cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17026/ar/kwrrcp
title: Foxhol, K. Nieboerweg 11 (Gemeente Midden-Groningen, Gr.) Een Archeologisch Bureauonderzoek en Inventariserend Veldonderzoek (IVO-O) Verkennende fase

description: Het plangebied ligt in de oude dorpskern van Foxhol op een holoceen dekzandgebied.
Dit was in de steentijd een geschikte plaats voor bewoning. De top van het dekzand bevindt zich
tussen de 0 tot 2 meter onder maaiveld en de verwachting voor archeologische waarden uit de
steentijd is daarom in het plangebied zeer hoog. Deze archeologische waarden kunnen bestaan
uit vuurstenen artefacten, houtskoolconcentraties en grondsporen zoals haardkuilen. Vanaf het
eind van het neolithicum overgroeide het gebied met veen dat vanaf de late middeleeuwen
ontgonnen werd. Van deze ontginningen en het daarop volgende landgebruik als akker kunnen
resten bewaard zijn gebleven in de bodem. Indicatoren hiervan bestaan uit artefacten van
aardewerk, steen, hout, metaal of glas, maar ook grondsporen zoals greppels en (paal)kuilen.
Tot 1965 liep er ten hoogte van de K. Nieboersweg een kanaal van het Foxholstermeer naar het
Winschoterdiep. Het plangebied maakt deel uit van de historische lintbebouwing langs dit
kanaal. Door de aanleg van dit kanaal en de bebouwing die langs dit kanaal werd gesitueerd
kan het zijn dat de archeologische resten in de bodem verstoord zijn geraakt.
De bodemopbouw in het plangebied bestaat uit een bouwvoor met daaronder een dik
verstoord pakket zand. Hieronder ligt een dik pakket bosveen met een dikte van 75 tot 180
centimeter. Vanaf 245 centimeter onder maaiveld ligt een dekzandbodem met een zwak
ontwikkelde podzolbodem. In het plangebied zijn geen archeologische indicatoren aangetroffen,
met uitzondering van een aardewerkfragment in het verstoorde zandpakket. Deze zandlaag is
vermoedelijk opgebracht der versteviging van de (veen)bodem. Het ernaast gelegen perceel ligt
beduidend lager.
DOI: 10.17632/t4kbxgm7my
title: Dataset and Code for SKU-Level Demand Forecasting Using ML–PPCP Framework

description: Synthetic dataset and Python code provided to ensure reproducibility of the machine learning and deterministic production planning framework proposed in the associated manuscript. Real industrial data are confidential; therefore, anonymized synthetic data are supplied.
DOI: 10.17632/t4kbxgm7my.1
title: Dataset and Code for SKU-Level Demand Forecasting Using ML–PPCP Framework

description: Synthetic dataset and Python code provided to ensure reproducibility of the machine learning and deterministic production planning framework proposed in the associated manuscript. Real industrial data are confidential; therefore, anonymized synthetic data are supplied.
DOI: 10.17026/ar/wjrldn
title: Formerum, Formerum 71-73 (Gemeente Terschelling, Fr.) Een Archeologisch Bureauonderzoek en Inventariserend Veldonderzoek (IVO-O) Verkennende fase

description: &lt;p&gt;De bodem bestaat in het plangebied van boven naar beneden hoofdzakelijk uit 1) een bouwvoor, 2) een grijs zandpakket met roestvlekken en sporadisch puin- en houtskoolspikkels, 3) een laag van lichtgrijs zand met roestvlekken en ijzerconcreties en 4) een natuurlijke laag van lichtgrijs zand.&lt;/p&gt;&lt;p&gt;
 Het veldonderzoek heeft een aantal archeologische indicatoren opgeleverd in de vorm van puin-, aardewerk en houtskoolspikkels. Deze bevinden zich tussen de 15 en 80 centimeter beneden het maaiveld. Daarnaast is er in boring 2, in de tweede laag tussen de 40 en 45 centimeter beneden het maaiveld, een scherf kogelpot aardewerk a daterend uit de 8e – 14e eeuw nC. Deze tweede laag bevindt zich tussen de 15 en 45 centimeter beneden het maaiveld en dateert uit de periode middeleeuwen – nieuwe tijd. Mogelijk heeft dit een relatie met het nabijgelegen rijksmonument, de voormalige kapel 'Vijfpoort' of met eerdere bebouwing langs de weg.&lt;/p&gt;
DOI: 10.17026/ar/xia787
title: Oosterwolde, Rikkingahof (Gemeente Ooststellingwerf, Fr.) Een Archeologisch Bureauonderzoek en Inventariserend Veldonderzoek (IVO-O) Verkennende Fase

