cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/cdzx7kxczn.3
title: Innovative Intellectual Property Rights in the Marine Seed Industry

description: Our research employs the Group Eigenvalue Method (GEM) to identify key indicators, aiming to enhance the accuracy of value assessment. The principal advantage of GEM lies in its ability to effectively overcome the inconsistency issues common in the judgement matrices of the Analytic Hierarchy Process (AHP). Moreover, it offers simplicity in data processing (as used in method analysis). This method is applicable not only to quantitative issues but also to the analysis of qualitative problems.
For the research problem addressed in this paper, staff from companies and research institutions in the marine seed industry technology innovation field were selected as experts to score the preliminary screening indicators. The ten invited experts hail from prestigious institutions including Shandong Marine Limited Company, Shandong New Sunshine Seed Industry Science and Technology Limited Company, Tongji University, Xiamen University, the First Institute of Natural Resources Ministry, and the Chinese Academy of Sciences' Institute of Oceanography. The expert consultation process was conducted in two phases: the first round involved distributing questionnaires to the experts who assessed each indicator based on their knowledge and experience to collect baseline data. The scoring was designed according to the Likert 7-point scale method, as shown in Table 2. The second round involved detailed processing of the collected data; through further consultation with the experts, we discussed the threshold setting standards for each sub-indicator system and sought expert advice on retaining, eliminating, or merging indicators. 
 Although the GEM has been applied to critically screen the indicators for the valuation of intellectual property in marine seed industry innovation, the selected indicators may still exhibit some degree of correlation. According to the principle of diversity in the design of the assessment indicator system, excessive correlation among indicators can reduce the precision and practicality of the evaluation results. Therefore, maintaining an appropriate level of independence among indicators is crucial for constructing a scientifically sound and rational assessment system. By employing Pearson correlation analysis, it is possible to effectively identify highly correlated indicators, which often provide overlapping information. Consequently, merging or eliminating these highly correlated indicators from the assessment system is an essential step in ensuring the effectiveness and accuracy of the indicator system. Through this approach, we ensure the independence and scientific integrity of the assessment indicator system, enhancing the reliability and utility of the overall evaluation model.
DOI: 10.17632/sg3k49x26p
title: Two-sex life table of Zeugodacus cucurbitae and Zeugodacus tau

description: Growth and development of Zeugodacus cucurbitae and Zeugodacus tau
DOI: 10.17632/sg3k49x26p.1
title: Two-sex life table of Zeugodacus cucurbitae and Zeugodacus tau

description: Growth and development of Zeugodacus cucurbitae and Zeugodacus tau
DOI: 10.15468/dl.u5vxpm
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Sylvacaecilia grandisonae (Taylor, 1970)"
 ]
}

 The dataset includes 20 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007672-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qvgbyh
title: Occurrence Download

description: A dataset listing the 4062640 species recorded in GBIF matching the query:
{ }

 The dataset's 4062640 records were derived from 72207 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007665-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.882465
title: Radiosonde measurements from station Barrow (2006-10)

description: Start location: Barrow, Alaska, USA; Distance from radiation site: 7 km
DOI: 10.5281/zenodo.4569093
title: Eukaryotic virome of Belgian mosquitoes

description: De novo assembled viral sequences &gt;1kb from the eukaryotic virome of Belgian mosquitoes caught in 2019.
DOI: 10.5281/zenodo.3820610
title: alendronate_CRISPRa

description: alendronate CRISPRa screen results
DOI: 10.5281/zenodo.4399441
title: cervical cancer dataset

description: cervical cancer dataset
DOI: 10.5281/zenodo.1160409
title: Newcastle Polysomnography And Accelerometer Data

description: # Newcastle PSG+Accelerometer study 2015

This data set contains 55 .bin files, 28 .txt files, and one .csv file,<br>
which were collected in Newcastle upon Tyne (UK) to evaluate an <br>
accelerometer-based algorithm for sleep classification.<br>
The data come form a a single night polysomnography recording in <br>
28 sleep clinic patients. A description of the experimental <br>
protocol can be found in this open access PLoSONE paper from 2015: <br>
https://doi.org/10.1371/journal.pone.0142533.

 

## Polysomnography

Sleep scores derived from polysomnography are stored in the .txt files. <br>
Each file represents a time series (one night) of one participant.<br>
The resolution of the scoring is <br>
30 seconds. Participants are numbered. The participant number is <br>
included in the file names as “mecsleep01_...”. pariticpants_info.csv is a<br>
dictionary of participant number, diagnosis, age, and sex.

