cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7674076
title: Data for "Rapid seaward expansion of seaport footprints worldwide"

description: Data used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023: https://doi.org/10.31223/X5SD3T). Data include five csv files: 'Sengupta_Lazarus_2023_F1_b' – total reclaimed area (km^2) in 2020, ranked by magnitude 'Sengupta_Lazarus_2023_F2_a' – normalised reclaimed area versus time for 68 seaports 'Sengupta_Lazarus_2023_F2_b' – series of ranks by different metrics: Lloyd's List 2020 (Lloyd's, 2021), total reclaimed area in 2020, area under normalised curves of reclaimed area through time, and coastal & cyclone hazard (Verschuur et al., 2023). 'Sengupta_Lazarus_rec_TEU_2011_2020' – data for container volume (in millions TEU) relative to reclamation area, 2011-2020; data for container volume sourced from UNCTD and the World Shipping Council. 'Sengupta_Lazarus_time_series_rec_data' – data (raw and smoothed) for seaport reclamation (km^2), 1990–2020 The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation Code for plotting these data are available here: https://github.com/edlazarus/Seaports
description: {"references": ["Sengupta, D., Choi, Y. R., Tian, B., Brown, S., Meadows, M., Hackney, C. R., et al. (2023). Mapping 21st century global coastal land reclamation. Earth's Future, 11, e2022EF002927. https://doi.org/10.1029/2022EF002927", "Verschuur, J., Koks, E. E., Li, S. et al. (2023). Multi-hazard risk to global port infrastructure and resulting trade and logistics losses. Commun Earth Environ 4, 5. https://doi.org/10.1038/s43247-022-00656-7"]}
DOI: 10.5281/zenodo.8158703
title: Data for "Rapid seaward expansion of seaport footprints worldwide"

description: [updated July 2023] This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; preprint: https://doi.org/10.31223/X5SD3T). The data include four csv files: <strong>'Sengupta_Lazarus_REC_1990_2020_v02.csv' </strong>– annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed]. To produce the smoothed data, the raw data are passed through a Savitzky–Golay filter. <strong>'Sengupta_Lazarus_REC_TEU_2011_2020_v02.csv'</strong> – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], and TEU (millions), collated from archived Lloyd's List reports. <strong>'Sengupta_Lazarus_REC_TEU_totals_v02.csv'</strong> – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (Lloyd's List, 2021). <strong>'Sengupta_Lazarus_2023_ports_excluded.csv'</strong> – contains list of 34 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020. <strong>'RECLAIM_port_trajectories_v7.ipynb' </strong>– Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.) The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation These data and code are also available here: https://github.com/edlazarus/Seaports
description: {"references": ["Sengupta, D., Choi, Y. R., Tian, B., Brown, S., Meadows, M., Hackney, C. R., et al. (2023). Mapping 21st century global coastal land reclamation. Earth's Future, 11, e2022EF002927. https://doi.org/10.1029/2022EF002927", "Verschuur, J., Koks, E. E., Li, S. et al. (2023). Multi-hazard risk to global port infrastructure and resulting trade and logistics losses. Commun Earth Environ 4, 5. https://doi.org/10.1038/s43247-022-00656-7"]}
DOI: 10.21227/mwq9-1d42
title: Color-distorted image dataset

description: Automatic white balance (AWB) is an important module for color constancy of cameras. The classification of the normal image and the color-distorted image is critical to realize intelligent AWB. One tenth of ImageNet is utilized as the normal image dataset for training, validating and testing. The distorted dataset is constructed by the proposed theory for generation of color distortion. To generate various distorted color, histogram shifting and matching are proposed to randomly adjust the histogram position or shape. Furthermore, the extent for shifting or matching is randomly generated to ensure the diversity of color distortion. The dataset is robust because both histogram position and histogram shape are randomly adjusted. There are totally 288,132 images in the dataset.
DOI: 10.5517/ccdc.csd.cc2g3zn7
title: CCDC 2267949: Experimental Crystal Structure Determination

description: Related Article: Michal Aman, Libor Dostál, Aleš Růžička, Zdenka Růžičková, Roman Jambor|2023|Dalton Trans.|52|16870|doi:10.1039/D3DT02568C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g3zp8
title: CCDC 2267950: Experimental Crystal Structure Determination

