cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26200138
title: Foliar application of agrochemicals enhance growth, productivity, quality and biochemical traits of soybean

description: Flower drop, senescence of flowers and inadequate pod filling pose significant challenges in soybean cultivation as rapid nutrient extraction from leaves during the reproductive stage accelerates leaf aging. The present study assessed the effect of foliar application of agrochemicals on growth, productivity, quality and biochemical traits of soybean at two locations during <i>kharif</i> (rainy) season of 2021 and 2022. The experiment comprised of ten treatments of foliar application of agrochemicals [absolute control, water spray, potassium nitrate (KNO<sub>3</sub>) 1.5%, NPK (19:19:19) 1.5%, boron 0.3%, ammonium molybdate (AM) 0.5%, thiourea 750 parts per million (ppm), urea 2.0%, muriate of potash (MOP) 0.5% and salicylic acid (SA) 100 ppm] at flower and pod initiation stages along with the recommended dose of fertilizer (RDF) in a randomized complete block design replicated thrice. Results revealed that foliar application of boron 0.3%, urea 2.0% and MOP 0.5% at Ludhiana and MOP 0.5%, potassium nitrate 1.5% and urea 2.0% at Ballowal Saunkhri increased growth (plant height and dry matter accumulation), yield attributes (number of pods plant<sup>−1</sup>, number of filled pods plant<sup>−1</sup> and 100-seed weight) and seed yield. These agrochemicals showed positive effects on the biochemical parameters with increased levels of proline and total phenol content as well as improvements in total soluble sugars, starch and protein content. The findings suggest that foliar application of agrochemicals (boron 0.3%, urea 2.0%, MOP 0.5% and potassium nitrate 1.5%) along with RDF can improve soybean growth and seed yield by mitigating the nutrient stress at reproductive stages.
DOI: 10.6084/m9.figshare.26200138.v1
title: Foliar application of agrochemicals enhance growth, productivity, quality and biochemical traits of soybean

description: Flower drop, senescence of flowers and inadequate pod filling pose significant challenges in soybean cultivation as rapid nutrient extraction from leaves during the reproductive stage accelerates leaf aging. The present study assessed the effect of foliar application of agrochemicals on growth, productivity, quality and biochemical traits of soybean at two locations during <i>kharif</i> (rainy) season of 2021 and 2022. The experiment comprised of ten treatments of foliar application of agrochemicals [absolute control, water spray, potassium nitrate (KNO<sub>3</sub>) 1.5%, NPK (19:19:19) 1.5%, boron 0.3%, ammonium molybdate (AM) 0.5%, thiourea 750 parts per million (ppm), urea 2.0%, muriate of potash (MOP) 0.5% and salicylic acid (SA) 100 ppm] at flower and pod initiation stages along with the recommended dose of fertilizer (RDF) in a randomized complete block design replicated thrice. Results revealed that foliar application of boron 0.3%, urea 2.0% and MOP 0.5% at Ludhiana and MOP 0.5%, potassium nitrate 1.5% and urea 2.0% at Ballowal Saunkhri increased growth (plant height and dry matter accumulation), yield attributes (number of pods plant<sup>−1</sup>, number of filled pods plant<sup>−1</sup> and 100-seed weight) and seed yield. These agrochemicals showed positive effects on the biochemical parameters with increased levels of proline and total phenol content as well as improvements in total soluble sugars, starch and protein content. The findings suggest that foliar application of agrochemicals (boron 0.3%, urea 2.0%, MOP 0.5% and potassium nitrate 1.5%) along with RDF can improve soybean growth and seed yield by mitigating the nutrient stress at reproductive stages.
DOI: 10.15468/dl.gwwebz
title: Occurrence Download

description: A dataset containing 24343 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Squamata",
 "TaxonKey is Crocodylia",
 "TaxonKey is Testudines",
 "TaxonKey is Sphenodontia",
 "TaxonKey is Amphibia"
 ]
 },
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "and" : [
 "DecimalLongitude is greater than 74.0926361",
 "DecimalLongitude is less than 81.8784714",
 "DecimalLatitude is greater than 5.9209719",
 "DecimalLatitude is less than 14.889681576672",
 "Country is India"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 24343 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039313-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ygvezn
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-79.03661 74.67627,-57.36716 74.67627,-57.36716 83.12661,-79.03661 83.12661,-79.03661 74.67627))",
 "HasGeospatialIssue is false",
 "TaxonKey is Plantae",
 "Year 1880-1884"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039314-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13886126
title: Watching data

description: Data from student video-watching, using this dataset to predict how long the student will watch a video.
DOI: 10.5281/zenodo.13886127
title: Watching data

description: Data from student video-watching, using this dataset to predict how long the student will watch a video.
DOI: 10.15468/dl.n27mnc
title: Occurrence Download

