cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15022982
title: Global Bioplastics Packaging Market 2024 To 2033

description: Bioplastics Packaging Market Size, Trends and Insights By Material (Polylactic Acid (PLA), Polyhydroxyalkanoates (PHA), Starch Blends, Others), By Type (Rigid Packaging, Flexible Packaging), By Application (Bottles, Films & Sheets, Bags & Pouches, Trays & Containers, Others), By End-User Industry (Food & Beverage, Pharmaceuticals, Personal Care, Consumer Goods, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the global Bioplastics Packaging Market is expected to record a CAGR of 12.5% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 17,728.8 Million. By 2033, the valuation is anticipated to reach USD 51,357.1 Million.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=49756
DOI: 10.5281/zenodo.15022983
title: Global Bioplastics Packaging Market 2024 To 2033

description: Bioplastics Packaging Market Size, Trends and Insights By Material (Polylactic Acid (PLA), Polyhydroxyalkanoates (PHA), Starch Blends, Others), By Type (Rigid Packaging, Flexible Packaging), By Application (Bottles, Films & Sheets, Bags & Pouches, Trays & Containers, Others), By End-User Industry (Food & Beverage, Pharmaceuticals, Personal Care, Consumer Goods, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the global Bioplastics Packaging Market is expected to record a CAGR of 12.5% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 17,728.8 Million. By 2033, the valuation is anticipated to reach USD 51,357.1 Million.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=49756
DOI: 10.5281/zenodo.15022991
title: Europe Glass Packaging Market 2024 To 2033

description: Europe Glass Packaging Market Size, Trends and Insights By Type (Bottles, Jars, Vials, Ampoules, Growlers, Carboys, Others), By Application (Food & Beverages, Pharmaceuticals, Cosmetics & Personal Care, Household Products, Chemicals, Others), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Europe Glass Packaging Market was valued at USD 22.08 Billion in 2024 and is expected to reach USD 29.55 Billion by 2033, at a CAGR of 3.29% during the forecast period 2024 – 2033.

Glass packaging is a method of packaging that utilizes durable and transparent containers and bottles crafted from glass, renowned for their non-reactive properties, ideal for preserving and storing a variety of products such as food, beverages, pharmaceuticals, and cosmetics.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=49020
DOI: 10.5281/zenodo.15022992
title: Europe Glass Packaging Market 2024 To 2033

description: Europe Glass Packaging Market Size, Trends and Insights By Type (Bottles, Jars, Vials, Ampoules, Growlers, Carboys, Others), By Application (Food & Beverages, Pharmaceuticals, Cosmetics & Personal Care, Household Products, Chemicals, Others), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Europe Glass Packaging Market was valued at USD 22.08 Billion in 2024 and is expected to reach USD 29.55 Billion by 2033, at a CAGR of 3.29% during the forecast period 2024 – 2033.

Glass packaging is a method of packaging that utilizes durable and transparent containers and bottles crafted from glass, renowned for their non-reactive properties, ideal for preserving and storing a variety of products such as food, beverages, pharmaceuticals, and cosmetics.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=49020
DOI: 10.5281/zenodo.15022997
title: Global Sterile Medical Packaging Market 2024 To 2033

description: Sterile Medical Packaging Market Size, Trends and Insights By Material (Glass, Plastic, Metal, Others), By Type (Thermoform Trays, Flexible Pouches, Bottles, Others), By End Use (Surgical Instruments, Pharmaceuticals, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Sterile Medical Packaging Market was valued at USD 27.3 Billion in 2024 and is expected to reach USD 63.6 Billion by 2033, at a CAGR of 9.4% during the forecast period 2024 – 2033.

Sterile medical packaging is the use of specialised materials and processes to package medical equipment, instruments, and supplies so that they remain sterile until they are required. Sterility is essential in medical environments to avoid contamination and infection, particularly during procedures and operations.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=48933
DOI: 10.5281/zenodo.15022998
title: Global Sterile Medical Packaging Market 2024 To 2033

description: Sterile Medical Packaging Market Size, Trends and Insights By Material (Glass, Plastic, Metal, Others), By Type (Thermoform Trays, Flexible Pouches, Bottles, Others), By End Use (Surgical Instruments, Pharmaceuticals, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Sterile Medical Packaging Market was valued at USD 27.3 Billion in 2024 and is expected to reach USD 63.6 Billion by 2033, at a CAGR of 9.4% during the forecast period 2024 – 2033.