description: &lt;p&gt;Oosterwolde ligt in het Drents zandgebied. Het plangebied ligt van oorsprong in een pleistoceen dekzandgebied. In het dekzandlandschap konden tijdens het paleolithicum en mesolithicum jager-verzamelaars trekken. Met name de hogere zandkoppen en zandruggen waren aantrekkelijke locaties voor (tijdelijke) kampen van deze jager-verzamelaars. Van 1500 vC tot 250 vC heeft het plangebied in een veengebied gelegen, waardoor het plangebied in deze periode geen geschikte locatie voor bewoning was. Volgens de geomorfologische kaart ligt het plangebied op grondmorenewelvingen (L11). Grondmorene is materiaal dat in de ijstijden door het landijs is afgezet. Hierover ligt een laag dekzand. De bodem in het plangebied bestaat uit veldpodzolgronden (Hn23). De hoogte in het plangebied ligt op circa 6,5 meter boven NAP. In het plangebied is sinds 1965 een weg aanwezig. Daarnaast zijn geen aanwijzingen voor bouwhistorische waarden in de ondergrond van het plangebied.
Uit het bureauonderzoek blijkt dat een hoge verwachting voor resten vanaf het paleolithicum tot de vroege bronstijd en vanaf de late ijzertijd tot de nieuwe tijd aanwezig is. Deze resten zullen voornamelijk afkomstig zijn van jachtkampen of nederzettingen.&lt;/p&gt;&lt;p&gt;
Tijdens het veldwerk zijn zes boringen verspreid over het plangebied gezet. Uit de resultaten van het booronderzoek blijkt dat de ondergrond van het plangebied bestaat uit een verstoorde toplaag gevolgd door dekzand en keileem. In twee boringen is een restant van een podzolbodem waargenomen, die in het plangebied klaarblijkelijk wel aanwezig is geweest. Echter is de bodem in het recente verleden sterk vergraven, vermoedelijk bij de inrichting van het zorgcomplex rond 1965. Tijdens het veldonderzoek zijn geen archeologische indicatoren en/of cultuurlagen aangetroffen.&lt;/p&gt;
DOI: 10.71900/jmyr-cs37
title: High-frequency physical/chemical data from LM2 buoy in Lake Maggiore - Italy 

description: The dataset includes hourly average values (obtained from sensor readings - frequency 1 min) for water temperature (thermistor chain, 11 fixed depths), pH, electrical conductivity, dissolved oxygen (at one fixed depth). Data are collected by in-situ sensors on the Lake Maggiore buoy located in the Ispra sub-basin of Lake Maggiore (LM2 buoy; for details see: https://deims.org/locations/d204d9c5-fef7-405e-9043-8bd7d6b954dc). Raw data have beeen checked level 1 (removal of  0, NA, outliers) and hourly averaged. The buoy has been deployed by the Regional Agency of Environmental Protection of the Lombardy Region (ARPA Lombardia) and is presenlty managed in cooperation between CNR IRSA and ARPA Lombardia.
DOI: 10.71900/j7jx-ag02
title: High-frequency physical/chemical data from LM2 buoy in Lake Maggiore - Italy 

description: The dataset includes hourly average values (obtained from sensor readings - frequency 1 min) for water temperature (thermistor chain, 11 fixed depths), pH, electrical conductivity, dissolved oxygen (at one fixed depth). Data are collected by in-situ sensors on the Lake Maggiore buoy located in the Ispra sub-basin of Lake Maggiore (LM2 buoy; for details see: https://deims.org/locations/d204d9c5-fef7-405e-9043-8bd7d6b954dc). Raw data have beeen checked level 1 (removal of  0, NA, outliers) and hourly averaged. The buoy has been deployed by the Regional Agency of Environmental Protection of the Lombardy Region (ARPA Lombardia) and is presenlty managed in cooperation between CNR IRSA and ARPA Lombardia.
DOI: 10.17632/7gy7dgx7gr
title: Recovery of Valuable Metals from NMC811 Lithium-Ion Cells

description: experimental datas
DOI: 10.17632/7gy7dgx7gr.1
title: Recovery of Valuable Metals from NMC811 Lithium-Ion Cells