## Accelerometer data

Accelerometer data from brand GENEActiv (https://www.activinsights.com) are <br>
stored in .bin files. Per participant two accelerometers were used: <br>
One accelerometer on each wrist (left and right). The right wrist from <br>
participant 10 is missing, hence the total number of 55 bin files. <br>
The tri-axial (three axis) accelerometers were configured to record <br>
at 85.7 Hertz. The accelerometer data can be read with R package <br>
GENEAread https://cran.r-project.org/web/packages/GENEAread/index.html. <br>
Additional information on the accelerometer can be found on the <br>
manufacturers product website: <br>
https://www.activinsights.com/resources-support/geneactiv/downloads-software/, <br>
including a description of the binary file structure on page 27 of <br>
this (pdf) file: https://49wvycy00mv416l561vrj345-wpengine.netdna-ssl.com/wp-content/uploads/2014/03/geneactiv_instruction_manual_v1.2.pdf.<br>
The participant number and the body side on which the accelerometer <br>
is worn are included in the file names as “MECSLEEP01_left wrist...”.

## Participant information

The .csv file as included in this dataset contains a dictionary <br>
of the participant numbers, sleep disorder diagnosis, <br>
participant age at the time of measurement, and sex.

## Example processing

The code we used ourselves to process this data can be found in this <br>
GitHub repository: https://github.com/wadpac/psg-ncl-acc-spt-detection-eval.<br>
Note that we use R package GGIR: https://cran.r-project.org/web/packages/GGIR/, <br>
which calls R package GENEAread for reading the binary data.
description: ## Questions and citation

For questions about this data set please contact v.vanhees@esciencecenter.nl. 
Please cite this dataset with the doi as provided on the zenodo page where 
you downloaded this dataset.
DOI: 10.1594/pangaea.610314
title: Documentation of sediment core PS69/852-1
DOI: 10.5281/zenodo.5590603
title: Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

description: This dataset is a subset of the MIMIC-III dataset used for non-invasive blood pressure prediction. PPG and ABP data were divided into windows of 7s length (875 data points). Systolic and diastolic blood pressure values were derived from the ABP windows. Each sample of the dataset consists of a PPG signal and blood pressure values as well as a unique subject identifier. The file consists of three datasets: PPG: PPG data of size 905,400 x 875 label: BP data of size 905,400 x 2 subject_idx: subject affiliation of each sample (size 905,400 x 1) Furthermore, this submission contains the following models: AlexNet ResNet50 LSTM Architecture published by Slapnicar et al. 2019 The architectures were trained using a non-mixed dataset derived from the MIMIC-III waveform database. Samples were divided between training, validation and test set based on their subject affiliation preventing contamination of validation and test sets with samples from subjects used for training.
DOI: 10.5281/zenodo.6366957
title: Supplemental data and code for Improved air quality in China can enhance solar power performance and accelerate carbon neutrality targets

description: Supplemental data and code for Improved air quality in China can enhance solar power performance and accelerate carbon neutrality targets
DOI: 10.5281/zenodo.3958315
title: Gradients1-KOK1606_14C-NPP_2020-03-04_V1.3

description: Cruise Name: KOK1606, Gradients 1.0. Project: Simons Foundation - Gradients NPSG Standard HOT protocols were used to measure in vitro primary productivity (PP) via the 14C radiotracer method [Karl et al., 1996]. Water samples were collected from 15m before dawn and incubated on deck in incubators screened to ~50% of surface irradiance and plumbed with flow through surface seawater for temperature regulation. Duplicate 500-ml bottles were incubated in the incubator for ~12hr and 24hr, light bottles were paired with dark bottles. Parallel samples were taken in duplicate to examine the influence of reduced light on PP for all stations. Duplicate 500-ml bottles were incubated inside mesh bags that reduced the light availability to ~25%. A time zero sample was taken at each station to ensure acidification was complete. Following dawn to dusk or dawn to dawn incubations, each sample was filtered onto a 25mm glass fiber filter. Filters were acidified with 1 ml of 2N hydrochloric acid in a fume hood for 12 hr to remove inorganic 14C after which 10 ml of Ultima Gold scintillation fluid was added to each sample. Radioactivity was determined using a Perkins Elmer 2600 liquid scintillation counter. The rate of PP was calculated by multiplying the percent of carbon labeled by the total carbon available for photosynthesis (e.g. dissolved inorganic carbon, DIC, calculated from SST and salinity measured at each station as per Lee et al. [2006]) and dividing by incubation time (t = 1 day). Rate measurements in light bottles were corrected to remove the rate measured in parallel dark bottles. Abbreviations for rate measurements are in the variable metadata. All rates are in units mg C m-3; ‘stdev’ refers to the standard deviation of duplicates for each incubation type, whereas ‘stdev-p’ refers to the propagated standard deviation. NaN’s represent stations or samples where rate measurements were not conducted.
DOI: 10.5281/zenodo.6780819
title: Open Satellite Video Single Target Tracking Datasets (OpenSatSTTD)

