cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14905297
title: Certified Technology for the effect of plant tannins and saponins on reducing the production of methane and ammonia produced by fermentation activity in the rumen, on the health and production of dairy cattle

description: The EU's strategic plan for reducing methane (CH4) emissions, improving air quality, andstrengthening the EU's global leadership in the fight against climate change creates a need todesign alternative solutions to reduce greenhouse gas emissions from animal production.Methane (CH4) is one of the three main greenhouse gases, along with carbon dioxide (CO2) andnitrous oxide (N2O). It is highly effective and has a global warming potential 28 times greaterthan CO2. In addition to contributing to global warming, CH4 formation in the rumen results ina loss of 2-12 % of the animal's metabolic energy intake. Since livestock production accountsfor about 17-20 % of the total 40 % of global anthropogenic emissions, many EU countrieshave made legislative commitments to substantially reduce their total greenhouse gasemissions, including CH4 emissions from livestock farming. On March 14, 2023, theParliament of the Czech Republic voted to increase the target for reducing greenhouse gasemissions by 2030 from 30 % to 40 % compared to 2005. Therefore, the main goal of the SS06020190 project is the development of a new feedsupplement for cattle with a strong antimethanogenic effect while simultaneously eliminatingthe negative impact on production, health, and animal welfare. To achieve these purposes, in2024, a pilot test of the impact of selected antimethanogenic components (tannins fromQuebracha and chestnut tree) was carried out, and their potential to reduce the production ofenteric CH4 in the rumen was evaluated. The influence of the dietaryaddition of tannins and macroalgae during the experiment on rumen fermentation, blood biochemical parameters, milk yield, and milk quality parameters was also monitored.
DOI: 10.5281/zenodo.14904809
title: Experimental batch of tannin-based feed premix for reducing stable emissions in ruminants

description: The EU's strategic aim to reduce methane (CH4) emissions, improve air quality, andstrengthen the EU's global leadership in the fight against climate change creates a need to designalternative solutions to reduce greenhouse gas emissions from livestock production. Livestockproduction accounts for about 17 – 20% of the total 30 – 35% of global anthropogenicemissions, representing a significant ecological burden. In addition to contributing to globalwarming, CH4 formation in the rumen results in a loss of 2 – 12% of the animal's metabolicenergy intake. Therefore, the main goal of the SS06020190 project is the development of a newfeed supplement for cattle with an antimethanogenic effect while simultaneously eliminatingthe negative impact on production, health, and animal welfare. In 2024, in cooperation with theproject partner, the company MIKROP Cebin, a functional sample of feed premix for ruminantswas designed to achieve these purposes. A pilot in vivo study conducted on cannulated cowsexperimentally verified the supplement's effect. This experiment monitored the influence of thenew premix/supplement dietary addition on rumen fermentation parameters, the total count andrelative abundance of protozoal genera, and the production of enteric CH4.
DOI: 10.5281/zenodo.14904810
title: Experimental batch of tannin-based feed premix for reducing stable emissions in ruminants

description: The EU's strategic aim to reduce methane (CH4) emissions, improve air quality, andstrengthen the EU's global leadership in the fight against climate change creates a need to designalternative solutions to reduce greenhouse gas emissions from livestock production. Livestockproduction accounts for about 17 – 20% of the total 30 – 35% of global anthropogenicemissions, representing a significant ecological burden. In addition to contributing to globalwarming, CH4 formation in the rumen results in a loss of 2 – 12% of the animal's metabolicenergy intake. Therefore, the main goal of the SS06020190 project is the development of a newfeed supplement for cattle with an antimethanogenic effect while simultaneously eliminatingthe negative impact on production, health, and animal welfare. In 2024, in cooperation with theproject partner, the company MIKROP Cebin, a functional sample of feed premix for ruminantswas designed to achieve these purposes. A pilot in vivo study conducted on cannulated cowsexperimentally verified the supplement's effect. This experiment monitored the influence of thenew premix/supplement dietary addition on rumen fermentation parameters, the total count andrelative abundance of protozoal genera, and the production of enteric CH4.
DOI: 10.6084/m9.figshare.28451567.v3
title: Subgroup analysis

description: Supplementary table
DOI: 10.6084/m9.figshare.28457450
title: GEE classification output for the Venezuelan Seagrass Mapping project

description: GEE classification output for the Venezuelan Seagrass Mapping.
DOI: 10.5281/zenodo.14046526
title: Deep learning empowered sensor fusion boosts infant movement classification

description: This repository contains data set and code for the paper:

Kulvicius, T., Zhang, D., Poustka, L.,  Bölte, S., Jahn, L., Flügge, S., Kraft, M., Zweckstetter, M., Nielsen-Saines, K., Wörgötter, F., and Marschik, P. B. (2024). Deep learning empowered sensor fusion boosts infant movement classification. Communications Medicine, 5(16). DOI:10.1038/s43856-024-00701-w.

