cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14713371
title: BRCA2 prevents PARPi-mediated PARP1 retention to protect RAD51 filaments

description: Manuscript number: 2023-10-18174A-Z

This folder contains raw data for both FACS and smFRET analyses used in the manuscript. The smFRET dataset has been post-processed to remove trajectories affected by photobleaching or exhibiting photo blinking events. Additional resources and details are available from the corresponding author upon request.
DOI: 10.5281/zenodo.15040288
title: Numerical data for publication "Nonlinear-response theory for lossy superconducting quantum circuits"

description: Numerical data and plotting scripts for Figures in publication "Nonlinear-response theory for lossy superconducting quantum circuits", Phys. Rev. Research
DOI: 10.5281/zenodo.2532738
title: Hemiptera-Phytoplasma-Plant dataset

description: This is a database of Hemiptera-Phytoplasma-Plant (HPP) biological interactions worldwide. The database contains 1860 records of plant-phytoplasma pairwise associations, and 968 records of insect-phytoplasma pairwise associations. Only the earliest reported record of each specific association between a 16Sr phytoplasma subgroup and a host (plant or insect vector) is reported for each country in the database. Last update: Nov 2018
DOI: 10.6084/m9.figshare.28524821
title: Data of "Telomere-to-telomere genome assemblies of three silkworm strains with long-term pupal characteristics"

description: Supplementary file 1.The annotation files (GFF3) of the three genomes, KA, L, and M, involved in this article.
Supplementary file 2. The annotation files (GFF3) of the rRNA genes of all genomes involved in this article.
DOI: 10.17632/35gb4v72dr.4
title: BananaSet: A Dataset of Banana Varieties in Bangladesh

description: This dataset presents an assortment of high-resolution images that exhibit six well-known banana varieties procured from two distinct regions in Bangladesh. These bananas were thoughtfully selected from rural orchards and local markets, providing a diverse and comprehensive representation. The dataset serves as a visual reference, offering a thorough portrayal of the distinct characteristics of these banana types, which aids in their precise classification. It encompasses six distinct categories, namely, Shagor, Shabri, Champa, Anaji, Deshi, and Bichi, with a total of 1166 original images and 6000 augmented JPG images. These images were diligently captured during the period from August 01 to August 15, 2023. The dataset includes two variations: one with raw images and the other with augmented images. Each variation is further categorized into six separate folders, each dedicated to a specific banana variety. The images are of non-uniform dimensions and have a resolution of 4608 × 3456 pixels. Due to the high resolution, the initial file size amounted to 4.08 GB. Subsequently, data augmentation techniques were applied, as machine vision deep learning models require a substantial number of images for effective training. Augmentation involves transformations like scaling, shifting, shearing, zooming, and random rotation. Specific augmentation parameters included rotations within a range of 1° to 40°, width and height shifts, zoom range, and shear ranges set at 0.2. As a result, an additional 1000 augmented images were generated from the original images in each category, resulting in a dataset comprising a total of 6000 augmented images (1000 per category) with a data size of 4.73 GB.
DOI: 10.17632/ptfscwtnyz.2
title: BananaImageBD: A Comprehensive Image Dataset of Common Banana Varieties with Different Ripeness Stages in Bangladesh.

description: Type of Data: 256x256 px Banana images.
Data Format: JPEG
Contents of the Dataset: Banana varieties and ripeness stages.

Number of Classes: (1) Four most popular banana varieties in Bangladesh - Bangla Kola, Chompa Kola, Sabri Kola, and Sagor Kola, and (2) Four ripeness stages - Green, Semi-ripe, Ripe, and Overripe.

Number of Images: (1) Total Original (Raw) images of banana varieties = 2,471, Augmented to 7,413 images, and (2) Total Original (Raw) images of ripeness stages = 820, Augmented to 2,457 images.

Distribution of Instances: (1) Original (Raw) images in each class of banana varieties: Bangla Kola = 444, Champa Kola = 994, Sabri Kola = 509, and Sagor Kola = 524; (2) Augmented images in each class of banana varieties: Bangla Kola = 1332, Champa Kola = 2,982, Sabri Kola = 1,527, Sagor Kola = 1,572; (3) Original (Raw) images in each class of Ripeness stages: Green = 212, Semi-ripe = 204, Ripe = 201, and Overripe = 203; (4) Augmented images in each class of Ripeness stages: Green = 636, Semi-ripe = 609, Ripe = 603, and Overripe = 609.

