC.(5) “data.csv”: CSV data used in this section.(6) “Maize global data.csv”: The distribution of global maize cropping area used to estimate global maize yield increase derived from SOC.(7) “Wheat global data.csv”: The distribution of global wheat cropping area used to estimate global wheat yield increase derived from SOC.(8) “Rice global data.csv”: The distribution of global rice cropping area used to estimate global rice yield increase derived from SOC.
DOI: 10.6084/m9.figshare.24132663.v1
title: Raw data including metadata for Duxbury et al., 2023

description: Raw data including metadata for Duxbury et al., 2023<br><br>
DOI: 10.6084/m9.figshare.24132663
title: Raw data including metadata for Duxbury et al., 2023

description: Raw data including metadata for Duxbury et al., 2023<br><br>
DOI: 10.6084/m9.figshare.20490051
title: Trichocomaceae alignment files

description: Supplementary phylogenetic data referenced in the article: "Two new species of <i>Trichocomaceae</i> (<i>Eurotiales</i>), accommodated in <i>Rasamsonia</i> and <i>Talaromyces</i> section <i>Bacillispori</i>, from the Czech Republic"DOI: 10.1038/s41598-023-42002-7Contains alignments and corresponding ML/BI trees.<br>
DOI: 10.3886/e136301
title: Replication Data for: Overpersistence Bias in Individual Income Expectations and its Aggregate Implications

description: Using micro level data, we document systematic forecast errors in household income expectations that are related to the level of income. We show that these errors can be formalized by a modest deviation from rational expectations, where agents overestimate the persistence of their income process. We then investigate the implications of these distortions on consumption and savings behavior and find two effects. First, these distortions allow an otherwise fully optimization-based quantitative model to match the joint distribution of liquid assets and income. Second, the bias alters the distribution of marginal propensities to consume which makes government stimulus policies less effective. <br><br>These codes were used to obtain the results in the paper.
DOI: 10.3886/e136301v1
title: Replication Data for: Overpersistence Bias in Individual Income Expectations and its Aggregate Implications

description: Using micro level data, we document systematic forecast errors in household income expectations that are related to the level of income. We show that these errors can be formalized by a modest deviation from rational expectations, where agents overestimate the persistence of their income process. We then investigate the implications of these distortions on consumption and savings behavior and find two effects. First, these distortions allow an otherwise fully optimization-based quantitative model to match the joint distribution of liquid assets and income. Second, the bias alters the distribution of marginal propensities to consume which makes government stimulus policies less effective. <br><br>These codes were used to obtain the results in the paper.
DOI: 10.25345/c5rj49529
title: MassIVE MSV000092849 - Toxoplasma protein phosphatase PPKL phosphoproteomics data
title: Toxoplasma protein phosphatase PPKL phosphoproteomics data of the paper "The protein phosphatase PPKL is a key regulator of daughter parasite development in Toxoplasma gondii".
DOI: 10.5281/zenodo.7884467
title: Cookiescanner Dataset

description: This upload contains the dataset from the paper "Cookiescanner: An Automated Tool for Detecting and Evaluating GDPR Consent Notices on Websites" presented at ARES 2023.<br> <br> <strong>Folder Structure</strong><br> - <strong>01_bert_classifier</strong>: The final BERT model as well as the datasets and Jupyter notebook to train/evaluate it. Additionally, the folder also contains a small flask application and a Docker file to deploy it as a web service.<br> - <strong>02_raw_dataset</strong>: The 1.000 sampled scans. The results.json contains the raw scan data, while the rest of the subfolders contain the screenshots of the detection methods.<br> - <strong>03_banner_detection</strong>: Contains the analysis CSV file, as well as a folder with the banner screenshots.<br> - <strong>04_dark_patterns</strong>: Analysis files as well as the screenshots of banners with the dark patterns from the paper. <strong>Source Code</strong><br> For the source code of the scanner, please refer to https://github.com/UBA-PSI/cookiescanner.
DOI: 10.5281/zenodo.8334087
title: Cookiescanner Dataset

description: This upload contains the dataset from the paper "Cookiescanner: An Automated Tool for Detecting and Evaluating GDPR Consent Notices on Websites" presented at ARES 2023.<br> <br> <strong>Folder Structure</strong><br> - <strong>01_bert_classifier</strong>: The final BERT model as well as the datasets and Jupyter notebook to train/evaluate it. Additionally, the folder also contains a small flask application and a Docker file to deploy it as a web service.<br> - <strong>02_raw_dataset</strong>: The 1.000 sampled scans. The results.json contains the raw scan data, while the rest of the subfolders contain the screenshots of the detection methods.<br> - <strong>03_banner_detection</strong>: Contains the analysis CSV file, as well as a folder with the banner screenshots.<br> - <strong>04_dark_patterns</strong>: Analysis files as well as the screenshots of banners with the dark patterns from the paper. <strong>Source Code</strong><br> For the source code of the scanner, please refer to https://github.com/UBA-PSI/cookiescanner.
DOI: 10.5281/zenodo.8340396
title: GLH-NDRS Centralised Data Submission Survey Results