description: Related Article: Michal Aman, Libor Dostál, Aleš Růžička, Zdenka Růžičková, Roman Jambor|2023|Dalton Trans.|52|16870|doi:10.1039/D3DT02568C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g3zq9
title: CCDC 2267951: Experimental Crystal Structure Determination

description: Related Article: Michal Aman, Libor Dostál, Aleš Růžička, Zdenka Růžičková, Roman Jambor|2023|Dalton Trans.|52|16870|doi:10.1039/D3DT02568C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g3zrb
title: CCDC 2267952: Experimental Crystal Structure Determination

description: Related Article: Michal Aman, Libor Dostál, Aleš Růžička, Zdenka Růžičková, Roman Jambor|2023|Dalton Trans.|52|16870|doi:10.1039/D3DT02568C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h073w
title: CCDC 2294127: Experimental Crystal Structure Determination

description: Related Article: Mingduo Lu, Yuanhong Liu|2023|Org.Lett.|25|8105|doi:10.1021/acs.orglett.3c03188
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h075y
title: CCDC 2294129: Experimental Crystal Structure Determination

description: Related Article: Mingduo Lu, Yuanhong Liu|2023|Org.Lett.|25|8105|doi:10.1021/acs.orglett.3c03188
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.10201225
title: Lantana camara L. (Verbenaceae) invasion effects on soil physicochemical properties in Ethiopian highland plateau ecosystems
DOI: 10.6084/m9.figshare.24405739
title: Underwater Sonifery Data

description: This file provides the complete underwater sonifery dataset, including species information, reference information, and an associated data dictionary. The dataset as hosted on WoRMS may subsequently be updated.
DOI: 10.6084/m9.figshare.24405742
title: Underwater Sonifery Code

description: These files are the R code and other data files used to create some of the underwater sonifery dataset and visualize the results of the data collection.
DOI: 10.6084/m9.figshare.24405742.v1
title: Underwater Sonifery Code

description: These files are the R code and other data files used to create some of the underwater sonifery dataset and visualize the results of the data collection.
DOI: 10.25326/549
title: Cloud droplet size distribution from 2-D polarization measurements of specMACS during EUREC4A

description: The data of the two polarization resolving cameras (lower left camera: polLL and lower right camera: polLR) of the spectrometer of the Munich Aerosol Cloud Scanner (specMACS) of the LMU are used to provide a dataset of the cloud droplet size distribution of liquid water clouds. 

During EUREC4A, specMACS was mounted onboard the research aircraft HALO in a downward-looking perspective. From the polarization measurements of the cloudbow, the effective radius and the effective variance of the cloud droplet size distribution (CDSD) are determined (Pörtge et al., 2023). This dataset provides the CDSDs for individual cloud targets with a target size of approximately 100 m x 100 m for all flight segments flown by HALO during the EUREC4A campaign. As a specific solar and viewing geometry is required to observe the cloudbow, not all flight segments can be evaluated. The effective radii and variances are determined individually for the measurements of all three color channels of the camera. The method derives the CDSD by fitting pre-calculated simulations of the cloudbow to the measurements. Various quality measures (RMSE, quality Index, scaling factor) of the fit are included in the dataset. The polarization signal of the cloudbow is a single scattering signal. Therefore, the derived CDSD should be representative of the CDSD at the cloud top. Furthermore, the mean coordinates of the target are given in both WGS-84 coordinates and in a cartesian reference frame which has its origin at 13.1°N and -57.4°E.
DOI: 10.5519/qd.4wy52tdh
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5730035 records)
DOI: 10.5281/zenodo.10201399
title: Language bias in orthodontic systematic reviews.

description: Dataset
DOI: 10.5281/zenodo.10201400
title: Language bias in orthodontic systematic reviews.