Dataset Size: (1) Total size of the Original (Raw) banana varieties dataset = 17.2 MB; (2) Total size of the Augmented banana varieties dataset = 78.5 MB; (3) Total size of the Original (Raw) ripeness stages dataset = 5.55 MB; and (4) Total size of the Augmented ripeness stages dataset = 25.2 MB.

Data Acquisition Process: Images of bananas are captured using high-quality smartphone cameras.
Data Source Location: Local banana wholesale markets and retail fruit shops located in different places in Bangladesh.
Where Applicable: The dataset presents considerable potential for fostering innovation and developing automated, efficient processes across various industries, such as precision agriculture, food processing, and supply chain management. By training Machine Learning (ML) and Deep Learning (DL) models on this dataset, it becomes possible to accurately classify banana varieties and evaluate their ripeness stages. These models can be utilized to design automated systems for determining ideal harvest times, establishing banana quality control standards, analyzing consumer preferences to guide product development and marketing strategies, and streamlining the supply chain through enhanced harvesting, sorting, packaging, and inventory management. Additionally, researchers focused on advancing Computer Vision technologies in food and agricultural sciences will find the dataset valuable for improving precision farming and food processing methods. As a result, the dataset offers substantial potential for automating banana production and processing, cutting labor costs, and boosting overall operational efficiency.

Note: This dataset is an updated version of "BananaImageBD: An Extensive Image Dataset of Common Bangladeshi Banana Varieties with Different Ripeness Levels", DOI: 10.17632/ptfscwtnyz.1
DOI: 10.18712/nsd-nsd2950-v2
title: Local Elections in Norway, 2019

description: The local election surveys were conducted the autumn of 2019. The purpose was to describe voter turnout, political orientation and attitudes towards political issues in the population. The local elections surveys are a collaboration between Statistics Norway and the Institute for Social Research (ISF).
DOI: 10.34820/fk2/gjbz0x
title: Banana Ripeness Image

description: Dataset merupakan dataset langsung dari sumbernya asli, terdiri dari tingkat kematangan pisang jenis Cavendish dan Ambon. Pisang Cavendish berkontribusi pada tingkat kematangan setengah matang, matang, dan terlalu matang. Pisang Ambon digunakan sebagai pengganti untuk tingkat kematangan mentah dan juga untuk menambahkan warna yang sesuai pada tingkat kematangan terlalu matang. Dataset terdiri dari 491 citra mentah, 606 citra setengah matang, 294 citra matang, dan 474 citra terlalu matang. Semua citra pada dataset memiliki ukuran (resolusi) 224 x 224 piksel. Dataset yang totalnya terdiri dari 1.865 citra dibagi menjadi dua bagian, yaitu 1.490 citra data latih dan 375 citra data test, masing-masing sesuai dengan 80% dan 20% dari total dataset.
DOI: 10.34892/6fwd-af11
title: The World Database on Protected Areas
title: WDPA

description: The World Database of Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management.

The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable.

Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets.

Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary.

The reach of the WDPA is further enhanced in services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.
DOI: 10.5281/zenodo.8233591
title: Project BioDyn: compilation of long-term (&gt;20yrs) community timeseries data from terrestrial and freshwater realms

description: The given dataset was compiled for n=2668 communities (242 study IDs in total, see the metadata file) across terrestrial and freshwater realms (terrestrial birds: n=1259, invertebrates: n=124, plants: n=435, and mammals: n=136, freshwater fish: n=587, invertebrates: n=112, and phytoplankton: n=15). Community timeseries include species-level abundance (or biomass in 15% of cases) and a minimum of 20 years sampled. We have compiled this data and used it for the first time to analyze how community stability and its drivers (species richness, overall synchrony, and tail-dependent synchrony) would differ across realms.
description: The authors thank David Vasseur for getting the requested data from a published article with no open data. We thank the ETH Board for funding through the Blue-Green Biodiversity (BGB) Initiative 2020, the Seehausen/Eawag lab and European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement No 787638) granted to Catherine H. Graham. We would like to thank the large number of scientists and volunteers that contributed to long-term data collection, and the data providers who chose to openly publish and share their datasets for continued analysis.
DOI: 10.5285/8a1800cc-b6a6-30ea-e053-6c86abc0c934
title: Size-fractionated iron measurements from surface sampling and depth profiles along 22N in the North Atlantic during summer 2017 on cruise JC150.