For more details please see README.md file.
DOI: 10.5281/zenodo.14046527
title: Deep learning empowered sensor fusion boosts infant movement classification

description: This repository contains data set and code for the paper:

Kulvicius, T., Zhang, D., Poustka, L.,  Bölte, S., Jahn, L., Flügge, S., Kraft, M., Zweckstetter, M., Nielsen-Saines, K., Wörgötter, F., and Marschik, P. B. (2024). Deep learning empowered sensor fusion boosts infant movement classification. Communications Medicine, 5(16). DOI:10.1038/s43856-024-00701-w.

For more details please see README.md file.
DOI: 10.60797/mca.2025.57.2.1
title: Стандартное отклонение при испытании

description: Современное строительство и архитектура, Выпуск 2 (57) 2025
DOI: 10.60797/mca.2025.57.1.3
title: Грунтовые условия опытной площадки по ул. Таврической в г. Сочи

description: Современное строительство и архитектура, Выпуск 2 (57) 2025
DOI: 10.15468/dl.u5p23z
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "DatasetKey is INPN - Données flore des CBN agrégées par la FCBN",
 "TaxonKey is Ailanthus altissima (Mill.) Swingle",
 "Year 2002-2025"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000063-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14905378
title: AMPERE Summary Plot Movies (2016-Dec)

description: 2016-Dec AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.14905379
title: AMPERE Summary Plot Movies (2016-Dec)

description: 2016-Dec AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5284/1124347
title: Images from an Archaeological Watching Brief at St Philip and St James Church, Church Lane, Whittington, Worcestershire, 2021 to 2022

description: This archive consists of images from a watching brief by Worcester Archaeology at St Philip and St James Church, Church Lane, Whittington from 2021 to 2022. Three trenches were excavated across the south-western side of the churchyard. No structural remains, voids or vaulted graves were recorded, nor deposits relating to anything other than churchyard activity.
DOI: 10.5281/zenodo.14905256
title: Appendix for the article "AI to Fight Disinformation: a Living Lab Approach"

description: You will find two files related to the paper: Duelen, A., Van den Broeck, W., Jennes, I., Fibecker Ladegaard, S., Hoff, M., & Bang Bådum, N. (2023). AI to Fight Disinformation: a Living Lab Approach. In Proceedings of the OpenLivingLab Days Conference 2023: Living Labs for an Era of Transitions. How human-centric innovation is changing our lives (pp. 148-156). ENoLL - European Network of Living Labs. https://openlivinglabdays.com/wp-content/uploads/2023/10/OLLD-2023-Proceedings.pdf

The 'Dataset of co-creation TITAN' contains the results of the three first exercises of the co-creation workshops. 

The '3.1 Report on citizen co-created design principles for TITAN Conceptual Architecture' is the Deliverable that was linked to the results of the co-creation activities that are partly described in the paper.
DOI: 10.5281/zenodo.14905257
title: Appendix for the article "AI to Fight Disinformation: a Living Lab Approach"

description: You will find two files related to the paper: Duelen, A., Van den Broeck, W., Jennes, I., Fibecker Ladegaard, S., Hoff, M., & Bang Bådum, N. (2023). AI to Fight Disinformation: a Living Lab Approach. In Proceedings of the OpenLivingLab Days Conference 2023: Living Labs for an Era of Transitions. How human-centric innovation is changing our lives (pp. 148-156). ENoLL - European Network of Living Labs. https://openlivinglabdays.com/wp-content/uploads/2023/10/OLLD-2023-Proceedings.pdf

The 'Dataset of co-creation TITAN' contains the results of the three first exercises of the co-creation workshops. 

The '3.1 Report on citizen co-created design principles for TITAN Conceptual Architecture' is the Deliverable that was linked to the results of the co-creation activities that are partly described in the paper.
DOI: 10.48323/9g2w6-dkb15
title: Data for investigation of hydraulic concepts to lower the return temperature from customers to district heating networks

description: These are monitoring data of a multifamily house used to define space heating and domestic hot water consumption profiles as input for a Polysun simulation model. Simulation results and postprocessing files are included as well.
DOI: 10.48323/xnwzw-8pz89
title: Data for investigation of hydraulic concepts to lower the return temperature from customers to district heating networks

description: These are monitoring data of a multifamily house used to define space heating and domestic hot water consumption profiles as input for a Polysun simulation model. Simulation results and postprocessing files are included as well.
DOI: 10.5281/zenodo.14351353
title: BioSR for LLS-SIM: Dataset of Chromosomes

description: This is LLS-SIM dataset of Chromosomes, which is a part of BioSR for LLS-SIM dataset (https://doi.org/10.5281/zenodo.14322457).

BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models.
DOI: 10.5281/zenodo.14351354
title: BioSR for LLS-SIM: Dataset of Chromosomes

description: This is LLS-SIM dataset of Chromosomes, which is a part of BioSR for LLS-SIM dataset (https://doi.org/10.5281/zenodo.14322457).

BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models.
DOI: 10.5281/zenodo.14904846
title: BioSR for LLS-SIM: Dataset of Chromosomes

description: This is LLS-SIM dataset of Chromosomes, which is a part of BioSR for LLS-SIM dataset (https://doi.org/10.5281/zenodo.14322457).

BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models.
DOI: 10.15468/dl.tj7daj
title: Occurrence Download

description: A dataset containing 6049701 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-76.35388 9.31224,-75.50368 8.79915,-75.03134 8.47226,-74.60624 7.95803,-74.74794 7.11518,-75.07858 6.64625,-74.37007 6.08294,-73.37817 6.36467,-73.09477 7.20891,-72.8586 8.05157,-72.95307 8.70578,-73.142 9.26563,-73.2837 10.1502,-73.33094 10.9396,-73.33094 11.72691,-74.18114 11.44929,-74.65347 11.0787,-75.40921 10.80045,-75.83431 10.33612,-76.35388 9.31224))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6049701 records from 1201 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000049-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tdwvr7
title: Occurrence Download

description: A dataset containing 1517 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-117.7708209999999980 34.0626415999999992, -117.7708209999999980 34.0652790299999992, -117.7708209999999980 34.0682267500000009, -117.7708209999999980 34.0719501800000018, -117.7708209999999980 34.0756736100000026, -117.7708209999999980 34.0781558999999987, -117.7664697099999955 34.0781558999999987, -117.7635168599999957 34.0781558999999987, -117.7608748399999996 34.0781558999999987, -117.7562124600000004 34.0781558999999987, -117.7535704400000043 34.0781558999999987, -117.7503067699999946 34.0781558999999987, -117.7478201600000034 34.0781558999999987, -117.7450227300000023 34.0781558999999987, -117.7428469500000006 34.0781558999999987, -117.7394278600000064 34.0781558999999987, -117.7366307000000063 34.0781558999999987, -117.7366307000000063 34.0762941799999979, -117.7366307000000063 34.0735016100000010, -117.7366307000000063 34.0700884600000009, -117.7366307000000063 34.0672958899999969, -117.7366307000000063 34.0651238900000024, -117.7366307000000063 34.0626415999999992, -117.7406711699999988 34.0626415999999992, -117.7437794199999956 34.0626415999999992, -117.7465768600000047 34.0626415999999992, -117.7498405300000002 34.0626415999999992, -117.7520163100000019 34.0626415999999992, -117.7546583299999980 34.0626415999999992, -117.7580774099999985 34.0626415999999992, -117.7604086099999989 34.0626415999999992, -117.7627398000000056 34.0626415999999992, -117.7664697099999955 34.0626415999999992, -117.7708209999999980 34.0626415999999992))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 1517 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075350-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2jdbvf
title: Occurrence Download

description: A dataset containing 1560 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Struthio molybdophanes",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 1560 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085598-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.drhzxn
title: Occurrence Download

description: A dataset containing 1282 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-118.3975537000000031 34.1504416000000006, -118.3975537000000031 34.1525255999999970, -118.3975537000000031 34.1554111499999991, -118.3975537000000031 34.1592585400000033, -118.3975537000000031 34.1618234700000016, -118.3975537000000031 34.1637471599999998, -118.3975537000000031 34.1663120899999981, -118.3975537000000031 34.1685563999999999, -118.3925706699999978 34.1685563999999999, -118.3903202599999958 34.1685563999999999, -118.3864624299999946 34.1685563999999999, -118.3814794000000035 34.1685563999999999, -118.3814794000000035 34.1650296199999985, -118.3814794000000035 34.1629456200000021, -118.3814794000000035 34.1600600800000009, -118.3814794000000035 34.1584569999999985, -118.3814794000000035 34.1552508400000008, -118.3814794000000035 34.1533271400000018, -118.3814794000000035 34.1504416000000006, -118.3851764900000063 34.1504416000000006, -118.3893558099999979 34.1504416000000006, -118.3943388400000032 34.1504416000000006, -118.3975537000000031 34.1504416000000006))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 1282 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075367-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mqrb7a
title: Occurrence Download

description: A dataset containing 2473 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juniperus virginiana L.",
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is unknown or invalid"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Continent" : [
 "is North America"
 ]
 }
 }
 ]
}

 The dataset includes 2473 records from 223 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045919-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s59akn
title: Occurrence Download

description: A dataset containing 1963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Plethodon ouachitae Dunn & Heinze, 1933"
 ]
}

 The dataset includes 1963 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0099368-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wpd6y2
title: Occurrence Download

description: A dataset containing 95 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juniperus occidentalis Hook.",
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is unknown or invalid"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Continent" : [
 "is North America"
 ]
 }
 }
 ]
}

 The dataset includes 95 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045914-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.