description: A dataset containing 11551 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Squamata",
 "TaxonKey is Crocodylia",
 "TaxonKey is Testudines",
 "TaxonKey is Sphenodontia",
 "TaxonKey is Amphibia"
 ]
 },
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "and" : [
 "DecimalLongitude is greater than 74.0926361",
 "DecimalLongitude is less than 81.8784714",
 "DecimalLatitude is greater than 5.9209719",
 "DecimalLatitude is less than 14.889681576672",
 "Country is Sri Lanka"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 11551 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039316-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12682387
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Roissy (PAROIS) from 2023-06-26 to 2024-02-22 [RAW]

description: Original data files from ALC measurements at Paris - Roissy Charles de Gaulle Airport.

Measurements were taken on the N roof terrace of the Meteo France Building at Roissy Charles de Gaulle Airport / Rue du Moulin (ID 95527001) in the NE of the built-up area of Greater Paris.

Part 1 of 2.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign.

ACKNOWLEDGEMENTS

Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding; Meteo France for hosting the measurement station.

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project.

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005).

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.12682388
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Paris – Roissy (PAROIS) from 2023-06-26 to 2024-02-22 [RAW]

description: Original data files from ALC measurements at Paris - Roissy Charles de Gaulle Airport.

Measurements were taken on the N roof terrace of the Meteo France Building at Roissy Charles de Gaulle Airport / Rue du Moulin (ID 95527001) in the NE of the built-up area of Greater Paris.

Part 1 of 2.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign.

ACKNOWLEDGEMENTS

Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding; Meteo France for hosting the measurement station.

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project.

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005).

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.13885936
title: Data underlying the research paper "Articulating Social Issues with Open Data: Exploring a Game Jam Approach"

description: Contains research data underlying the following research paper:



Davide Di Staso, Lærke Christiansen, Fernando Kleiman, and Marijn Janssen. 2024. Articulating Social Issues with Open Data: Exploring a Game Jam Approach. In Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events (ICGJ ’24), October 11, 2024, Copenhagen, Denmark. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3697789.3697798


The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).
DOI: 10.5281/zenodo.13885937
title: Data underlying the research paper "Articulating Social Issues with Open Data: Exploring a Game Jam Approach"

description: Contains research data underlying the following research paper:



Davide Di Staso, Lærke Christiansen, Fernando Kleiman, and Marijn Janssen. 2024. Articulating Social Issues with Open Data: Exploring a Game Jam Approach. In Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events (ICGJ ’24), October 11, 2024, Copenhagen, Denmark. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3697789.3697798


The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).
DOI: 10.15468/dl.vcsvc2
title: Occurrence Download

description: A dataset containing 1098 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Senecio brasiliensis (Spreng.) Less."
 ]
}

 The dataset includes 1098 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039318-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13886153
title: Raw pollen, cereal, parasite and npp data from Lagore Ireland
DOI: 10.5281/zenodo.12744173
title: Data from Automatic Lidar and Ceilometer (ALC) calibration measurements in the greater Paris area [RAW]

description: Original data files from ALC measurements from calibration periods. A calibration period is when a calibration termination hood was placed on the ALC, typically interrupting a longer-term standard deployment
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign.

ACKNOWLEDGEMENTS

Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding.

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.12744174
title: Data from Automatic Lidar and Ceilometer (ALC) calibration measurements in the greater Paris area [RAW]

description: Original data files from ALC measurements from calibration periods. A calibration period is when a calibration termination hood was placed on the ALC, typically interrupting a longer-term standard deployment
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign.

ACKNOWLEDGEMENTS

Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding.

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.6084/m9.figshare.27160017.v2
title: EHR Actual Use

description: This study investigated factors influencing nurses' use of electronic health record (EHR) systems in resource-limited settings in Botswana, and the authors propose strategies for improved adoption and utilization. Despite EHR benefits, many nurses in resource-limited settings still use paper-based systems, hindering health care delivery and patient outcomes.
DOI: 10.48420/27015289
title: <b>A repository-integrated quantitative magnetic resonance imaging data analysis platform for enabling multi-centre clinical studies: application and measurable impact in an oxygen-enhanced MRI validation study of head and neck cancer</b>

