cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4803352
title: EMHIRES dataset: wind and solar power generation [archived]

description: <strong>This repository is now archived. The official repository for the EMHIRES dataset is now EMHIRES dataset: wind and solar power generation | Zenodo</strong> <strong>EMHIRES Wind</strong> The first version of EMHIRES dataset releases four different files about the wind power generation hourly time series during 30 years (1986-2015), taking into account the existing wind fleet at the end of 2015, for each country (onshore and offshore), bidding zone and by NUTS 1 and NUTS 2 region. The time series are given as capacity factors. The installed capacity used accounted for calculating the capacity factors are summarised in the annexes of the report. - https://setis.ec.europa.eu/emhires-dataset-part-i-wind-power-generation_en <strong>EMHIRES Solar</strong> EMHIRES provides RES-E generation time series for the EU-28 and neighbouring countries. The solar power time series are released at hourly granularity and at different aggregation levels: by country, power market bidding zone, and by the European Nomenclature of territorial units for statistics (NUTS) defined by EUROSTAT; in particular, by NUTS 1 and NUTS 2 level. The time series provided by bidding zones include special aggregations to reflect the power market reality where this deviates from political or territorial boundaries. The overall scope of EMHIRES is to allow users to assess the impact of meteorological and climate variability on the generation of solar power in Europe and not to mime the actual evolution of solar power production in the latest decades. For this reason, the hourly solar power generation time series are released for meteorological conditions of the years 1986-2015 (30 years) without considering any changes in the solar installed capacity. Thus, the installed capacity considered is fixed as the one installed at the end of 2015. For this reason, data from EMHIRES should not be compared with actual power generation data other than referring to the reference year 2015. - https://setis.ec.europa.eu/emhires-dataset-part-ii-solar-power-generation_en
DOI: 10.5281/zenodo.4803353
title: EMHIRES dataset: wind and solar power generation [archived]

description: <strong>This repository is now archived. The official repository for the EMHIRES dataset is now EMHIRES dataset: wind and solar power generation | Zenodo</strong> <strong>EMHIRES Wind</strong> The first version of EMHIRES dataset releases four different files about the wind power generation hourly time series during 30 years (1986-2015), taking into account the existing wind fleet at the end of 2015, for each country (onshore and offshore), bidding zone and by NUTS 1 and NUTS 2 region. The time series are given as capacity factors. The installed capacity used accounted for calculating the capacity factors are summarised in the annexes of the report. - https://setis.ec.europa.eu/emhires-dataset-part-i-wind-power-generation_en <strong>EMHIRES Solar</strong> EMHIRES provides RES-E generation time series for the EU-28 and neighbouring countries. The solar power time series are released at hourly granularity and at different aggregation levels: by country, power market bidding zone, and by the European Nomenclature of territorial units for statistics (NUTS) defined by EUROSTAT; in particular, by NUTS 1 and NUTS 2 level. The time series provided by bidding zones include special aggregations to reflect the power market reality where this deviates from political or territorial boundaries. The overall scope of EMHIRES is to allow users to assess the impact of meteorological and climate variability on the generation of solar power in Europe and not to mime the actual evolution of solar power production in the latest decades. For this reason, the hourly solar power generation time series are released for meteorological conditions of the years 1986-2015 (30 years) without considering any changes in the solar installed capacity. Thus, the installed capacity considered is fixed as the one installed at the end of 2015. For this reason, data from EMHIRES should not be compared with actual power generation data other than referring to the reference year 2015. - https://setis.ec.europa.eu/emhires-dataset-part-ii-solar-power-generation_en
DOI: 10.5519/qd.iob39u8l
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10940 records)
DOI: 10.34740/kaggle/dsv/6464760
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.24132422.v1
title: Random Fixed Boundary Flows

