cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7863294
title: Food Resource Database (FREDA) – all you can eat from past to today

description: The <strong>F</strong>ood <strong>Re</strong>source <strong>Da</strong>tabase (FREDA) is created in the frame of the PhD project of Susanne Krüger. The data collected in FREDA comprise information on edibility, abundance and seasonality of edible resources that occur in natural environments. These resources include plants, mushrooms, invertebrates, such as insects or land snails, as well as small (&lt;10 kg) and large vertebrates (&gt;10 kg). Based on such records it is possible to analyse and quantify food resources in different aspects, such as an analysis of the changing availability of resources in time and space. The availability of resources is considered taking not only natural aspects into account but also the different levels of cultural capacities necessary for the utilization. Cultural capacities of different hominin groups regarding the exploitation of different resources change considerably through time, for example with respect to the use of fire or hunting techniques. The information stored in this database are most detailed with respect to edibility, nutrition, abundance and seasonal availability of resources.
DOI: 10.5281/zenodo.7863293
title: Food Resource Database (FREDA) – all you can eat from past to today

description: The <strong>F</strong>ood <strong>Re</strong>source <strong>Da</strong>tabase (FREDA) is created in the frame of the PhD project of Susanne Krüger. The data collected in FREDA comprise information on edibility, abundance and seasonality of edible resources that occur in natural environments. These resources include plants, mushrooms, invertebrates, such as insects or land snails, as well as small (&lt;10 kg) and large vertebrates (&gt;10 kg). Based on such records it is possible to analyse and quantify food resources in different aspects, such as an analysis of the changing availability of resources in time and space. The availability of resources is considered taking not only natural aspects into account but also the different levels of cultural capacities necessary for the utilization. Cultural capacities of different hominin groups regarding the exploitation of different resources change considerably through time, for example with respect to the use of fire or hunting techniques. The information stored in this database are most detailed with respect to edibility, nutrition, abundance and seasonal availability of resources.
DOI: 10.34740/kaggle/dsv/5690222
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.7937233
title: drosophyllum_lusitanicum_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7937232
title: drosophyllum_lusitanicum_spades_01.fasta.gz

description: .
DOI: 10.15468/xqfc4f
title: Checklist derived from animal species published on GBIF through BID project

description: This checklist was achieved from the animal occurrence data published on GBIF through BID projet
DOI: 10.6084/m9.figshare.22761428
title: Geometric modelling of cranial closing wedge ostectomies

description: VBA macros used for geometrical models of 6 variants of cranial closing wedge ostectomy. The macros are used to model these variants in as-described form, iterated to intended target tibial plateau angle, and with a 2 mm error in placement of the wedge apex. <br> CWTOrobustnessV5 - uses mean landmarks for tibial plateau, tibial diaphysis and centre of talus along with cranial and caudal tibial cortices, to output as-described, iterated, and error models. CWTOrobustnessV5A - uses observer specific landmarks for tibial plateau, tibial diaphysis and centre of talus along with cranial and caudal tibial cortices, to output as-described models only. CollectCWTOerrordata, CollectCWTObaselinedata, CollectACAdata - used to collate data from all tibiae into worksheets for each variant. <br> Coordinate landmark data all observers - contains key landmark data and cranial/caudal tibial cortices, all rotated so the mechanical axis is horizontal and proximal is to the right, for use in the macros listed above. <br> DataAnalysisCollatedMacrov5 - collated data organised by outcome variable of interest, with interim calculations and output from various statistical tests. <br>
DOI: 10.6084/m9.figshare.22761428.v1
title: Geometric modelling of cranial closing wedge ostectomies