description: The distribution of size-fractionated iron in the subtropical North Atlantic was investigated along a meridional transect at 22deg N in summer 2017 to gain insights into the biogeochemical cycling of the different size classes of iron in the ocean. Samples for soluble, dissolved and total dissolvable were taken from onboard the R.R.S. James Cook (Cruise JC150) from the underway sampling system at 130 locations as well as well from full-depth CTD casts at seven stations. Iron concentrations were measured by flow injection analysis with chemiluminescence detection onboard and at the University of Southampton.
DOI: 10.6073/pasta/16f1a4e8d31297af7713e6cb3d70311b
title: Plant Community and Ecosystem Responses to Long-term Fertilization & Disturbance at the Kellogg Biological Station, Hickory Corners, MI  (1989 to 2014)
DOI: 10.6073/pasta/6fc6015c620056034512fde089d50c27
title: Cascade Project at North Temperate Lakes LTER Core Data Zooplankton 1984 - 2016

description: Zooplankton data from 1984-2016. Sampled approximately weekly with two net hauls through the water column (30 cm diameter net, 80 um mesh). There have been over eight zooplankton counters during this period, so species-level identifications (TAX, below) are not as consistent as those for some of the other datasets. Sampling Frequency: varies; Number of sites: 8
DOI: 10.6073/pasta/7b6df00de4d0fcecfd344c02de9f9c62
title: PVC02 Plant Species Composition on Selected Watersheds at Konza Prairie

description: Canopy coverage and frequency were recorded in 20 circular 10 sq m plots. Six treatments were sampled, three ungrazed and three to grazed by native grazers. In each case one of the three watersheds was unburned, another burned annually in April, the third burned every four years in April. In each treatment two soils were sampled: a lower-slope deep fertile nonrocky soil (tully silty clay loam), and a shallow rocky soil (florence cherty silt loam) on level to gently sloping ridges. In 1983 another ungrazed annual burn area (1c) was added (both tully and florence soils) because original area (1d) appeared aberrant.
DOI: 10.6073/pasta/83c888854e239a79597999895bb61cfe
title: Long Term Mammal Data from Powdermill Biological Station 1979-1999

description: This is a 20-year record of small mammal trapping from the Powdermill Biological Station, Rector, PA 15677 collected by Joseph F. Merritt. It is included here as a comparative source of small mammal data.
DOI: 10.6073/pasta/a0d7c18521ca6bc45a485048a05050aa
title: North Temperate Lakes LTER: Zooplankton - Trout Lake Area 1982 - current
DOI: 10.6073/pasta/c1d40e9d0ec610bb74d02741e9d22576
title: Long-Term Core Site Grasshopper Dynamics for the Sevilleta National Wildlife Refuge, New Mexico (1992-2013)

description: Grasshoppers are important animals in semi-arid environments, both as herbivores and as food resources for higher level consumer animals. Grasshoppers tend to be numerous and represented by many species in semi-arid environments, especially in desert grasslands. Grasshopper species range from environmental specialists, to environmental generalists. Grasshopper populations tend to change considerably from year to year, often in response to annual variation in rainfall and plant production. The purpose of this study was to monitor grasshopper species composition and abundance over a period of many years from black grama grassland, blue grama grassland, creosotebush shrubland, and pinyon/juniper woodland environments at the Sevilleta, in relation to seasonal and annual variation in precipitation and plant production. Data were collected for all individual species to provide information on community dynamics as well as population dynamics. The working research hypothesis for this study was that grasshopper populations in all environments will correlate positively to seasonal and annual variation in precipitation and plant production. Spring grasshopper populations will be especially high during El Nino years, and late summer populations especially high during La Nina years. This study was initiated in 1992, and is ongoing to the present time.
DOI: 10.6084/m9.figshare.26404867
title: Data and Codes from 'Microbial photosynthesis mitigates carbon loss from northern peatlands under warming'

description: This file contains the main data used in the manuscript Microbial photosynthesis mitigates carbon loss from northern peatlands under climate warming. 

R codes and GEE codes used to make the main figures are also provided. 

Please read the READ_ME file for details.
DOI: 10.13120/qac0-1s48
title: E2M3A-2019-2020-Water-Column-time-series- SouthAdriatic 

description: Hourly time series of temperature, salinity and currents at the EMSO South Adriatic Facility. Temperature and salinity data were collected at 150 db, 350 db, 550 db, 750 db, 900 db, 1000 db and 1200 db at mooring E3M3A in the southern Adriatic Pit.
DOI: 10.13120/s046-ja62
title: E2M3A-2021-2022-Water-Column-time-series- SouthAdriatic