description: We consider fixed boundary flows with canonical interpretability as principal components extended on non-linear Riemannian manifolds. We aim to find a flow with fixed starting and ending points for noisy multivariate data sets lying near an embedded non-linear Riemannian manifold. In geometric terms, the fixed boundary flow is defined as an optimal curve that moves in the data cloud with two fixed end points. At any point on the flow, we maximize the inner product of the vector field, which is calculated locally, and the tangent vector of the flow. The rigorous definition is derived from an optimization problem using the intrinsic metric on the manifolds. For random data sets, we name the fixed boundary flow the random fixed boundary flow and analyze its limiting behavior under noisy observed samples. We construct a high-level algorithm to compute the random fixed boundary flow, and provide the convergence of the algorithm. We show that the fixed boundary flow yields a concatenate of three segments, one of which coincides with the usual principal flow when the manifold is reduced to the Euclidean space. We further prove that the random fixed boundary flow converges largely to the population fixed boundary flow with high probability. Finally, we illustrate how the random fixed boundary flow can be used and interpreted, and demonstrate its application in real data sets.
DOI: 10.6084/m9.figshare.24132422
title: Random Fixed Boundary Flows

description: We consider fixed boundary flows with canonical interpretability as principal components extended on non-linear Riemannian manifolds. We aim to find a flow with fixed starting and ending points for noisy multivariate data sets lying near an embedded non-linear Riemannian manifold. In geometric terms, the fixed boundary flow is defined as an optimal curve that moves in the data cloud with two fixed end points. At any point on the flow, we maximize the inner product of the vector field, which is calculated locally, and the tangent vector of the flow. The rigorous definition is derived from an optimization problem using the intrinsic metric on the manifolds. For random data sets, we name the fixed boundary flow the random fixed boundary flow and analyze its limiting behavior under noisy observed samples. We construct a high-level algorithm to compute the random fixed boundary flow, and provide the convergence of the algorithm. We show that the fixed boundary flow yields a concatenate of three segments, one of which coincides with the usual principal flow when the manifold is reduced to the Euclidean space. We further prove that the random fixed boundary flow converges largely to the population fixed boundary flow with high probability. Finally, we illustrate how the random fixed boundary flow can be used and interpreted, and demonstrate its application in real data sets.
DOI: 10.4121/dc5a7619-3c99-4d12-886e-174a6a3abe51
title: Data underlying MSc Thesis: Telerehabilitation of a Exoskeleton-haptic device System

description: Position, Orientation, Force, Torque, and timestamps are saved as .csv (saved on both computers running the system) from the 12 sets of experiments (2 users, 12 tasks)Used for validation of Stability & Transparency
DOI: 10.4121/dc5a7619-3c99-4d12-886e-174a6a3abe51.v1
title: Data underlying MSc Thesis: Telerehabilitation of a Exoskeleton-haptic device System

description: Position, Orientation, Force, Torque, and timestamps are saved as .csv (saved on both computers running the system) from the 12 sets of experiments (2 users, 12 tasks)Used for validation of Stability & Transparency
DOI: 10.4121/21431427
title: Dataset accompanying the publication: Morphodynamic modeling of tidal basins: the role of sand-mud interaction

description: This dataset accompanies the manuscript: "Morphodynamic modeling of tidal basins: the role of sand-mud interaction".<br>It contains input and output files of a morphodynamic model of a schematised tidal basin, with 20 different scenrarios with varying sand-mud interaction settings and mud erodibility settings.
DOI: 10.4121/21431427.v3
title: Dataset accompanying the publication: Morphodynamic modeling of tidal basins: the role of sand-mud interaction

description: This dataset accompanies the manuscript: "Morphodynamic modeling of tidal basins: the role of sand-mud interaction".<br>It contains input and output files of a morphodynamic model of a schematised tidal basin, with 20 different scenrarios with varying sand-mud interaction settings and mud erodibility settings.
DOI: 10.6084/m9.figshare.23609643.v1
title: dataset_labels.csv

description: Dataset labels consist of : Normal (1), Pre-Plus (2) and Plus (3)
DOI: 10.6084/m9.figshare.24132423.v1
title: Dynamic characterization and interpretation for protein–RNA interactions across diverse cellular conditions using HDRNet

