cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15062940
title: Global Hand Tools Market 2025 To 2034

description: Hand Tools Market Size, Trends and Insights By Product Type (Hand Tools for Construction, Hand Tools for Automotive, Hand Tools for DIY, Hand Tools for Industrial Applications), By Material (Steel, Aluminum, Plastic, Others), By End User (Professional, DIY Enthusiasts), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

As per the Hand Tools Market analysis conducted by the CMI Team, the global Hand Tools Market is expected to record a CAGR of 4.5% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 29.58 Billion. By 2034, the valuation is anticipated to reach USD 43.96 Billion.

Hand Tools Market Overview

Demand from various industries such as construction, automotive, and manufacturing, as well as do-it-yourself enthusiasts, drives the hand tools market within global tools. The professional and personal use of products, such as hammers, screwdrivers, pliers, and wrenches, comprises these tools. Technological innovations, improved ergonomics in tools, and the desire for more DIY projects also influence the market.

Furthermore, the expansion of the construction industry and rising infrastructure activities globally contribute to market growth. As demand for high-quality, durable tools increases, manufacturers are focusing on product differentiation and sustainable practices.

The hand tools market is expected to grow with a projected compound annual growth rate of 4.5% from 2025 to 2034, and key manufacturers will offer a wide range of products for many applications.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=67677
DOI: 10.5281/zenodo.8409592
title: Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format

description: Derived from https://zenodo.org/record/8260741, prepared in a machine-learning friendly TFDS format, ready to be used with https://zenodo.org/record/8397954. clic_edm_ttbar_pf.tar: ee -&gt; ttbar, center of mass energy at 380 GeV clic_edm_qq_pf.tar: ee -&gt; Z* -&gt; qqbar, center of mass energy at 380 GeV clic_edm_ww_fullhad_pf.tar: ee -&gt; WW -&gt; W decaying hadronically, center of mass energy at 380 GeV clic_edm_zh_tautau_pf.tar: ee -&gt; ZH -&gt; Higgs decaying to tau leptons, center of mass energy at 380 GeV <strong>Contents</strong> Each .tar file contains the dataset in the tensorflow-datasets (minimum version v4.9.1), array_record format. <strong>Dataset semantics</strong> Each dataset consists of events that can be iterated over using the tensorflow-datasets library in either tensorflow or pytorch. Each event has the following information available: X: the reconstruction input features, i.e. tracks and clusters ygen: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v1.6/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py.
description: Funding support for the development and generation of this dataset by Estonian Research Council (ETAG) grant PSG864. The full dataset is hosted at the Julich HPC, supported by the CoE RAISE project. CoE RAISE project has received funding from the European Union's Horizon 2020 – Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733.
DOI: 10.5281/zenodo.15062717
title: Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format

description: Synopsis

Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with jpata/particleflow:v2.3.0. Derived from the EDM4HEP ROOT files in https://zenodo.org/record/8260741.



clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV

clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV

clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV

clic-tfds.ipynb: an example notebook on how to load the files


Contents

Each .zip file contains the dataset in the tensorflow-datasets, array_record format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.

Dataset semantics (to be updated)

Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:



X: the reconstruction input features, i.e. tracks and clusters

ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle

ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle


The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.
description: Funding support for the development and generation of this dataset was provided by the Estonian Research Council (ETAG) grant PSG864.
DOI: 10.5281/zenodo.8409591
title: Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format

description: Synopsis

Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with jpata/particleflow:v2.3.0. Derived from the EDM4HEP ROOT files in https://zenodo.org/record/8260741.



clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV

clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV

clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV

clic-tfds.ipynb: an example notebook on how to load the files


Contents

Each .zip file contains the dataset in the tensorflow-datasets, array_record format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.

Dataset semantics (to be updated)

Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:



X: the reconstruction input features, i.e. tracks and clusters

ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle

ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle


The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.
description: Funding support for the development and generation of this dataset was provided by the Estonian Research Council (ETAG) grant PSG864.
DOI: 10.5281/zenodo.14741028
title: SpaIM
title: SpaIM-Datasets

description: SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer
DOI: 10.5281/zenodo.15062258
title: DataExtraction
DOI: 10.5281/zenodo.15062259
title: DataExtraction
DOI: 10.5281/zenodo.10753497
title: Grid/NLTE

description: Departure coefficients for titanium in late-type stars.