description: Dataset
DOI: 10.34740/kaggle/dsv/7036648
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/7036649
title: Mahanagar Yatayat Transport Dataset
DOI: 10.48544/65c7e957-9278-41fe-b7b1-38d7fc34ca39
title: Jonge thuiszitters uit de schaduw
DOI: 10.6084/m9.figshare.24623814
title: Supplementary information table for "The implementation of a simplex-centroid mixture design approach to generate components of sugar industry by-products for usage as biomass pellets"

description: It is the supplementary data to the paper "The implementation of a simplex-centroid mixture design approach to generate components of sugar industry by-products for usage as biomass pellets"AbstractThe current study aimed to improve the efficiency of raw materials that were by-products of the sugar industry, such as sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC), in order to obtain a mixture ratio with good thermal properties that could be used as a renewable energy source. The generated model offered an acceptable estimate of HHV with an R<sup>2</sup> of 96.46, a mean absolute error (MAE) of 3.45%, and a mean bias error (MBE) of -1.83%, while the second portion was a simplex-centroid mixture design used to assess LHV of sugar industrial waste mixture ratios. The results of the LHV prediction equation were found to have an R<sup>2</sup> of 93.66%. The prediction equation's assessment and validation generated average absolute error (AAE) of 7.07% and average bias error (ABE) of 6.02%. The outcomes from the design elements applying the simplex-centroid mixture design approach revealed biomass's potential as a fuel and served as essential in advancing the use of byproducts as an alternative energy source in the sugar industry.
DOI: 10.6084/m9.figshare.24623814.v1
title: Supplementary information table for "The implementation of a simplex-centroid mixture design approach to generate components of sugar industry by-products for usage as biomass pellets"

description: It is the supplementary data to the paper "The implementation of a simplex-centroid mixture design approach to generate components of sugar industry by-products for usage as biomass pellets"AbstractThe current study aimed to improve the efficiency of raw materials that were by-products of the sugar industry, such as sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC), in order to obtain a mixture ratio with good thermal properties that could be used as a renewable energy source. The generated model offered an acceptable estimate of HHV with an R<sup>2</sup> of 96.46, a mean absolute error (MAE) of 3.45%, and a mean bias error (MBE) of -1.83%, while the second portion was a simplex-centroid mixture design used to assess LHV of sugar industrial waste mixture ratios. The results of the LHV prediction equation were found to have an R<sup>2</sup> of 93.66%. The prediction equation's assessment and validation generated average absolute error (AAE) of 7.07% and average bias error (ABE) of 6.02%. The outcomes from the design elements applying the simplex-centroid mixture design approach revealed biomass's potential as a fuel and served as essential in advancing the use of byproducts as an alternative energy source in the sugar industry.
DOI: 10.34740/kaggle/dsv/7036711
title: Online Shopping Sentiment Analysis Flipkart
DOI: 10.6073/pasta/147b9a29bc682b3d94a9c4db7ee30d2c
title: Interagency Ecological Program: Discrete water quality monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2022

description: \ The Interagency Ecological Program’s (IEP) Environmental Monitoring Program (EMP) was initiated in compliance with the Water Right Decision D-1379 (now mandated by Water Right Decision D-1641) and has monitored discrete water quality and nutrients in the upper San Francisco Estuary since 1975. The objectives of the EMP are to obtain consistent and accurate monthly data at established monitoring stations, provide and document information necessary to achieve compliance with salinity, flow, and dissolved oxygen standards, and to report this information for the purpose of management and conservation of the upper San Francisco Estuary. While the EMP also collects biological data, this dataset only includes the discrete water quality and nutrient data collected by the EMP from 1975-2021. Links to other EMP datasets can be found [here](https://emp-des.github.io/emp-reports/data-links.html) \
DOI: 10.34740/kaggle/dsv/7036751
title: Online Shopping Sentiment Analysis Flipkart
DOI: 10.6084/m9.figshare.24624774
title: Search strategy in OVID - <b>Efficacy of non-pharmacological interventions in reversion to normoglycemia and cardiometabolic markers in patients with prediabetes (IGF and IGT)</b>

description: This file has important information about the search strategy,
DOI: 10.6084/m9.figshare.24624774.v1
title: Search strategy in OVID - <b>Efficacy of non-pharmacological interventions in reversion to normoglycemia and cardiometabolic markers in patients with prediabetes (IGF and IGT)</b>

description: This file has important information about the search strategy,
DOI: 10.18112/openneuro.ds004860.v1.0.0
title: Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study
DOI: 10.6084/m9.figshare.24624564
title: Trash state classification.