description: Hourly time series of temperature, salinity and currents at the EMSO South Adriatic Facility. Temperature and salinity data were collected at 150 db, 350 db, 750 db, 1000 db and 1200 db at mooring E3M3A in the southern Adriatic Pit.
DOI: 10.1594/pangaea.772244
title: Physical oceanography during POLARSTERN cruise ANT-XXVII/2
DOI: 10.1594/pangaea.854656
title: Annotated record of the detailed examination of the analysed Mn nodules from the Challenger expedition

description: From 1983 until 1989 NOAA-NCEI compiled the NOAA-MMS Marine Minerals Geochemical Database from journal articles, technical reports and unpublished sources from other institutions. At the time it was the most extended data compilation on ferromanganese deposits world wide. Initially published in a proprietary format incompatible with present day standards it was jointly decided by AWI and NOAA to transcribe this legacy data into PANGAEA. This transfer is augmented by a careful checking of the original sources when available and the encoding of ancillary information (sample description, method of analysis...) not present in the NOAA-MMS database.
DOI: 10.1594/wdcc/cmip5.mxelhi
title: CMIP5 simulations of the Max Planck Institute for Meteorology (MPI-M) based on the MPI-ESM-LR model: The historical experiment, served by ESGF

description: Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: 'historical' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 3.2 historical (3.2 Historical) - Version 1: Simulation of recent past (1850 to 2005). Impose changing conditions (consistent with observations). Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.
DOI: 10.17189/1414249
title: MAVEN Tabulated vector magnetic field vs. time in payload coordinates Data Collection

description: A collection of vector magnetic field data acquired by the Fluxgate Magnetimeter instrument aboard the MAVEN spacecraft. The data are calibrated and provided in physical units (nT). The time resolution depends on the telemetry rate available when the data were taken. The data are expressed in Payload coordinates.
DOI: 10.17632/5r29833mxz.1
title: Medical tourist dataset

description: We explored the role of voice of medicine of doctors in relation to their cognitive, behavioural and emotional Cultural intelligence and how it mediated the relationship between the perceived self-efficacy and build-up of patient experiences in the initial clinical interactions in the host country hospitalThe research model was tested in a large Joint Commission International (JCI) and National Accreditation Board for Hospitals & Healthcare provider (NABH) accredited, multi-speciality health care hospital located in the National Capital Region (NCR) of New Delhi, India (N=300) The hospital’s research ethics committee approved the study particularly concerning data confidentiality
DOI: 10.17632/7fhwrgfh2s.3
title: Dataset for High-Entropy Alloys Phases

description: Ronald Machaka, Glenda T. Motsi, Lerato M. Raganya, Precious M. Radingoana, Silethelwe Chikosha,
Machine learning-based prediction of phases in high-entropy alloys: A data article,
Data in Brief,
2021,
107346,
ISSN 2352-3409,
https://doi.org/10.1016/j.dib.2021.107346.
(https://www.sciencedirect.com/science/article/pii/S2352340921006302)
Abstract: ABSTRACT
A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1]. The data associated with that research paper, titled “Machine learning-based prediction of phases in high-entropy alloys”, are presented in this data article. This dataset is a systematic documentation and comprehensive survey of experimentally reported HEA microstructures. It contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles. The dataset is provided with this article as a supplementary file. Since the dataset was collected from experimental peer-reviewed articles, these data can provide insights into the microstructural characteristics of HEAs, can be used to improve the optimization HEA phases, and have an important role in machine learning, material informatics, as well as in other fields.
Keywords: High entropy alloys; HEA microstructures; phases; machine learning; deep learning; material informatics

-------------------
The dataset contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles. 

Secondary data (i.e. composition-specific features, alloy processing and post-processing parameters, and the resulting phases) were collected. Some typical empirical HEA design parameters were calculated using known methods. Data was processed using Excel and R, a language and environment for statistical computing, for purposes of visualization and data analysis.
DOI: 10.17632/hb3whw3nfr.1
title: Accompanying data to "Modulation of the Northern polar vortex by the Hunga Tonga-Hunga Ha'apai eruption and associated surface response"

description: These datasets represent already postprocessed model and other data outputs in order to reproduce figures in the manuscript.
DOI: 10.21227/45m9-9p82
title: Boğaziçi University DDoS Dataset

description: Boğaziçi University DDoS dataset (BOUN DDoS) is generated in Boğaziçi University via Hping3 traffic generator software by flooding TCP SYN, and UDP packets. This dataset includes attack-free user traffic as well as attack traffic and suitable for evaluating network-based DDoS detection methods. Attacks are towards one victim server connected to the backbone router of the campus. Attack packets have randomly generated spoofed source IP addresses. The data-trace was recorded on the backbone and included over 2000 active hosts. The average packet/second ratio was around 18000.