description: We collected 261 RNA binding site datasets from https://www.nature.com/articles/s41422-021-00476-y for 172 RBPs constructed using the same flag-marked technique in K562, HepG2, HEK293, HEK293T, HeLa and H9 cell lines from the ENCODE project and POSTAR. Of these datasets, there are 65 CLLP-seq datasets for 61 RBPs from POSTAR and 196 eCLIP datasets for 111 RBPs from the ENCODE project. In terms of the RBP binding data in tissues, the processed MBNL2 (Muscleblind Like Splicing Regulator 2) binding peak data in human brain tissues (GSE68890) were curated from POSTAR; the DGCR8 and HNRNPU binding data in human adrenal gland were collected from ENCODE; and the MBNL1 (Muscleblind Like Splicing Regulator 1) binding data in brain, heart, muscle, and myoblasts from mice were obtained from (GSE39911). These datasets were quality controlled and processed using a uniform pipeline to ensure consistent results.
DOI: 10.5281/zenodo.8329558
title: COP26_4.6.9_Data

description: MAgPIE data for reproducibility of baseline and COP26 scenario <br> Emission savings through the COP26’s declaration of deforestation could come at the expense of non-forest land conversion Contact: Dr. Abhijeet Mishra<br> A.Mishra@cgiar.org / mishra@pik-potsdam.de<br> September 2023
DOI: 10.5281/zenodo.8329557
title: COP26_4.6.9_Data

description: MAgPIE data for reproducibility of baseline and COP26 scenario <br> Emission savings through the COP26’s declaration of deforestation could come at the expense of non-forest land conversion Contact: Dr. Abhijeet Mishra<br> A.Mishra@cgiar.org / mishra@pik-potsdam.de<br> September 2023
DOI: 10.5281/zenodo.8341247
title: ormyrus_thymus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8341246
title: ormyrus_thymus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8321817
title: Lightning-Classification-Dataset-8000

description: 8000 waveforms with pictures including RS, PB, NB and IC. 2000 for each. Stored in .pkl format. Please use pickle in python to use it. Each row has 10000 datapoints and 1 label type.
DOI: 10.5519/qd.meohrjf9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (45079 records)
DOI: 10.6084/m9.figshare.23921238.v2
title: The output files of MEANGS when assembling mitogenome of five marine organisms

description: The output files of MEANGS when assembling mitogenome of five marine organisms including the white-spotted bamboo shark (<i>Chiloscyllium plagiosum</i>), ocellate spot skate (<i>Okamejei kenojei</i>), brown-spotted flathead (<i>Platycephalus </i>sp.1), crocodile flathead (<i>Cociella crocodilus</i>) and spotted seal (<i>Phoca largha</i>).
DOI: 10.5281/zenodo.8341290
title: sycophila_texana_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8341289
title: sycophila_texana_spades_01.fasta.gz

description: .
DOI: 10.5066/f72806w4
title: Globorotalia truncatulinoides Sediment Trap Data in the Gulf of Mexico

description: Modern observations of planktic foraminifera from sediment trap studies help to constrain the regional ecology of paleoceanographically valuable species. Results from a weekly-resolved sediment trap time series (2008-2014) in the northern Gulf of Mexico demonstrate that 92% of Globorotalia truncatulinoides flux occurs in winter (January, February, and March), and that encrusted and non-encrusted individuals represent calcification in distinct depth habitats. We use individual foraminiferal analysis (IFA) of G. truncatulinoides tests to investigate differences in the elemental (Mg/Ca) and isotopic composition (delta 18O and delta 13C) of the encrusted and non-encrusted ontogenetic forms of G. truncatulinoides, and to estimate their calcification depth in the northern Gulf of Mexico. For further information regarding data collection and/or processing methods as well as the associated published manuscript refer to Reynolds, C.E., Richey, J.N., Fehrenbacher, J.S., Rosenheim, B.E., and Spero, H.J., 2018, Environmental controls on the geochemistry of Globorotalia truncatulinoides in the Gulf of Mexico-Implications for paleoceanographic reconstructions: Marine Micropaleontology, https://doi.org/10.1016/j.marmicro.2018.05.006. *Sediment trap foraminifera flux were incorrectly calculated per 0.5 m2 area instead of 1 m2 in this version of the dataset. Corrected data has been provided in a new data release, https://doi.org/10.5066/P9O76R8R.
DOI: 10.34740/kaggle/dsv/6457847
title: Rice Seed image dataset from Pakistan
DOI: 10.6084/m9.figshare.24132462
title: Dataset