Sterile medical packaging is the use of specialised materials and processes to package medical equipment, instruments, and supplies so that they remain sterile until they are required. Sterility is essential in medical environments to avoid contamination and infection, particularly during procedures and operations.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=48933
DOI: 10.15468/dl.he8teq
title: Occurrence Download

description: A dataset containing 4447 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paranthrene tabaniformis (Rottemburg & von, 1775)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4447 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008129-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nzuwjm
title: Occurrence Download

description: A dataset containing 86157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sphinx pinastri Linnaeus, 1758",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 86157 records from 370 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008120-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gh3tma
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paratachardina pseudolobata Kondo & Gullan, 2007",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 83 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008130-250310093411724/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.28562891
title: <b>Chemopreventive efficacy and mechanisms of piceatannol against benzo[a]pyrene/dextran sodium sulfate-induced colitis-associated colorectal cancer in mice</b>

description: <b>raw data-Chemopreventive efficacy and mechanisms of piceatannol against benzo[a]pyrene/dextran sodium sulfate-induced colitis-associated colorectal cancer in mice</b>
DOI: 10.5281/zenodo.15022950
title: Quanty in statistics

description:  Statistical measures play a crucial role in medical research and public health by providing a quantitative basis for comparing populations. This article explores absolute, relative, and average quantities and their application in healthcare data analysis. Absolute quantities, Relative quantities, average quantities. By applying these statistical tools, researchers can better assess disease burden, evaluate healthcare interventions, and guide public health policies. Understanding the strengths and limitations of these measures is essential for accurate data interpretation and effective decision-making in epidemiology and clinical practice.
DOI: 10.5281/zenodo.15022951
title: Quanty in statistics

description:  Statistical measures play a crucial role in medical research and public health by providing a quantitative basis for comparing populations. This article explores absolute, relative, and average quantities and their application in healthcare data analysis. Absolute quantities, Relative quantities, average quantities. By applying these statistical tools, researchers can better assess disease burden, evaluate healthcare interventions, and guide public health policies. Understanding the strengths and limitations of these measures is essential for accurate data interpretation and effective decision-making in epidemiology and clinical practice.
DOI: 10.15468/dl.kbyg3v
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Uzbekistan",
 "Geometry POLYGON((46.03919 30.30761,91.96081 30.30761,91.96081 48.69239,46.03919 48.69239,46.03919 30.30761))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tulipa lehmanniana Merckl."
 ]
}

 The dataset includes 64 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008131-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mgkupe
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plagiotrochus amenti Kieffer, 1901",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 94 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008132-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13334757
title: A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021

description:  

I) DESCRIPTION:

· A global daily seamless 9-km Vegetation Optical Depth (VOD) product is generated through gap-filling and spatiotemporal fusion model. This daily products start from Jan 01, 2010 to Jul 31, 2021 (about 20GB memory after uncompressing all zip files).

· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.

· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.

II) DATA FORMATTING AND FILE NAMES 

Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.

MATFILES (.mat): The folders with matfiles contain individual files for:

1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat

2. Latitude/Longitude: VOD_9km_Coordinates.mat
DOI: 10.5281/zenodo.13334756
title: A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021

description:  

(I) DESCRIPTION:

· A global daily seamless 9-km Vegetation Optical Depth (VOD) product is generated through gap-filling and spatiotemporal fusion model. This daily products start from Jan 01, 2010 to Jul 31, 2021 (about 20GB memory after uncompressing all zip files).

· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.

· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.

(II) DATA FORMATTING AND FILE NAMES 

For the convenience of our readers, we have two formats of data available for download.

1) MAT file (Version v1)

Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.

MATFILES (.mat): The folders with matfiles contain individual files for:

1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat

2. Latitude/Longitude: VOD_9km_Coordinates.mat

2) NetCDF file (Version v2)

The year-by-year daily data from 2010 to 2021 are stored in the ‘.nc’ files for the corresponding years. The daily data within each year into one NetCDF file. The variable names are named as VOD_xxxxyydd, where xxxx represents the year, yy represents the month, and dd represents the day. The longitude variable is named “lon” with a dimension of 4000×1, and the latitude variable is named “lat” with a dimension of 2000×1. 

It should be noted that these NetCDF files are saved using the netCDF4 library in Python, with the dimension order being (lat, lon). When reading these NetCDF files in MATLAB, the default data dimension order is (lon, lat). Therefore, it is necessary to transpose the variables to match the correct dimension order.
DOI: 10.5281/zenodo.15018195
title: A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021

description:  

(I) DESCRIPTION:

· A global daily seamless 9-km Vegetation Optical Depth (VOD) product is generated through gap-filling and spatiotemporal fusion model. This daily products start from Jan 01, 2010 to Jul 31, 2021 (about 20GB memory after uncompressing all zip files).

· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.

· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.

(II) DATA FORMATTING AND FILE NAMES 

For the convenience of our readers, we have two formats of data available for download.

1) MAT file (Version v1)

Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.

MATFILES (.mat): The folders with matfiles contain individual files for:

1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat

2. Latitude/Longitude: VOD_9km_Coordinates.mat

2) NetCDF file (Version v2)

The year-by-year daily data from 2010 to 2021 are stored in the ‘.nc’ files for the corresponding years. The daily data within each year into one NetCDF file. The variable names are named as VOD_xxxxyydd, where xxxx represents the year, yy represents the month, and dd represents the day. The longitude variable is named “lon” with a dimension of 4000×1, and the latitude variable is named “lat” with a dimension of 2000×1. 

It should be noted that these NetCDF files are saved using the netCDF4 library in Python, with the dimension order being (lat, lon). When reading these NetCDF files in MATLAB, the default data dimension order is (lon, lat). Therefore, it is necessary to transpose the variables to match the correct dimension order.
DOI: 10.15468/dl.s2nxvk
title: Occurrence Download

description: A dataset containing 552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Indonesia",
 "TaxonKey is Seleucidis melanoleucus (Daudin, 1800)",
 "Year 2015-2025"
 ]
}

 The dataset includes 552 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008133-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bcsaym
title: Occurrence Download

description: A dataset containing 93 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "GadmGid is one of (IDN.10_1, IDN.11_1, IDN.9_1, IDN.7_1, IDN.7.1_1, IDN.7.2_1, IDN.7.3_1, IDN.7.4_1, IDN.7.5_1, IDN.4_1)",
 "TaxonKey is Beilschmiedia Nees"
 ]
}

 The dataset includes 93 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008134-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gz8mnx
title: Occurrence Download

description: A dataset containing 12528 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paratrechina longicornis (Latreille, 1802)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12528 records from 111 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008135-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jgp38c
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parlatoria ziziphi (Lucas, 1853)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 24 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008136-250310093411724/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.28594493
title: Stress resistance of entomogenous fungi

description: Resistance of <i>Beauveria bassiana</i> and <i>Metarhizium rileyi</i>
DOI: 10.15468/dl.drb68d
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Metopolophium frisicum Hille Ris Lambers, 1947",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008138-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15023036
title: Evaluation Package for Detecting Unintended Hierarchical Moves in GumTree
DOI: 10.5281/zenodo.15023037
title: Evaluation Package for Detecting Unintended Hierarchical Moves in GumTree
DOI: 10.15468/dl.hgyjpx
title: Occurrence Download

description: A dataset containing 2856055 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Liliopsida"
 ]
}

 The dataset includes 2856055 records from 782 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008137-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18434/mds2-3159
title: Dataset presenting improved bandwidth in Rydberg atom electrometry with an optical frequency comb probe
DOI: 10.15468/dl.dckq5h
title: Occurrence Download

description: A dataset containing 132 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Perkinsiella saccharicida Kirkaldy, 1903",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 132 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008139-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j82ths
title: Occurrence Download

description: A dataset containing 313 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phataginus tricuspis (Rafinesque, 1821)"
 ]
}

 The dataset includes 313 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008140-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0005177
title: 单细胞多组学重建视网膜衰老分子机制数据集
DOI: 10.15468/dl.skkhst
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Neophyllaphis varicolor Miller & Halbert, 2014",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008141-250310093411724/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.26135107
title: <b>The remaining categories of the DsDPM 66 dataset, including coal_miner, compressed_oxygen_self_rescuer, and mining_helmet</b>

description: In the field of coal mine safety monitoring and automation, high-quality and specialized datasets are crucial for the development and validation of artificial intelligence algorithms. Currently, there is no comprehensive benchmark dataset specifically for coal mine industrial scenarios, which significantly limits the research progress of AI algorithms in the coal mining industry. This study has constructed for the first time a benchmark dataset (DsDPM 66) specifically for coal mine heading faces, containing 105,096 images obtained from videos of 66 drilling operation scenes. The dataset has been meticulously annotated manually to suit computer vision tasks such as object detection and pose estimation. In addition, this study conducted extensive benchmarking experiments on this dataset, applying various advanced AI algorithms including but not limited to YOLOv8 and DETR. The experimental results show that the proposed dataset can effectively improve the accuracy of various object detection and pose estimation models in coal mines, filling the data gap in the coal mining field and providing valuable resources for the development of coal mine safety monitoring and automation technologies.