description: experimental datas
DOI: 10.15468/dl.8ymqkw
title: Occurrence Download

description: A dataset containing 2098 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Garra ukhrulensis, Garra bimaculacauda, Garra parastenorhynchus, Garra chaudhurii, Laubuka tenella, Pseudacanthicus pitanga, Ancistrus maximus, Laubuka parafasciata, Laubuka latens, Laubuka trevori, Garra robertsi, Hemibrycon sierraensis, Hyphessobrycon natagaima, Glossogobius macrocephalus, Silvinichthys pachonensis, Glossogobius multipapillus, Cyphocharax boiadeiro, Brachychalcinus reisi, Hyphessobrycon ericae, Hyphessobrycon wosiackii, Glossogobius sentaniensis, Stiphodon annieae, Crenicichla ploegi, Boehlkea weitzmani, Astyanax joaovitori, Homatula change, Exostoma sawmteai, Schistura kampucheensis, Austrolebias bagual, Melanotaenia flavipinnis, Melanotaenia ericrobertsi, Melanotaenia laticlavia, Melanotaenia multiradiata, Cophecheilus brevibarbatus, Schistura andrewi, Hypselobarbus kushavali, Moenkhausia uirapuru, Glyptothorax senapatiensis, Neolissochilus capudelphinus, Neolissochilus minimus, Neolissochilus micropthalmus, Neolissochilus acutirostris, Neolissochilus tamiraparaniensis, Oreoglanis pangenensis, Schistura nebeshwari, Schistura scyphovecteta, Telestes comes, Hypselobarbus menoni, Hypselobarbus tamiraparaniei, Hypselobarbus vaigaiensis, Garra mondica, Aphyosemion fellmanni, Barbatula restricta, Barbatula markakulensis, Laimosemion mabura, Triplophysa minxianensis, Barbatula tomiana, Melanorivulus imperatrizensis, Labeo latebra, Syncomistes bonapartensis, Syncomistes carcharus, Pelvicachromis sacrimontis, Geophagus diamantinensis, Rubricatochromis, Syncomistes dilliensis, Syncomistes holsworthi, Syncomistes moranensis, Syncomistes versicolor, Syncomistes wunambal, Astyanax keronolepis, Salmo pellegrini, Anablepsoides fransvermeuleni, Chrysobrycon guahibo, Labeotropheus chlorosiglos, Labeotropheus simoneae, Maylandia flavicauda, Maylandia usisyae, Tropheops kumwera, Tropheops biriwira, Tropheops kamtambo, Cynotilapia chilundu, Chindongo bellicosus, Schistura myrmekia, Hyphessobrycon piranga, Serrapinnus zanatae, Cyphomyrus cubangoensis, Nothobranchius mkuziensis, Hemigrammus changae, Trematocranus pachychilus, Alburnus maximus, Iranocichla persa, Electrophorus varii, Electrophorus voltai, Corydoras knaacki, Pseudacanthicus pirarara, Chaetostoma joropo, Hypostomus cordovae, Hypostomus spiniger, Aphyosemion flavocyaneum, Aphyosemion flammulatum)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2098 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002109-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4fcvnr77sb
title: 20250129_Fabric_defects

description: 1. Dataset Overview

The DME Fabric Defect Detection Dataset is an industrial microscopy image collection designed for research on automated fabric defect detection and classification. The dataset captures a diverse range of real textile defects under controlled acquisition conditions and reflects practical challenges encountered in textile quality inspection environments.

The dataset is intended for use in computer vision, deep learning, and industrial inspection research, particularly for evaluating CNN-based and hybrid architectures.
Class Distribution
Class Label 
Stain |Damage |Broken Thread |Holes |Non-defective
3. Fabric Types and Visual Diversity

Images were collected from multiple fabric categories to ensure diversity and generalization:

Plain fabrics

Textured fabrics

Woven fabrics

Printed fabrics

Satin fabrics

Denim fabrics

Defects vary in size, orientation, contrast, and texture, reflecting real industrial variability.
4. Image Resolution and Format

Original acquisition resolution: 1920 × 1080 pixels

Processed resolution (for model training): 128 × 128 pixels

Color format: RGB (3 channels)

File format: JPG

Downsampling was applied only during model training to ensure computational feasibility, while original high-resolution images are preserved.
DOI: 10.17632/4fcvnr77sb.1
title: 20250129_Fabric_defects

description: 1. Dataset Overview

The DME Fabric Defect Detection Dataset is an industrial microscopy image collection designed for research on automated fabric defect detection and classification. The dataset captures a diverse range of real textile defects under controlled acquisition conditions and reflects practical challenges encountered in textile quality inspection environments.