description: data chirps
DOI: 10.6084/m9.figshare.24132462.v1
title: Dataset

description: data chirps
DOI: 10.57745/heenwa
title: template_senso.tab

description: :unav
DOI: 10.6084/m9.figshare.23921238.v3
title: The output files of MEANGS when assembling mitogenome of three marine fish

description: The output files of MEANGS when assembling mitogenome of three marine fish species including the white-spotted bamboo shark (<i>Chiloscyllium plagiosum</i>), ocellate spot skate (<i>Okamejei kenojei</i>) and brown-spotted flathead (<i>Platycephalus </i>sp.1). The second level folders are named with the '-n' parameter and the 'mito_cliped.fas' files are the final assemblies of three species, according to the first level folder name.
DOI: 10.5281/zenodo.8341322
title: ormyrus_labotus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8341321
title: ormyrus_labotus_spades_01.fasta.gz

description: .
DOI: 10.14469/hpc/7846
title: MALDI-ToF spectrum

description: Visualisation of a MALDI-ToF spectrum from a csv file using Plotly
DOI: 10.23729/b9adb0a2-7381-45db-b32f-7e78ae1bc9e3
title: Smart Campus Oulu indoor climate, air-quality and motion
title: Smart Campus Oulu sisätilojen ilmanlaatu

description: Please always refer to up-to-date documentation of this release, available at https://smartcampus.oulu.fi/open-data --- Indoor air-quality, presence and light measurements from the University of Oulu Linnanmaa-campus. Collected by wireless low-power sensors connected to the 5GTN LoRa-network. The dataset begins on 1.7.2020, this initial release containing data until 1.6.2021. Later the set may be appended as new data is released. ## Quantities The following indoor environment and air-quality measurements are collected: - Air temperature [°C] - Relative humidity [% RH] - Light (linear index) - Passive infrared (linear index, pir) - CO2 [ppm] - Battery voltage [V] - Average SPL [dB] - Peak SPL[dB] - Soil moisture [%] - Atmospheric pressure [bar] - Acceleration (X, Y, Z) [g] ## Map of deployment An overview of the sensor deployment is available at https://smartcampus.oulu.fi/manage ## README Short introduction to the dataset is included with the data itself in `README.md`. Please always refer to up-to-date documentation of this release, available at https://smartcampus.oulu.fi/open-data ## .csv-tables The data is split into two tables, 'application' and 'lora', the former containing a numeric representation the physical quantities under measurement, and the latter containing values exposed by the LoRa-gateway, including metrics such as signal strength. The time format of the 'time'-column is epoch milliseconds. ## Sensor metadata devices.json contains sensor metadata, such geolocation and description of installation location. The jsonlines-format is used. ## Issue: missing .csv headers *Affecting*: release-2021062800 *Fixed by*: release-2021062801 On 22.3.2022 it was brought to our attention that the initial release is missing .csv headers. A fix was implemented by releasing this data again, now including the expected headers.
DOI: 10.5281/zenodo.8341235
title: Hydrogen Transport and Evolution in Ni-MH Batteries by Neutron Imaging

description: Dataset to "Hydrogen Transport and Evolution in Ni-MH Batteries by Neutron Imaging"
DOI: 10.5281/zenodo.8341234
title: Hydrogen Transport and Evolution in Ni-MH Batteries by Neutron Imaging

description: Dataset to "Hydrogen Transport and Evolution in Ni-MH Batteries by Neutron Imaging"
DOI: 10.6084/m9.figshare.24132540
title: Structures of FLT3 with inhibitors

description: PDB files of models after energy minimisation
DOI: 10.6084/m9.figshare.24132540.v1
title: Structures of FLT3 with inhibitors

description: PDB files of models after energy minimisation
DOI: 10.6084/m9.figshare.24132531
title: TEST