description: Variation in procedures for the transfer, analysis and quality control of data between sites in multi-centre imaging studies hinders reproducibility and makes translation of novel biomarkers to clinical practice more challenging. This work aimed to demonstrate a streamlined and harmonised workflow for data analysis and quality control that could be applied to magnetic resonance imaging data in a multi-centre clinical trial. A data processing pipeline reporting quantitative magnetic resonance imaging biomarkers, and the integration of these processes within the XNAT imaging repository using a software container platform was demonstrated in an exemplar patient with head-and-neck cancer imaged using a multi-parametric MRI scan. The developed container is highly customisable and facilitates a host of pre-processing techniques such as sorting, scaling and conversion of DICOM images, as well as image and region of interest registration. Data analysis can be performed to derive common quantitative imaging biomarkers, such as T1 relaxation time, apparent diffusion coefficient and various parameters obtained with intravoxel incoherent motion mapping, dynamic contrast-enhanced MRI, and more bespoke analyses such as oxygen-enhanced hypoxia mapping. The work presented here achieves this by utilising the Madym C++ quantitative MRI toolkit as its base. We provide an overview of data analysis container design and the interface within the image repository, along with a demonstration of user configuration options and example output. The main limitations of this study are that containerisation and integration of the software was performed using a single combination of container engine and imaging repository, and the workflow is yet to be tested in a multi-centre trial. Improvements to the repeatability of data acquisition and analysis from using a standardised, locked-down and traceable approach in multi-centre clinical studies, supports the translation of both novel and more established quantitative imaging biomarkers from research to clinical practice.Access to data used in this study can be requested by following this link: https://qbi-xnat.manchester.ac.uk and clicking 'Register' or by emailing qbilab@manchester.ac.uk.
DOI: 10.48420/27015289.v1
title: <b>A repository-integrated quantitative magnetic resonance imaging data analysis platform for enabling multi-centre clinical studies: application and measurable impact in an oxygen-enhanced MRI validation study of head and neck cancer</b>

description: Variation in procedures for the transfer, analysis and quality control of data between sites in multi-centre imaging studies hinders reproducibility and makes translation of novel biomarkers to clinical practice more challenging. This work aimed to demonstrate a streamlined and harmonised workflow for data analysis and quality control that could be applied to magnetic resonance imaging data in a multi-centre clinical trial. A data processing pipeline reporting quantitative magnetic resonance imaging biomarkers, and the integration of these processes within the XNAT imaging repository using a software container platform was demonstrated in an exemplar patient with head-and-neck cancer imaged using a multi-parametric MRI scan. The developed container is highly customisable and facilitates a host of pre-processing techniques such as sorting, scaling and conversion of DICOM images, as well as image and region of interest registration. Data analysis can be performed to derive common quantitative imaging biomarkers, such as T1 relaxation time, apparent diffusion coefficient and various parameters obtained with intravoxel incoherent motion mapping, dynamic contrast-enhanced MRI, and more bespoke analyses such as oxygen-enhanced hypoxia mapping. The work presented here achieves this by utilising the Madym C++ quantitative MRI toolkit as its base. We provide an overview of data analysis container design and the interface within the image repository, along with a demonstration of user configuration options and example output. The main limitations of this study are that containerisation and integration of the software was performed using a single combination of container engine and imaging repository, and the workflow is yet to be tested in a multi-centre trial. Improvements to the repeatability of data acquisition and analysis from using a standardised, locked-down and traceable approach in multi-centre clinical studies, supports the translation of both novel and more established quantitative imaging biomarkers from research to clinical practice.Access to data used in this study can be requested by following this link: https://qbi-xnat.manchester.ac.uk and clicking 'Register' or by emailing qbilab@manchester.ac.uk.
DOI: 10.57745/8vjhgy
title: metadata.ods

description: sample description for importing in MCQR package
DOI: 10.57745/k4mawa
title: XIC_result_prot.tab

description: Link between protein and peptide id for Xic Based data
DOI: 10.57745/w4dq3o
title: spectral_counting.tab

description: Spectral count data from i2masschroq
DOI: 10.57745/2njh0q
title: filled_metadata.ods

description: sample description for importing in MCQR package
DOI: 10.57745/5izsc7
title: XIC_result_pep.tab

description: Xic quantification data at peptide levels generated from MassChroQ software
DOI: 10.57745/jc0jei
title: spectral_counting.tab

description: Spectral count data from i2masschroq
DOI: 10.57745/ptyud0
title: all_msrun_proteins.tab

description: Link between protein and peptide id for Xic Based data
DOI: 10.57745/simqwv
title: peptides_q1_all_msrun.tab

description: Xic quantification data at peptide levels generated from MassChroQ software
DOI: 10.57745/wevg8t
title: spectral_counting.tab

description: Spectral count data from i2masschroq
DOI: 10.57745/r5pcoe
title: trace.zip

description: trace files generated by i2MassChroQ after retention time alignment
DOI: 10.15468/dl.mkj82s
title: Occurrence Download

description: A dataset containing 15091 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Colocasia esculenta (L.) Schott"
 ]
}

 The dataset includes 15091 records from 313 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039319-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/rzf-xjp-bem
title: Lomographa temerata ([Denis & Schiffermüller], 1775)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/t9p-swz-hfp
title: Cabera exanthemata (Scopoli, 1763)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.