cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.757391
title: (Figure 2) C31 δ¹³C, %C3 vegetation, wet season length and mean annual rainfall for tropical western Africa: mean values for Last Glacial Maximum, Heinrich Stadial 1, mid-Holocene and late Holocene timeslices

description: calc. % C3: Estimates are based on d13C values for C4 plants of -21.7 per mil and for C3 plants of -35.2 per mil.calc. Wet season length: Estimates are based on WSL = 0.077(%C3) + 0.98calc. Mean annual rainfall: Estimates are based on MAR = 0.127(%C3) + 2.25
DOI: 10.1594/pangaea.856126
title: (Appendix A) Stable oxygen isotope record and SST reconstruction for sediment core GL-77
DOI: 10.1594/pangaea.758906
title: Late Miocene palaeoclimate of Lincang (China)
DOI: 10.6084/m9.figshare.28225790
title: [CSEDU 2025] - Supplementary Material

description: This supplementary material presents the results of our study. We analyzed each interactive feature of a sample of technologies used in remote learning through an engagement, participation, and liveness lens, aiming to understand how the interactive feature could influence the student experience.
DOI: 10.6084/m9.figshare.28225790.v1
title: [CSEDU 2025] - Supplementary Material

description: This supplementary material presents the results of our study. We analyzed each interactive feature of a sample of technologies used in remote learning through an engagement, participation, and liveness lens, aiming to understand how the interactive feature could influence the student experience.
DOI: 10.1594/pangaea.962229
title: Detailed tree inventory and area coverage of remote mangrove forests (species: Pelliciera rhizophorae and Rhizophora mangle) in the Utría National Park in the Colombian Pacific Coast

description: This dataset contains detailed inventories of 7 large plots of mangrove forests in the Utría National Park in the Colombian Pacific Coast. The inventory consists of individual geo-referenced tree masks for the endemic Pelliciera rhizophorae species (/pelliciera_trees/Pelliciera.shp), and area coverages for the Rhizophora mangle species, as well as Mud and Water areas (/other_classes_coverage/.tiff). For each individual tree of the Pelliciera rhizophorae species we provide the predicted height, crown diameter and crown area (/pelliciera_trees/trees.csv). We also provide the cover area of the other predicted classes (/other_area_coverage/area_coverages.csv). The inventories were automatically produced with trained Artificial Intelligence (AI) algorithms. The algorithms were trained with orthomosaic images and digital surface models (DSMs) produced from Unoccupied Aerial System (UAS) imagery with Structure-from-Motion software, both paired with expert annotations of the trees and areas (/annotations/.shp). In this dataset we provide all the input data for the algorithms, as well as the predicted geo-referenced data products, such as: predicted Pelliciera rhizophorae tree masks, Rhizophora mangle areas, Water areas, Mud areas, canopy height models (CHM), digital elevation models (DEM), digital terrain models (DTM) and various ancillary images. We also provide the initial orthomosaic files (/orthomosaic.tif) and the DSM files (/DSM.tif), that were produced with SfM software Agisoft Metashape v1.6.2 from the aerial footage captured in 2019 (19–22 February) using two consumer-grade UASs: the DJI Phantom 4 and DJI Mavic Pro (SZ DJI Technology Co., Ltd—Shenzhen, China). The DJI Phantom 4 has an integrated photo camera, the DJI FC330 and the DJI Mavic Pro was equipped with the integrated DJI FC220. The flights were programmed to follow the trajectories in an automated mode by means of the commercial application "DroneDeploy". Ground control points (GCPs) were positioned in the field, and their geographic location was acquired. We used two single-band global navigation satellite system (GNSS) receivers: an Emlid Reach RS+ single-band real-time kinematics (RTK) GNSS receiver (Emlid Tech Kft.—Budapest, Hungary) as a base station, and a Bad Elf GNSS Surveyor handheld GPS (Bad Elf, LLC—West Hartford, AZ, USA). RINEX static data from the base station was processed with the Precise Point Positioning Service (PPP) of the Natural Resources of Canada, while rover position was processed using the RTKLib software through a post processed kinematics (PPK) workflow. The final absolute positional accuracy of the products is below one meter because the results of the PPP workflow has a positional accuracy between 0.2 m and 1 m.
description: The data in this work was produced and curated at the Leibniz Centre for Tropical Marine Research (ZMT) DataLab in Bremen, Germany.
DOI: 10.1594/pangaea.964192
title: Stable isotope record of planktic foraminifera of sediment core SIS-249 in the western South Atlantic