description: We collect the latest open-source datasets for satellite video single target tracking (SatSTT) and launch the OpenSatSTTD project to promote the sharing of the latest research datasets in the SatSTT field. Satellite videos in the OpenSatSTTD project are collected from different sensors and platforms, and four targets (i.e., vehicles, trains, airplanes and vessels) are annotated by oriented bounding boxes. Users can obtain all satellite videos in the OpenSatSTTD project from links in the files. Source: Zheng, Ying., Zhu, Q., Luo, J., Li, Z., Lin, Z., Huang, X., and Zhang L.: Single Target Tracking in High-Resolution Satellite Videos: A Comprehensive Review (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6780820, 2022.
DOI: 10.5281/zenodo.4708298
title: Data set for The influence of hydropower and coal consumption on green-house gas emissions: a comparison between China and India

description: Dataset.
DOI: 10.15468/dl.33v8xn
title: Occurrence Download

description: A dataset containing 6448036 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pica pica (Linnaeus, 1758)"
 ]
}

 The dataset includes 6448036 records from 5488 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048211-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r2awt5
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((13.27572 55.68802, 13.31529 55.64814, 13.34814 55.63626, 13.3529 55.61776, 13.33232 55.61229, 13.31542 55.62221, 13.30362 55.60639, 13.34314 55.55803, 13.36993 55.56285, 13.39246 55.53391, 13.45003 55.52777, 13.47397 55.52692, 13.48892 55.54821, 13.49454 55.57524, 13.51832 55.58236, 13.54047 55.63272, 13.59667 55.63678, 13.60329 55.65271, 13.61363 55.66865, 13.5608 55.69821, 13.52745 55.70303, 13.45895 55.73272, 13.42704 55.73407, 13.38381 55.74697, 13.32742 55.75445, 13.29833 55.75102, 13.30294 55.76854, 13.28785 55.78057, 13.25218 55.77391, 13.22762 55.79063, 13.21336 55.79196, 13.14345 55.78734, 13.13535 55.74825, 13.12902 55.72418, 13.11222 55.71342, 13.11544 55.70357, 13.14513 55.70636, 13.17349 55.68175, 13.20437 55.69265, 13.21293 55.67843, 13.27572 55.68802))",
 "ScientificName is one of (Lycaenidae, Hesperiidae, Nymphalidae, Papilionidae, Pieridae, Riodinidae, Zygaenidae)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2023",
 "BasisOfRecord is Human Observation",
 "Country is Sweden",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020748-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jefx8g
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((13.27572 55.68802, 13.31529 55.64814, 13.34814 55.63626, 13.3529 55.61776, 13.33232 55.61229, 13.31542 55.62221, 13.30362 55.60639, 13.34314 55.55803, 13.36993 55.56285, 13.39246 55.53391, 13.45003 55.52777, 13.47397 55.52692, 13.48892 55.54821, 13.49454 55.57524, 13.51832 55.58236, 13.54047 55.63272, 13.59667 55.63678, 13.60329 55.65271, 13.61363 55.66865, 13.5608 55.69821, 13.52745 55.70303, 13.45895 55.73272, 13.42704 55.73407, 13.38381 55.74697, 13.32742 55.75445, 13.29833 55.75102, 13.30294 55.76854, 13.28785 55.78057, 13.25218 55.77391, 13.22762 55.79063, 13.21336 55.79196, 13.14345 55.78734, 13.13535 55.74825, 13.12902 55.72418, 13.11222 55.71342, 13.11544 55.70357, 13.14513 55.70636, 13.17349 55.68175, 13.20437 55.69265, 13.21293 55.67843, 13.27572 55.68802))",
 "TaxonKey is Pieris napi (Linnaeus, 1758)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2023",
 "BasisOfRecord is Human Observation",
 "Country is Sweden",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 123 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020749-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.585y3n
title: Occurrence Download

description: A dataset containing 654 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "DatasetKey is ONF - Observations et inventaires MOA ONF-ONF - Observations opportunistes et inventaires",
 "OccurrenceStatus is Present",
 "TaxonKey is Lynx lynx (Linnaeus, 1758)"
 ]
}

 The dataset includes 654 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020751-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10939643
title: LAMASUS NUTS-level economic data 1980-2021

description: This dataset comprises spatial and temporal economic data compiled from the Annual Regional Database of the European Commission (ARDECO) and education attainment from Eurostat, covering the period from 1980 to 2021(2024). The dataset consists of three files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification.