description: VBA macros used for geometrical models of 6 variants of cranial closing wedge ostectomy. The macros are used to model these variants in as-described form, iterated to intended target tibial plateau angle, and with a 2 mm error in placement of the wedge apex. <br> CWTOrobustnessV5 - uses mean landmarks for tibial plateau, tibial diaphysis and centre of talus along with cranial and caudal tibial cortices, to output as-described, iterated, and error models. CWTOrobustnessV5A - uses observer specific landmarks for tibial plateau, tibial diaphysis and centre of talus along with cranial and caudal tibial cortices, to output as-described models only. CollectCWTOerrordata, CollectCWTObaselinedata, CollectACAdata - used to collate data from all tibiae into worksheets for each variant. <br> Coordinate landmark data all observers - contains key landmark data and cranial/caudal tibial cortices, all rotated so the mechanical axis is horizontal and proximal is to the right, for use in the macros listed above. <br> DataAnalysisCollatedMacrov5 - collated data organised by outcome variable of interest, with interim calculations and output from various statistical tests. <br>
DOI: 10.5281/zenodo.7937055
title: Data for manuscript "Wildfire Emissions Transport Pathways in the Global Atmosphere"

description: Model results and emission dataset that was used to produce the data presented in the manuscript by Daskalakis et al., submitted in GRL in May 2023.
DOI: 10.5281/zenodo.7937054
title: Data for manuscript "Wildfire Emissions Transport Pathways in the Global Atmosphere"

description: Model results and emission dataset that was used to produce the data presented in the manuscript by Daskalakis et al., submitted in GRL in May 2023.
DOI: 10.5281/zenodo.7937254
title: pittosporum_tobira_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7937253
title: pittosporum_tobira_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7615208
title: AmelHap

description: <strong>AmelHap 1.1.0.f4</strong> 1,328 samples 17,414,346 variants Sequencing reads were aligned to the Amel_HAv3.1 reference genome using BWA-MEM v0.7.17. Reads were sorted with SAMtools v1.9 and duplicates marked (MarkDuplicates) with GATK v4.0.11.0. Variants for each sample were called using GATK’s HaplotypeCaller with the following non-default parameters --ERC GVCF, --sample-ploidy 1 and -A AlleleFraction. Joint variant calling was performed across all samples collated for AmelHap using GATK’s GenomicDBImport and GenotypeGVCFs with --sample-ploidy 1 and a window size of 10 Mb. The AmelHap dataset comprises samples and variants that have passed a series of filters. The first filter excluded variants with quality by depth (QD) less than 20 or greater than 40, or with mapping quality (MQ) less than 50, or with a strand odds ratio (SOR) greater than 3. The second filter set genotypes with depth (DP) greater than 704 or quality (GQ) less than 40 as missing. The third filter removed monomorphic variants. The fourth and final filter retained samples and variants with a minimum call rate of 90%. Unfiltered data for all samples processed is also available in the community repository in the form of individual gVCF files and joint-called raw variants grouped by ENA project accession. Sample metadata is available here: https://doi.org/10.5281/zenodo.7030888 <strong>Citing this resource</strong> If you use this resource please cite both the resource, and the publication describing it: Parejo, Melanie, Talenti, Andrea, Richardson, Matthew, Vignal, Alain, Barnett, Mark, & Wragg, David. (2022). AmelHap (1.1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6983102 Parejo, M., Talenti, A., Richardson, M. <em>et al.</em> AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap. <em>Sci Data</em> <strong>10</strong>, 198 (2023). https://doi.org/10.1038/s41597-023-02097-z
DOI: 10.5281/zenodo.6983102
title: AmelHap