See Titanium abundances in late-type stars. II. Grid of departure coefficients and application to a sample of 70 000 stars (Mallinson, Lind, Amarsi & Youakim, A&A, in press) and Non-LTE departure coefficients for large spectroscopic surveys (Amarsi et al., A&A, 2020, 642, A62).
DOI: 10.5281/zenodo.3888394
title: Grid/NLTE

description: Non-LTE departure coefficients for large spectroscopic surveys (see Amarsi et al. 2020).
DOI: 10.5281/zenodo.3960831
title: Grid/NLTE

description: Non-LTE departure coefficients for large spectroscopic surveys (see Amarsi et al. 2020).
DOI: 10.5281/zenodo.3982506
title: Grid/NLTE

description: Non-LTE departure coefficients for large spectroscopic surveys (see Amarsi et al. 2020).
DOI: 10.5281/zenodo.7088951
title: Grid/NLTE

description: Departure coefficients for iron in late-type stars. See <em>3D non-LTE iron abundances in FG-type dwarfs</em> (Amarsi, Liljegren, Nissen, A&A, in press) and <em>Non-LTE departure coefficients for large spectroscopic surveys</em> (Amarsi et al., A&A, 2020, 642, A62).
DOI: 10.5281/zenodo.15044958
title: Supplementary Dataset: DFT Calculations for Spin-Spin Coupling of Amino Acids

description: Supplementary Dataset.



Coord-Files and XYZ-files of DFT optimized (PW6B95/def2-TZVP/SMD(Water)) structures of 20 standard amino acids (L) and their mirrored structure (enantiomer (D)).

XLSX-file containing all SSCC calculated in TURBOMOLE using different Methods. 

XLSX-file containing all chemical shielding tensors using TURBOMOLE

ORCA input and output for SSCC calculation at BP/TZVP/CPCM(water) level of theory using the structures published in (https://doi.org/10.1038/s41467-024-49966-8)

Input/Output from SIMPSON

Data for Conformational Sampling

Python script to mirror conformers 

Input and Output for relativistic ReSpect calculations
DOI: 10.5281/zenodo.15062813
title: Grid/NLTE

description: Non-LTE departure coefficients for late-type stars.

For this version, see primarily Revisiting inelastic Cu+H collisions and the non-LTE Galactic evolution of copper (Caliskan, Amarsi, Racca, Koutsouridou, Barklem, Lind & Salvadori, A&A, submitted).  The methods were first presented in Non-LTE departure coefficients for large spectroscopic surveys (Amarsi et al., A&A, 2020, 642, A62).

Also included are data for hydrogen based on calculations carried out for 3D Stagger model atmospheres with FreeEOS. I. Exploring the impact of microphysics on the Sun (Zhou, Amarsi, Aguirre Børsen-Koch, Karlsmose, Collet & Nordlander, A&A, 2023, 677, A98) that are preferred over the grids in versions 2-3, as well as a fix to the energies stored in the PySME grid for titanium based on Titanium abundances in late-type stars. II. Grid of departure coefficients and application to a sample of 70 000 stars (Mallinson, Lind, Amarsi & Youakim, A&A, 687, A5) that is preferred over the corresponding grid in version 5.
DOI: 10.5281/zenodo.15062965
title: Global Secure Microcontroller Market 2025 To 2034

description: Secure Microcontroller Market Size, Trends and Insights By Application (Banking, Automotive, Telecommunications, Consumer Electronics, IoT, Others), By Type (Secure Elements, Trusted Platform Modules, Hardware Security Modules, Others), By End-User Industry (Consumer Electronics, Automotive, Industrial, Healthcare, Government, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

 