For each file, the following columns are included: Identifier:



NUTS Code: The unique identifier for the NUTS (2016) region

Year: The year of the data point


Variables:

      3. - 8. Hours Worked by NACE sector in 1000 hours (empHour_*)      9. - 15. Employment by NACE sector in 1000 jobs (emp_*)      16. Total employment in 1000 jobs (empl)      17. GDP at constant prices ref. 2015 in mio EUR (gdp)      18. - 23. GVA by NACE sector at constant prices ref. 2015 in mio EUR (gva_*)      24. Total Labour Force in 1000 jobs (labour)      25. Total Population (Regional Accounts) in persons (pop)      26. - 31. Compensation of Employees by NACE sector at constant prices ref. 2015 in mio EUR (wage_*)      32. Share of low education workers in per cent (loweduc) [not available for NUTS3]      33. Share of high education workers in per cent (terteduc) [not available for NUTS3]

The temporal dimension is yearly, ranging from 1980 to 2021(2024). The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.



This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 45-47 belonging to section 3.3 Sectoral Income and Employment. The full text of the deliverable can be accessed via: https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.

Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.
DOI: 10.5281/zenodo.10939644
title: LAMASUS NUTS-level economic data 1980-2021

description: This dataset comprises spatial and temporal economic data compiled from the Annual Regional Database of the European Commission (ARDECO) and education attainment from Eurostat, covering the period from 1980 to 2021(2024). The dataset consists of three files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification.

For each file, the following columns are included: Identifier:



NUTS Code: The unique identifier for the NUTS (2016) region

Year: The year of the data point


Variables:

      3. - 8. Hours Worked by NACE sector in 1000 hours (empHour_*)      9. - 15. Employment by NACE sector in 1000 jobs (emp_*)      16. Total employment in 1000 jobs (empl)      17. GDP at constant prices ref. 2015 in mio EUR (gdp)      18. - 23. GVA by NACE sector at constant prices ref. 2015 in mio EUR (gva_*)      24. Total Labour Force in 1000 jobs (labour)      25. Total Population (Regional Accounts) in persons (pop)      26. - 31. Compensation of Employees by NACE sector at constant prices ref. 2015 in mio EUR (wage_*)      32. Share of low education workers in per cent (loweduc) [not available for NUTS3]      33. Share of high education workers in per cent (terteduc) [not available for NUTS3]

The temporal dimension is yearly, ranging from 1980 to 2021(2024). The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.



This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 45-47 belonging to section 3.3 Sectoral Income and Employment. The full text of the deliverable can be accessed via: https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.

Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.
DOI: 10.15468/dl.hyrhma
title: Occurrence Download

description: A dataset containing 97482 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is The New York Botanical Garden Herbarium (NY)",
 "TaxonKey is Plantae",
 "Year 2000-2024"
 ]
}

 The dataset includes 97482 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020750-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14034163
title: Innovative L-band electron paramagnetic resonance investigation of solid-state pouch cell batteries
DOI: 10.5281/zenodo.14034164
title: Innovative L-band electron paramagnetic resonance investigation of solid-state pouch cell batteries
DOI: 10.34740/kaggle/dsv/9802359
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.15468/dl.mynye8
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erica herbacea Georgi"
 ]
}

 The dataset includes 6 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020752-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27604398.v1
title: <b>Past, current and future of three of </b><b><i>Fonsecaea </i></b><b>agents of human chromoblastomycosis</b> Supplementary Materials

description: The epidemiology of the<i>Fonsecaea</i>etiologic agents of human chromoblastomycosis is investigated. Haplotype networks based on ribosomal and mitochondrial genes show significant diversity, in part associated with geographical distance.<i>Fonsecaea nubica,</i>which is hypothesized to have an African origin, showed the largest diversity and was interpreted as being ancestral. On the basis of 13 mitochondrial genes, the origin of the pathogenic strains was estimated at 40 Kya. The phylogeny of the pathogenic clade of<i>Fonsecaea</i>was linked to human population movements out of Africa and colonization of the Americas. Analysis of mating type suggests a prevalently clonal reproductive mode, with preponderance of MAT-1-1 mating type and occasional occurrence of truncated MAT-1-2 and well as fusion products which may not be functional. A detected virulence factor shared by the<i>Fonsecaea</i>species potentially causing deep infection was aSOCE-associated regulatory factor of calcium homeostasis, playing a role in extremotolerance. Slight differences in climatic preference of the species were noted, which enabled prediction of the suitable areas for each species and suggests a possible increase in the distribution of these pathogens in the future under a scenario of climate change.