description: The datasets utilized in this study encompass relative abundances of planktonic foraminifera and stable isotope data retrieved from sediment core SIS-249 in the western South Atlantic, spanning the 110-30 thousand-year interval. For the oxygen stable isotope analysis, a minimum of 10 specimens each from Globigerinoides ruber and Globorotalia inflata were picked. These specimens were selected from the fraction larger than 0.250 mm and analyzed at the University of São Paulo. The resulting data were employed to reconstruct historical patterns of sea surface fertilization, stratification, and calcium carbonate dissolution in the western South Atlantic throughout the 110-30 thousand-year period.
description: Brazilian Coordination of Higher Education Staff Improvement–CAPES (grant number 88887.091729/2014-01) Brazilian National Council for Scientific and Technological Development –CNPq (grant number 407922/2016-4)Charles University Grant Agency (GAUK, grant number 355422)
DOI: 10.1594/pangaea.964191
title: Planktonic foraminifera of sediment core SIS-249 in the western South Atlantic

description: The datasets utilized in this study encompass relative abundances of planktonic foraminifera and stable isotope data retrieved from sediment core SIS-249 in the western South Atlantic, spanning the 110-30 thousand-year interval. Planktonic foraminifera counts were examined at the Laboratório de Microfósseis Calcários, Universidade Federal do Rio Grande do Sul. The analysis focused on specimens larger than 0.150 mm, with a minimum of 300 specimens counted. The resulting data were employed to reconstruct historical patterns of sea surface fertilization, stratification, and calcium carbonate dissolution in the western South Atlantic throughout the 110-30 thousand-year period.
description: Brazilian Coordination of Higher Education Staff Improvement–CAPES (grant number 88887.091729/2014-01) Brazilian National Council for Scientific and Technological Development –CNPq (grant number 407922/2016-4)Charles University Grant Agency (GAUK, grant number 355422)
DOI: 10.1594/pangaea.973595
title: Arctic landforms and processes around the coast of Svalbard

description: This dataset presents the thirteen coastal classes that were identified around the coast of the Svalbard archipelago, using remote sensing and field data. The map is based on aerial images, satellite images and detailed landform and geomorphological maps from various coastal areas within the Isfjorden region. Field observations have been extrapolated to the entire coastline of Svalbard. The aerial images or orthophotos were acquired between 2008 and 2012, the satellite images date from 2020 and both are available through the Web Map Service (WMS) of the Norwegian Polar Institute (NPI). The map is projected in ETRS 89 UTM 33 (EPSG:25833), which is most compatible for Svalbard. The mapping was conducted at a scale of 1:30,000 (1mm = 30m). The ongoing warming climate affects coastal regions, making them vulnerable to changes. This map provides a baseline for the active coastal processes and morphology, which can be used to predict changes and potential coastal erosion in arctic regions.
description: Vector format: ESRI shapefileProjection: ETRS 89 UTM 33 (EPSG:25833)Vector type: polyline ZA layer file with symbology is included.The dataset was mapped in a scale of 30.000, with the aim to map a total approx. 6800 kilometres of coastline of Svalbard. For optimal use in the 30.000 scale in ArcGIS Pro, use the following instructions:• Set map scale to 30.000.• Open symbology (right click on 'coastal classes' in contents tab and select 'symbology') and choose 'Vary symbology by attribute'.• Select 'Size' and set 'minimum' AND 'maximum' size to 30 pt (enable size range can be clicked on or off).• Set reference scale (In contents tab, right click on 'map' and select 'set reference scale').
DOI: 10.1594/pangaea.973597
title: Passive acoustic monitoring data recorded by recorder SV1059 at mooring AWI232-12 in the Weddell Sea, Southern Ocean, in 2015