description: Full responses from the 2023 UK CSG Diagnostic Laboratory Survey component focused on centralised data submission to PHE/NHSD and LIMS system organisation. To preserve anonymity, free-text comments are not provided.
DOI: 10.5281/zenodo.8341484
title: Indirect cytocompatibility and antibacterial properties of Zr-Cu-Ag metallic glass coatings

description: In order to evaluate a possible antibacterial effect of the released ions, specimens (4x4 mm<sup>2</sup> square metallic glass coatings) were submerged with 7 ml of LB broth and maintained at 37°C inside a shaker (120 rpm) for 1, 5 days, following the same procedure exploited for the ions release evaluation. At each time point the supernatants were collected and used to cultivate bacteria at a defined concentration (1x10<sup>5</sup> cells/ml); bacteria cultivated with supernatants obtained from ions-free PBT were considered as control. The released ions killing activity was evaluated in terms of metabolic activity that was measured by the alamar blue assay as previously detailed. The same procedure with LB medium for hMSC was repeated.
description: Marie Skłodowska-Curie Individual Fellowship (MSCA-IF-EF-ST) Horizon 2020 - Research and Innovation Framework Program Grant Agreement Number: 892050 Project Acronym: MAGIC Project title: [Development of antibacterial MetAllic Glass mICrofibers] Principal Investigator: Elham Sharifikolouei
DOI: 10.5281/zenodo.8341483
title: Indirect cytocompatibility and antibacterial properties of Zr-Cu-Ag metallic glass coatings

description: In order to evaluate a possible antibacterial effect of the released ions, specimens (4x4 mm<sup>2</sup> square metallic glass coatings) were submerged with 7 ml of LB broth and maintained at 37°C inside a shaker (120 rpm) for 1, 5 days, following the same procedure exploited for the ions release evaluation. At each time point the supernatants were collected and used to cultivate bacteria at a defined concentration (1x10<sup>5</sup> cells/ml); bacteria cultivated with supernatants obtained from ions-free PBT were considered as control. The released ions killing activity was evaluated in terms of metabolic activity that was measured by the alamar blue assay as previously detailed. The same procedure with LB medium for hMSC was repeated.
description: Marie Skłodowska-Curie Individual Fellowship (MSCA-IF-EF-ST) Horizon 2020 - Research and Innovation Framework Program Grant Agreement Number: 892050 Project Acronym: MAGIC Project title: [Development of antibacterial MetAllic Glass mICrofibers] Principal Investigator: Elham Sharifikolouei
DOI: 10.7284/144979
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AR60-01 using a Hawaii UHDAS instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144981
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR60-01 using a SeaBird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144993
title: Flowmeter data collected during research cruise AR60-01 using a Thiel Thiel Flowmeter instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144972
title: Fluorometer data collected during research cruise AR60-01 using a WETLabs WETStar instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144976
title: GNSS (Global Navigation Satellite System) data collected during research cruise AR60-01 using a CCTech C-Nav 3050 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144992
title: GNSS (Global Navigation Satellite System) data collected during research cruise AR60-01 using a Kongsberg DPS112 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144980
title: Gravimeter data collected during research cruise AR60-01 using a Bell BGM-3 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144989
title: Gyrocompass data collected during research cruise AR60-01 using a Sperry NAVIGAT X MK 1 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144991
title: INS (Inertial Navigation System) data collected during research cruise AR60-01 using a Applanix POS/MV-320 V5 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144982
title: Meteorological Station data collected during research cruise AR60-01 using a Vaisala WXT520 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144983
title: Meteorological Station data collected during research cruise AR60-01 using a Vaisala WXT520 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144986
title: Multibeam Echosounder data collected during research cruise AR60-01 using a Kongsberg EM122 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144987
title: Multibeam Echosounder data collected during research cruise AR60-01 using a Kongsberg EM710 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144997
title: pCO2 (Partial Pressure of Carbon Dioxide) data collected during research cruise AR60-01 using a GeneralOceanics 8050 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144977
title: Radiometer data collected during research cruise AR60-01 using a Eppley RAD instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144978
title: Radiometer data collected during research cruise AR60-01 using a Biospherical QSR-2150 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144975
title: Singlebeam Echosounder data collected during research cruise AR60-01 using a Knudsen 3260 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144994
title: Speedlog data collected during research cruise AR60-01 using a Furuno DS-60 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144990
title: Splitbeam Echosounder data collected during research cruise AR60-01 using a Kongsberg EK80 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144995
title: SSV (Sound Speed Velocimeter) data collected during research cruise AR60-01 using a Valeport Thru-Hull SVS instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144984
title: Water Temperature data collected during research cruise AR60-01 using a SeaBird SBE-48 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144996
title: Transmissometer data collected during research cruise AR60-01 using a SeaBird C-Star instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/144973
title: TSG (Thermosalinograph) data collected during research cruise AR60-01 using a SeaBird SBE-45 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/143229
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise NBP1101 using a Hawaii UHDAS instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/143378
title: Multiplexed data collected during research cruise NBP1101 using a HOBI HydroDAS instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/143381
title: Gravimeter data collected during research cruise NBP1101 using a LR S-33 instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/143384
title: Meteorological Station data collected during research cruise NBP1101 using a USAP RVDAS Metstation instrument system onboard the platform RV Nathaniel B. Palmer

description: Original (unprocessed) data collected during a Field_expedition