As per the Secure Microcontroller Market analysis conducted by the CMI Team, the global Secure Microcontroller Market is expected to record a CAGR of 12.5% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 27.6 Billion. By 2034, the valuation is anticipated to reach USD 80.7 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=67743
DOI: 10.5281/zenodo.15062966
title: Global Secure Microcontroller Market 2025 To 2034

description: Secure Microcontroller Market Size, Trends and Insights By Application (Banking, Automotive, Telecommunications, Consumer Electronics, IoT, Others), By Type (Secure Elements, Trusted Platform Modules, Hardware Security Modules, Others), By End-User Industry (Consumer Electronics, Automotive, Industrial, Healthcare, Government, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

 

As per the Secure Microcontroller Market analysis conducted by the CMI Team, the global Secure Microcontroller Market is expected to record a CAGR of 12.5% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 27.6 Billion. By 2034, the valuation is anticipated to reach USD 80.7 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=67743
DOI: 10.5281/zenodo.15062981
title: Global Pneumatic Equipment Market 2025 To 2034

description: Pneumatic Equipment Market Size, Trends and Insights By Equipment Type (Compressors, Actuators, Valves, Cylinders, Pumps, Filters, Air Tools), By End User (Manufacturing, Automotive, Packaging, Food Processing, Construction, Healthcare), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

 

As per the Pneumatic Equipment Market analysis conducted by the CMI Team, the global Pneumatic Equipment Market is expected to record a CAGR of 7% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 34.9 Billion. By 2034, the valuation is anticipated to reach USD 54 Billion.

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DOI: 10.5281/zenodo.15062982
title: Global Pneumatic Equipment Market 2025 To 2034

description: Pneumatic Equipment Market Size, Trends and Insights By Equipment Type (Compressors, Actuators, Valves, Cylinders, Pumps, Filters, Air Tools), By End User (Manufacturing, Automotive, Packaging, Food Processing, Construction, Healthcare), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

 

As per the Pneumatic Equipment Market analysis conducted by the CMI Team, the global Pneumatic Equipment Market is expected to record a CAGR of 7% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 34.9 Billion. By 2034, the valuation is anticipated to reach USD 54 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=67732
DOI: 10.5281/zenodo.15062952
title: Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #7

description: ## Hyperspectral

Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.

Hyperspectral data also contain calculated RGB and NDVI png and tiff images. PNG images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.

All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.

### Hyper 7

https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_7.zip

Dataset consists of 8 flight lines filmed over a forest patch.

Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. 

Dataset zip size: 83.0 GB
DOI: 10.5281/zenodo.15062953
title: Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #7

description: ## Hyperspectral

Hyperspectral data in all of the datasets consist of processed hyperspectral data cubes to radiance values.all data cubes with _radiance.dat and ._radiance.hdr files are for ENVI format data file. While _rect.dat and _rect.hdr files are ENVI files with georectification applied.

Hyperspectral data also contain calculated RGB and NDVI png and tiff images. PNG images are generated from _radiance.dat files and tiff images are generated from _rect.dat files that have georectification.

All hyperspectral data was collected using the Specim hyperspectral pushbroom camera.

### Hyper 7

https://art21-icaerus.s3.eu-central-1.amazonaws.com/Hyper_7.zip

Dataset consists of 8 flight lines filmed over a forest patch.

Each flight folder denoted by fl# contains processed hyperspectral data cubes and "processed" folder with RGB and NDVI images. 

Dataset zip size: 83.0 GB
DOI: 10.18150/83f56m
title: Immune response to Toxocara canis infection in C57BL/6 mice