description: Passive acoustic monitoring (PAM) data were collected by recorder SV1059 of type Sono.Vault (manufactured by develogic GmbH, Hamburg, Germany) at 68.9815° S, 0.0833° W, mooring AWI232-12, in the Weddell Sea, Atlantic sector of the Southern Ocean. During a deployment period from December 2014 to December 2016, passive acoustic data were collected from January 2015 to August 2015 (recording period) by SV1059 as part of the Hybrid Antarctic Float Observing System (HAFOS) in the Weddell Sea. The recorder was moored at 999 m depth and scheduled to record continuously at a sample rate of 6,857 Hz. Further details about the data acquisition and processing of this data set can be found in the accompanying metadata file (see Additional metadata) as well as the data processing report (see Data Processing Report). Passive acoustic data archived here represent data processing Level 2+ (see Data Processing Report), deviating from Standard Operation Procedure (SOP) Glossary (Thomisch et al. 2023a). Further information on data processing with regard to data preparation and standardization can be found in the associated SOP Part 1: Data preparation and standardization (Thomisch et al. 2023b).
description: Each daily .tar contains the 10-minute audio files (.wav format) collected during the respective day.
DOI: 10.1594/pangaea.974284
title: Expanded measurements from station Ny-Ålesund (2024-11)

description: 99999: No clouds detected
DOI: 10.1594/pangaea.974286
title: Basic measurements of radiation at station Langley Research Center (2024-11)
DOI: 10.1594/pangaea.974287
title: Basic and other measurements of radiation at station Ny-Ålesund (2024-11)
DOI: 10.1594/pangaea.974288
title: Basic and other measurements of radiation at station Cabauw (2024-11)

description: Use of alternative pyranometer (Kipp & Zn CM11 s/n 020053) for diffuse irradiance on 20 and 21 November 2024 because of suboptimal ventilation. Occasional data loss for direct irradiance because of pointing problems in the morning (7-10:30 UTC).
DOI: 10.6084/m9.figshare.28225535
title: <b>Passport data and phenotypic characterization of sequenced accessions</b>

description: <i>Bromus auleticus</i>, a valuable forage grass native to the Pampa biome, is currently suffering genetic erosion. Therefore, it is central to assess the appropriate methodologies for developing population genomic studies and contributing to the conservation of this plant genetic resource. This study evaluated five accessions using two genotyping strategies: individual sequencing (ind-seq) and pooled sequencing (pool-seq). To compare their efficacy, was investigated the correlation between allele frequencies calculated using each approach. Geneticdiversity and population structure were also analyzed using both methods. These comparisons explicitly accounted for the potential effects of one or more of the following parameters: sample size, missing data, sequencing depth, and minor allele frequencies. Notably, the highest concordance and percentage of common SNPs between ind-seq and pool-seq was achieved using 30 to 60 plants per accession, with a maximum missing data threshold of 10% and a more tolerant minimum allele frequency threshold for pool-seq (0.01) compared to ind-seq (0.05). Reaching maximum similarity in allele frequencies also required a higher sequencing depth for pool-seq (4.8 million reads-Mr) than for ind-seq (0.9 Mr). Pools of 50 individuals yielded the highest number of polymorphic sites, averaging over 9,000 across all accessions at a sequencing depth of 4.8 Mr. Under these conditions, pool-seq consistently yielded a means across all accessions of 0.09 higher expected heterozygosity and 0.24 lower allelic richness compared to ind-seq across all accessions. The population structure detected with both methodologies showed agreement, aligning with the outcrossing nature of <i>B. auleticus</i> and the geographical origin of each accession. The average inbreeding coefficient of 0.2 indicates signs of inbreeding, highlighting the importance of conservation efforts of this valuable plant genetic resource. Based on these findings, two workflows were proposed to carry out population genomics studies of <i>Bromus auleticus</i>.
DOI: 10.5281/zenodo.14676355
title: Global Semiconductor Equipment Manufacturing Market 2025 To 2034

description: Semiconductor Equipment Manufacturing Market Size, Trends and Insights By Process (Back-end, Front-end), By Dimension (3D, 2.5D, 2D), By Application (Testing & Inspection, Semiconductor Fabrication Plant/Foundry, Semiconductor Electronics Manufacturing), 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 Semiconductor Equipment Manufacturing Market is expected to record a CAGR of 7.78% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 111.38 Billion. By 2033, the valuation is anticipated to reach USD218.60 Billion.