description: data chirps
DOI: 10.6084/m9.figshare.24132531.v1
title: TEST

description: data chirps
DOI: 10.6084/m9.figshare.19412468
title: ABM results from 1500 simulations

description: Results from independent runs of the stochastic simulations under different treatments and cell-number groups. <br>
DOI: 10.6084/m9.figshare.19412468.v1
title: ABM results from 1500 simulations

description: Results from independent runs of the stochastic simulations under different treatments and cell-number groups. <br>
DOI: 10.6084/m9.figshare.24132558
title: Scatter diagram between the sea level anomalies (SLA) and instabilities

description: Scatter diagram between the sea level anomalies (SLA) averaged between 120°W-160°W and 2.5°N-2.5°S obtained from the CR simulation and the difference (CR-LIN) of EKE, PeKe and KmKe.
DOI: 10.6084/m9.figshare.24132558.v1
title: Scatter diagram between the sea level anomalies (SLA) and instabilities

description: Scatter diagram between the sea level anomalies (SLA) averaged between 120°W-160°W and 2.5°N-2.5°S obtained from the CR simulation and the difference (CR-LIN) of EKE, PeKe and KmKe.
DOI: 10.5281/zenodo.8341361
title: Negara Asal Penulis Topik Impoliteness

description: Data ini berasal dari Scopus yang diunduh bulan Juli 2023 dengan string pencarian "impoliteness" pada title
DOI: 10.5281/zenodo.8341360
title: Negara Asal Penulis Topik Impoliteness

description: Data ini berasal dari Scopus yang diunduh bulan Juli 2023 dengan string pencarian "impoliteness" pada title
DOI: 10.5258/soton/d2698
title: Experimental data for the journal article: The effects of simulated hydropower turbine rapid decompression on two Neotropical fish species.

description: Experimental data for research: The effects of simulated hydropower turbine rapid decompression on two Neotropical fish species.
DOI: 10.34740/kaggle/dsv/6465154
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.57745/fmvag8
title: temporal-sensory-evaluation-methods-review.xlsx

description: :unav
DOI: 10.5258/soton/d2775
title: Dataset in support of the Southampton doctoral thesis 'Psychological outcomes and the role of compassion in men who have experienced a betrayal'

description: This dataset contains: Quantitative data on psychological outcome measures in 52 betrayed men for baseline, pre, post, and follow-up time points. This data was collected over 8 months between September 2022 and April 2023 using the online survey tool Qualtrics XM. The IBM Software SPSS will be needed to view the data output which was created using SPSS Version 28
DOI: 10.5281/zenodo.8341396
title: sycophila_quercilanae_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8341395
title: sycophila_quercilanae_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.24132585
title: Materials to compare the clinical efficacy of RRH and LRH for cervical cancer.zip

description: This document includes the literature search records, the included literature (pdf), the extracted data tables, the original records of the data analysis, the characteristics of the included studies, and the assessment of the quality of the included studies.The demographic data for included studies included study design, sample size, mean age, mean BMl, FIGO stage, mean tumor size, and follow-up period. The following results were compared by: (1) perioperative results: operation time, blood loss, blood transfusion rate, complication rate, length of hospitalization; and (2) survival results: disease-free survival, recurrence, and mortality. Specific complications were also summarized.
DOI: 10.5281/zenodo.7766602
title: All Data for "Transport properties and doping evolution of the Fermi surface in cuprates"

description: All data pertaining to the publication "Transport properties and doping evolution of the Fermi surface in cuprates". This includes data analysis software (jupyter notebooks), custom program code, literature data, handwritten notes and the manuscript. This is straight from the hard drive, not particularly well ordered/prepared.
DOI: 10.5281/zenodo.7766603
title: All Data for "Transport properties and doping evolution of the Fermi surface in cuprates"

description: All data pertaining to the publication "Transport properties and doping evolution of the Fermi surface in cuprates". This includes data analysis software (jupyter notebooks), custom program code, literature data, handwritten notes and the manuscript. This is straight from the hard drive, not particularly well ordered/prepared.
DOI: 10.5281/zenodo.8095497
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.