description: Experimental Design:Two different experiments were conducted.In the first experiment, eight-week-old female C57BL/6J mice (n = 20) were orally infected with 1,000 infective eggs of T. canis suspended in a 0.9% NaCl solution. Non-infected mice (n = 5) served as controls. Five infected mice were euthanized at 0 (control group), 3, 5, 14, and 28 days post-infection (dpi).In the second experiment, eight-week-old female C57BL/6J mice (n = 18) were treated intranasally with 2 µg of TES in 20 µl of PBS, while control mice (n = 5) received an intranasal treatment with the same volume of PBS. Nasal administration was applied once per day for 3 days. Six TES-treated mice were euthanized at each of the following time points: 3, 14, and 28 days post-first intranasal administration (dpfi). The control mice were euthanized at 3 dpfi.After sacrifice the following procedures were performed:Lung images were taken - folder Lung_imagesLung sections were submitted to hematoxylin-eosin and periodic-acid Schiff staining - folder HEMultiparametric flow cytometric analyses were performed on isolated lung cell suspensions - folder Flow_cytometryTES specific antibodies were measured in sera using ELISA - folder AntibodiesTotal IgE was measured in sera and in lung tissue lysates using ELISA Antibody Pair Kit (StemCell Technologies, Canada) - folder AntibodiesCytokine concentration was measured in splenocyte culture media and lung tissue lysates using commercial DuoSet Elisa kits (R&D Systems, USA) - folders: splenocytes, lung_lysatesCytokine expression in lung tissue lysates was analyzed using the Proteome Profiler™ Mouse XL Cytokine Array (R&D Systems, USA) - folder Proteome_Profiler_Mouse_XL_Cytokine_ArrayGene expression in lungs was assesed using qPCR - folder qPCRExplanation of abbreviations used in attached files:   d0_1-5 – Mice from the control group (not infected, n=5).   d3_1-5 – Mice euthanized 3 days post-infection (n=5).   d5_1-5 – Mice euthanized 5 days post-infection (n=5).   d14_1-5 – Mice euthanized 14 days post-infection (n=5).   d28_1-5 – Mice euthanized 28 days post-infection (n=5).   PBS_d3_1-5 – Control group mice euthanized 3 days after the first PBS intranasal administration (n=5).   TES_d3_1-6 – Mice euthanized 3 days after the first TES intranasal administration (n=6).   TES_d14_1-6 – Mice euthanized 14 days after the first TES intranasal administration (n=6).   TES_d28_1-6 – Mice euthanized 28 days after the first TES intranasal administration (n=6)
DOI: 10.15468/dl.b4pju5
title: Occurrence Download

description: A dataset containing 10416 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Database and digitization of bees in Thailand"
 ]
}

 The dataset includes 10416 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036486-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vdzkq3
title: Occurrence Download

description: A dataset containing 946 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-16.83511 34.41440,39.44112 34.41440,39.44112 56.91440,-16.83511 56.91440,-16.83511 34.41440))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Trapa natans L.",
 "Year 2022-2025"
 ]
}

 The dataset includes 946 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020092-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.35097/1g4v0wv0crcc0brv
title: Experimental data of tailings as a source for generating valuable magnesium and calcium carbonates by leaching and carbonization

description: Mining produces a lot of disposable mineral waste after the ore has been removed. This waste with added CO$_2$ can be used as a feedstock for carbonate production. The advantage of this production process would be the recycling of a waste product with additional carbon capture. However, the processing challenge is a feed with a fluctuating composition of the mining waste, which makes classical engineering and control with fixed process parameters difficult. For characterization, model particles and tailings are studied for their behavior with fluctuating acid and mass concentrations as a function of leaching time. The results confirm the possibility of leaching calcium (max 7g L$^{-1}$) and magnesium (max 0.7 g L$^{-1}$) using hydrochloric acid. The amount varies depending on the particle, concentration and pH. Mathematically, the leaching over time can be described by an exponential expression, which is applied to both investigated cases for the leaching of calcium and magnesium ions. Finally, a pH shift process is used to precipitate solid CaCO$_3$ from the ion-enriched solution obtained, thus demonstrating the successful production of CaCO$_3$ and carbon capture from mine waste.
description: (Technical Remarks)
This data set contains experimental data of the publication "Tailings as a Source for Generating valuable Magnesium and Calcium Carbonates by Leaching and Carbonization" in ChemRxiv (DOI: 10.26434/chemrxiv-2025-cjxfm). The laboratory setup as well as the used materials for data generation are described in detail within the paper.
The .csv-files provide the raw data for each diagram and in the origin file are all diagrams with the fit functions.
DOI: 10.15468/dl.dnj3g9
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Saissetia neglecta De Lotto, 1969",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020094-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6m6qba
title: Occurrence Download

description: A dataset containing 230 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ormosia xylocarpa Merr. & Chun ex H.Y.Chen"
 ]
}

 The dataset includes 230 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020096-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.