Semiconductor equipment manufacture is an essential aspect of the production of semiconductors, the very building blocks of modern electronics. This sector focuses on specialized engineering and manufacturing of tools required to undertake wafer fabrication, lithography, etching, deposition, and inspection.

There has been an increase in the demand for advanced equipment owing to the rising reliance on semiconductors globally. Such demand is boosted by its usage in various industries, such as consumer electronics, automotive, telecommunications, and AI.

Technological advancements have included extreme ultraviolet (EUV) lithography and 3D chip stacking, which have produced more minor, faster, and energy-efficient chips. The Asia-Pacific market leader in Taiwan, South Korea, and China are the three significant hubs due to their formidable semiconductor manufacturing ecosystems and several government initiatives toward strengthening domestic manufacturing, such as the U.S. CHIPS Act and Europe’s push for semiconductor sovereignty.

Although growth may be organic, there are hurdles, such as the high capital investment required, R&D cycles that last long, and exposing the industry to supply chain vulnerabilities. This industry will surely grow as the development of emerging applications such as 5G, IoT, and autonomous vehicles allows semiconductor equipment manufacturers to innovate and expand continuously in future years.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=60807
DOI: 10.5281/zenodo.14676356
title: Global Semiconductor Equipment Manufacturing Market 2025 To 2034

description: Semiconductor Equipment Manufacturing Market Size, Trends and Insights By Process (Back-end, Front-end), By Dimension (3D, 2.5D, 2D), By Application (Testing & Inspection, Semiconductor Fabrication Plant/Foundry, Semiconductor Electronics Manufacturing), 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 Semiconductor Equipment Manufacturing Market is expected to record a CAGR of 7.78% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 111.38 Billion. By 2033, the valuation is anticipated to reach USD218.60 Billion.

Semiconductor equipment manufacture is an essential aspect of the production of semiconductors, the very building blocks of modern electronics. This sector focuses on specialized engineering and manufacturing of tools required to undertake wafer fabrication, lithography, etching, deposition, and inspection.

There has been an increase in the demand for advanced equipment owing to the rising reliance on semiconductors globally. Such demand is boosted by its usage in various industries, such as consumer electronics, automotive, telecommunications, and AI.

Technological advancements have included extreme ultraviolet (EUV) lithography and 3D chip stacking, which have produced more minor, faster, and energy-efficient chips. The Asia-Pacific market leader in Taiwan, South Korea, and China are the three significant hubs due to their formidable semiconductor manufacturing ecosystems and several government initiatives toward strengthening domestic manufacturing, such as the U.S. CHIPS Act and Europe’s push for semiconductor sovereignty.

Although growth may be organic, there are hurdles, such as the high capital investment required, R&D cycles that last long, and exposing the industry to supply chain vulnerabilities. This industry will surely grow as the development of emerging applications such as 5G, IoT, and autonomous vehicles allows semiconductor equipment manufacturers to innovate and expand continuously in future years.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=60807
DOI: 10.5281/zenodo.14676347
title: US Red Meat Market 2025 To 2034

description: US Red Meat Market Size, Trends and Insights By Type (Beef, Pig, Sheep & Goat, Others), By Cut Type (Steaks, Ribs, Ground Meat, Roasts, Shank, Others), By Form (Fresh, Frozen, Processed), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

As per the current market research conducted by the CMI Team, the US Red Meat Market is expected to record a CAGR of 3.2% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 98,192.9 Million. By 2033, the valuation is anticipated to reach USD 130,376.1 Million.