description: The project explores the development of an AI model using computer vision for precise household waste classification, targeting plastics, cardboard, glass, cans, and paper. The literature review underscores the environmental concern in Costa Rica and examines AI techniques in solid waste management for enhanced efficiency and accuracy.The methodology employs a mixed approach, combining quantitative and applied elements. A carefully selected dataset and augmentation techniques are used to develop a robust AI model. Evaluation occurs in real household environments with active user participation. Results indicate good performance in classifying with positive user acceptance.
DOI: 10.6084/m9.figshare.24624768
title: HRP2 and pLDH performance comparison as rapid diagnostic tools for symptomatic malaria patients at Rugazi HC IV in Rubirizi District.

description: This research was aimed at comparing the diagnostic performance of HRP2 and pLDH mRDTs for malaria diagnosis among symptomatic OPD patients at Rugazi HCIV in Rubirizi District by:Determining the positivity and negativity rate of HRP2 and pLDH mRDTs Determining the sensitivity, specificity, positive and negative predictive values of HRP2 and pLDH mRDTs Assessing the most common plasmodium species in malaria-symptomatic patients at Rugazi HC IV.
DOI: 10.6084/m9.figshare.24624768.v1
title: HRP2 and pLDH performance comparison as rapid diagnostic tools for symptomatic malaria patients at Rugazi HC IV in Rubirizi District.

description: This research was aimed at comparing the diagnostic performance of HRP2 and pLDH mRDTs for malaria diagnosis among symptomatic OPD patients at Rugazi HCIV in Rubirizi District by:Determining the positivity and negativity rate of HRP2 and pLDH mRDTs Determining the sensitivity, specificity, positive and negative predictive values of HRP2 and pLDH mRDTs Assessing the most common plasmodium species in malaria-symptomatic patients at Rugazi HC IV.
DOI: 10.6084/m9.figshare.24547774
title: Supplementary materials for Rudd et al (Challenges 14 - Bitcoin mining research needs)

description: <b>Supplementary material for: </b>Rudd MA, Bratcher L, Collins S, Branscum D, Carson M, Connell S, David E, Gronowska M, Hess S, Mitchell A, Prusak M, Schneps K, Serezhin M, Wolfe SA, Porter D. 2023. Bitcoin and its energy, environmental, and social impacts: an assessment of key research needs in the mining sector. Challenges 14:xx (https://doi.org/10.3390/xxxxx)<b>Article abstract</b>: In this study, we used a combination of AI-assisted analysis of social media discourse and collaboration with industry experts to delve into the key research needs associated with the Bitcoin mining industry. We identified primary threats, opportunities, and research questions related to the Bitcoin mining industry and its wider impacts, focusing on its energy use and environmental footprint. Our findings spotlight the industry's move towards increasingly greater energy efficiency and an emerging commitment to renewable energy, highlighting its potential to contribute to the coming energy transition. We underscore the transformative potential of emerging applications in the Bitcoin mining sector, especially regarding demand response, grid flexibility, and methane mitigation. We suggest that targeted research on Bitcoin can serve policymakers, private sector decision-makers, research funding agencies, environmental scientists, and the Bitcoin industry itself. We propose that filling key information gaps could help clarify the risks and benefits of Bitcoin mining by encouraging collaboration among researchers, policymakers, and industry stakeholders, and conducting research that provides baseline peer-reviewed evidence surrounding Bitcoin’s production and impacts. A collaborative approach could help mitigate the risks and realize the benefits of Bitcoin mining, including potentially positive and substantive contributions in alignment with the Sustainable Development Goals.
DOI: 10.21223/s3rgsi/3w6wpl
title: 01_PTYield112022_EXP_LTVR_HYO.xlsx

description: :unav
DOI: 10.21223/s3rgsi/5opkyh
title: 01_LTVR_Majes_2022_Cycle 7.tab

description: :unav
DOI: 10.21223/s3rgsi/8g9b2y
title: 01_VHW_SR_2021_Cycle 7.tab

description: :unav
DOI: 10.21223/s3rgsi/ysgumb
title: 01_PTYield112022_EXP_LTVR_HYO.xlsx

description: :unav