The dataset is intended for use in computer vision, deep learning, and industrial inspection research, particularly for evaluating CNN-based and hybrid architectures.
Class Distribution
Class Label 
Stain |Damage |Broken Thread |Holes |Non-defective
3. Fabric Types and Visual Diversity

Images were collected from multiple fabric categories to ensure diversity and generalization:

Plain fabrics

Textured fabrics

Woven fabrics

Printed fabrics

Satin fabrics

Denim fabrics

Defects vary in size, orientation, contrast, and texture, reflecting real industrial variability.
4. Image Resolution and Format

Original acquisition resolution: 1920 × 1080 pixels

Processed resolution (for model training): 128 × 128 pixels

Color format: RGB (3 channels)

File format: JPG

Downsampling was applied only during model training to ensure computational feasibility, while original high-resolution images are preserved.
DOI: 10.5281/zenodo.18412857
title: Data and code from: The diversity of fungivorous mites in Dutch forests affected by nitrogen deposition and soil acidification

description: This study analyzed microarthropod communities in old oak forests (h9190 in the EU habits directive) in the Veluwe, the Netherlands and compared them to previous data from other forest sites from the same geographical area and a similar habitat (h9120 in the EU habits directive).
DOI: 10.5281/zenodo.18412856
title: Data and code from: The diversity of fungivorous mites in Dutch forests affected by nitrogen deposition and soil acidification

description: This study analyzed microarthropod communities in old oak forests (h9190 in the EU habits directive) in the Veluwe, the Netherlands and compared them to previous data from other forest sites from the same geographical area and a similar habitat (h9120 in the EU habits directive).
DOI: 10.17632/fhwtmzxbf2
title: Simulation-derived electricity consumption dataset for standardized TOKİ mass housing across 11 provinces affected by the 6 February 2023 earthquakes in Türkiye

description: This dataset contains simulation-derived energy, climate, and thermal performance variables for a standardized TOKİ mass housing typology located in provinces affected by the 6 February 2023 earthquakes in Türkiye. The data were generated using dynamic building energy simulations conducted in DesignBuilder with the EnergyPlus simulation engine.

Simulations were performed on an annual basis using province-specific EPW climate files to capture regional climatic variations. The dataset includes electricity and gas consumption indicators, heating and cooling loads, thermal comfort parameters, environmental variables, and CO₂ emission-related outputs. All building geometry, envelope properties, and system configurations were kept constant across simulations to isolate the impact of climatic conditions on energy performance.

The dataset is structured to support binary electricity consumption tier classification (LOW/HIGH) based on daily average electricity usage, in accordance with Türkiye’s tiered electricity tariff framework defined by the Energy Market Regulatory Authority (EPDK). Raw simulation outputs were organized and preprocessed through standardization and binary label transformation to facilitate reuse in data-driven analyses, including classification, feature selection, and dimensionality reduction studies.

This dataset is intended to support research on residential energy consumption analysis, post-disaster housing assessment, energy efficiency evaluation, and machine learning–based modeling without requiring the repetition of computationally intensive building energy simulations.
DOI: 10.15468/dl.3nzmk8
title: Occurrence Download

description: A dataset containing 346 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Jacaratia corumbensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 346 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002110-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fhwtmzxbf2.2
title: Simulation-derived electricity consumption dataset for standardized TOKİ mass housing across 11 provinces affected by the 6 February 2023 earthquakes in Türkiye

description: This dataset contains simulation-derived energy, climate, and thermal performance variables for a standardized TOKİ mass housing typology located in provinces affected by the 6 February 2023 earthquakes in Türkiye. The data were generated using dynamic building energy simulations conducted in DesignBuilder with the EnergyPlus simulation engine.

Simulations were performed on an annual basis using province-specific EPW climate files to capture regional climatic variations. The dataset includes electricity and gas consumption indicators, heating and cooling loads, thermal comfort parameters, environmental variables, and CO₂ emission-related outputs. All building geometry, envelope properties, and system configurations were kept constant across simulations to isolate the impact of climatic conditions on energy performance.