This chart reveals the projections for the world’s meat production for 2020, 2021 and 2022, focusing on China, the USA, Brazil and Russia. China occupies the largest production capacity share, which increased from 78.35 million tonnes in 2020 to 92.95 million tonnes in 2022.

The USA is another relatively stable production, from 47.06 million tonnes in 2020 to 47.53 million tonnes in 2022. Brazil also increases the growth rate constantly, it increases from 28.57 to 30.4 million tonnes in the same years. At the same time, production in Russia rose to 12.24 million tonnes in 2022.

Especially for the US Red Meat Market, this chart clearly expresses that the country has continued to contribute to global meat production and the sector is unaffected by global economic cycles. Such stability indicates that the US continues to hold up its position in the red meat market to compete effectively with other countries.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=60378
DOI: 10.5281/zenodo.14676348
title: US Red Meat Market 2025 To 2034

description: US Red Meat Market Size, Trends and Insights By Type (Beef, Pig, Sheep & Goat, Others), By Cut Type (Steaks, Ribs, Ground Meat, Roasts, Shank, Others), By Form (Fresh, Frozen, Processed), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

As per the current market research conducted by the CMI Team, the US Red Meat Market is expected to record a CAGR of 3.2% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 98,192.9 Million. By 2033, the valuation is anticipated to reach USD 130,376.1 Million.

This chart reveals the projections for the world’s meat production for 2020, 2021 and 2022, focusing on China, the USA, Brazil and Russia. China occupies the largest production capacity share, which increased from 78.35 million tonnes in 2020 to 92.95 million tonnes in 2022.

The USA is another relatively stable production, from 47.06 million tonnes in 2020 to 47.53 million tonnes in 2022. Brazil also increases the growth rate constantly, it increases from 28.57 to 30.4 million tonnes in the same years. At the same time, production in Russia rose to 12.24 million tonnes in 2022.

Especially for the US Red Meat Market, this chart clearly expresses that the country has continued to contribute to global meat production and the sector is unaffected by global economic cycles. Such stability indicates that the US continues to hold up its position in the red meat market to compete effectively with other countries.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=60378
DOI: 10.1594/pangaea.911300
title: Taxonomically annotated underwater hyperspectral and color images of coral reef transects from Curaçao

description: This dataset contains underwater hyperspectral imagery that can be used by researchers in the domains of computer vision, machine learning, remote sensing and coral reef ecology. A diver-operated hyperspectral imaging system (HyperDiver) was used to survey 147 transects at 8 coral reef sites around the Caribbean island of Curaçao. The proximal sensing approach produced cm-scale images of more than 2.2 billion points of detailed optical spectra. Of these, more than 10 million data points have been annotated for benthic taxonomic identity with hierarchical labels. In addition to HyperDiver survey data, we also include images and annotations from traditional (color photo) quadrat surveys conducted along 23 of the 147 transects, which enables comparative reef description between two types of reef survey methods.
description: Description of the general layout of each zip folder:-transect.nc in netCDF4 format: the HyperDiver transect data. All transects will contain this file. This contains a 3D (Y, X, W) array "cube" which is the hyperspectral image. The other arrays (indexed by Timestamp Ts) are 1D with data from other sensors such as altitude (m), water pressure (bar), camera pose and acceleration.-natural.jpg: an image file that visualizes the hyperspectral scene using standard red, green, blue channels to produce a natural view of scene-metadata.json: a text JSON file which contains the name of the site location and the original source folder including acquisition date.-classmap.nc and classmap.jpg: the taxonomic annotations marked on the transect's hyperspectral cube, and visualized as a color image. Only transects that have been annotated would contain these files.-video_frames/frame_xxx.jpg are a series of frames from corresponding video of the transect extracted as images. All transects will contain this folder.-photo_quadrats/[filename].jpg and .csv which are available in transects that had co-located survey through traditional photo survey technique. The jpg file is the photo, and the csv file contains the pixel coordinate and corresponding taxonomic annotation.