The dataset is structured to support binary electricity consumption tier classification (LOW/HIGH) based on daily average electricity usage, in accordance with Türkiye’s tiered electricity tariff framework defined by the Energy Market Regulatory Authority (EPDK). Raw simulation outputs were organized and preprocessed through standardization and binary label transformation to facilitate reuse in data-driven analyses, including classification, feature selection, and dimensionality reduction studies.

This dataset is intended to support research on residential energy consumption analysis, post-disaster housing assessment, energy efficiency evaluation, and machine learning–based modeling without requiring the repetition of computationally intensive building energy simulations.
DOI: 10.17632/hbpn696gh4
title: Incidence of Hidradenitis Suppurativa in Transgender and Cisgender Individuals: A Multicenter Matched Cohort Study

description: This supplemental material contains additional methods, tables, and figures for the manuscript.
DOI: 10.17632/hbpn696gh4.1
title: Incidence of Hidradenitis Suppurativa in Transgender and Cisgender Individuals: A Multicenter Matched Cohort Study

description: This supplemental material contains additional methods, tables, and figures for the manuscript.
DOI: 10.17632/b9thz3mkyp
title: Data Supplement for "The Effects of Iron and Manganese Doping on the Carbonation of Brucite [Mg(OH)2]"

description: This is the data associated with the manuscript "The Effects of Iron and Manganese Doping on the Carbonation of Brucite [Mg(OH)2]" at The Journal of Physical Chemistry C (DOI: 10.1021/acs.jpcc.5c06987)
DOI: 10.25502/2yvz-0p14/d
title: The relative response of LBC and minitubers to different regimes of inorganic fertiliser application
DOI: 10.17632/b9thz3mkyp.1
title: Data Supplement for "The Effects of Iron and Manganese Doping on the Carbonation of Brucite [Mg(OH)2]"

description: This is the data associated with the manuscript "The Effects of Iron and Manganese Doping on the Carbonation of Brucite [Mg(OH)2]" at The Journal of Physical Chemistry C (DOI: 10.1021/acs.jpcc.5c06987)
DOI: 10.6084/m9.figshare.25738503
title: Energy specific [GJ/ha] for the 6 most irrigated crops for (i) different water source, (ii) different irrigation system used, and (iii) different energy source used.

description: 1) Raw Data of Figure 1a, FIgure 1b, Figure 4a and FIgure 4b.<br><br>2) MATLAB code (user-adaptible version) with the choice to use user-specific data and import processes.<br><br>3) Energy specific [GJ/ha] for the 6 most irrigated crops for (i) different water source, (ii) different irrigation system used, and (iii) different energy source used.Unit: GJ/haFile name: Energysource_irrigationsystem_watersource_cropname.tif.Energy source may be: diesel (efficiency equal to 0.21), electric (efficiency equal to 0.56) or EDmix standing for elentric-diesel mix (weighted average efficiency equal to 0.315).Irrigation system may be:SU-&gt; surface irrigation systemSP-&gt; sprinkler irrigation systemDP-&gt; drip irrigation systemWater source may be:SU -&gt; surface water source (i.e. river or lake)GW-&gt; groundwater sourceCrop name may be: cotton, maize, rice, sugarcane, vegetables and wheat.
DOI: 10.6084/m9.figshare.25738503.v1
title: Energy specific [GJ/ha] for the 6 most irrigated crops for (i) different water source, (ii) different irrigation system used, and (iii) different energy source used.

description: 1) Raw Data of Figure 1a, FIgure 1b, Figure 4a and FIgure 4b.<br><br>2) MATLAB code (user-adaptible version) with the choice to use user-specific data and import processes.<br><br>3) Energy specific [GJ/ha] for the 6 most irrigated crops for (i) different water source, (ii) different irrigation system used, and (iii) different energy source used.Unit: GJ/haFile name: Energysource_irrigationsystem_watersource_cropname.tif.Energy source may be: diesel (efficiency equal to 0.21), electric (efficiency equal to 0.56) or EDmix standing for elentric-diesel mix (weighted average efficiency equal to 0.315).Irrigation system may be:SU-&gt; surface irrigation systemSP-&gt; sprinkler irrigation systemDP-&gt; drip irrigation systemWater source may be:SU -&gt; surface water source (i.e. river or lake)GW-&gt; groundwater sourceCrop name may be: cotton, maize, rice, sugarcane, vegetables and wheat.