description: <strong>AmelHap 1.1.0.f4</strong> 1,328 samples 17,414,346 variants Sequencing reads were aligned to the Amel_HAv3.1 reference genome using BWA-MEM v0.7.17. Reads were sorted with SAMtools v1.9 and duplicates marked (MarkDuplicates) with GATK v4.0.11.0. Variants for each sample were called using GATK’s HaplotypeCaller with the following non-default parameters --ERC GVCF, --sample-ploidy 1 and -A AlleleFraction. Joint variant calling was performed across all samples collated for AmelHap using GATK’s GenomicDBImport and GenotypeGVCFs with --sample-ploidy 1 and a window size of 10 Mb. The AmelHap dataset comprises samples and variants that have passed a series of filters. The first filter excluded variants with quality by depth (QD) less than 20 or greater than 40, or with mapping quality (MQ) less than 50, or with a strand odds ratio (SOR) greater than 3. The second filter set genotypes with depth (DP) greater than 704 or quality (GQ) less than 40 as missing. The third filter removed monomorphic variants. The fourth and final filter retained samples and variants with a minimum call rate of 90%. Unfiltered data for all samples processed is also available in the community repository in the form of individual gVCF files and joint-called raw variants grouped by ENA project accession. Sample metadata is available here: https://doi.org/10.5281/zenodo.7030888 <strong>Citing this resource</strong> If you use this resource please cite both the resource, and the publication describing it: Parejo, Melanie, Talenti, Andrea, Richardson, Matthew, Vignal, Alain, Barnett, Mark, & Wragg, David. (2022). AmelHap (1.1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6983102 Parejo, M., Talenti, A., Richardson, M. <em>et al.</em> AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap. <em>Sci Data</em> <strong>10</strong>, 198 (2023). https://doi.org/10.1038/s41597-023-02097-z
DOI: 10.20363/zfmk-coll.diptera-2023-02
title: ZFMK Diptera collection
title: The dipteran collection at the Zoological Research Museum Alexander Koenig contains some 300,000 specimens of more than 6000 species.

description: The dipteran collection at the Zoological Research Museum Alexander Koenig contains some 300,000 specimens of more than 6000 species. The majority of specimens are pinned, but also includes of large alcohol collection of Tipulidae s.lat. In addition to 327 primary types (excluding the Phoridae), 32 are represented by syntypes and 295 species represented by secondary types. The holdings include the following important specialist collections: Phoridae (H. Schmitz, E. Beyer, E. Baumann), Tipuloidea (B. Mannheims, H. Mendl), Blephariceridae (B. Mannheims, S. Kitakami), and Sciaridae [F. Lengersdorf - on long term loan to F. Menzel (Deutsches Entomologisches Institut, Müncheberg)]. The collections are focused primarily on the western Palaearctic Region, but important parts of the collections originate also from the Oriental and Arotropical Regions. The collection of the Cologne Zoo is also housed here (permanent loan).
DOI: 10.34740/kaggle/dsv/5690348
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.6353260
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2015

description: <strong># GPM_API 2015</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5281/zenodo.6353259
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2015

description: <strong># GPM_API 2015</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5281/zenodo.6413909
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019

description: <strong># GPM_API 2019</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5281/zenodo.6413908
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019

description: <strong># GPM_API 2019</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.6084/m9.figshare.22656730
title: Supplementary Material for: Unusual cause of thrombocytopenia and renal failure in a 14-year-old boy (MYH9-associated disorders)

description: MYH9-associated disorders represent rare group of autosomal dominant diseases and are caused by pathogenic mutations in the MYH9 gene. Clinically are represented by macro-platelet-thrombocytopenia, various degrees of renal dysfunction, hearing loss and early-onset cataracts. We describe the case of 14-year-old boy in medical follow-up from birth for thrombocytopenia. Systolic hypertension and nephrotic proteinuria were detected at preventive health check. Renal biopsy revealed sing of segmental glomerulosclerosis. Dialysis treatment was needed. Before transplantation due to the finding of chronic tonsillitis with positive bacterial capture in the culture examination, tonsillectomy was indicated. Postoperative period was complicated with arterial post-tonsillectomy haemorrhage. Six months after tonsillectomy the patient underwent primary deceased-donor kidney transplantation without complication. Blood platelets showed fluctuating character in the zone of sever thrombocytopenia. However, no signs of bleeding were present. Three months after successful transplantation gene sequencing of whole exon was performed. The presence of the variant c.2105G&gt; A [p. (Arg702HIS)] in exon 17 of the MYH9 gene has been detected. The variant c.2105G&gt;A may be clinically manifested by progressive proteinuria with rapid deterioration of renal function. This case is an example of the delayed diagnosis of rare disease and highlights the usefulness of genetic testing.
DOI: 10.34740/kaggle/dsv/5690435
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7937250
title: Supplementary data and code for "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators"

description: VMEC equilibria and plotting code for Figure 3 in "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators".
DOI: 10.5281/zenodo.7937249
title: Supplementary data and code for "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators"

description: VMEC equilibria and plotting code for Figure 3 in "Higher order theory of quasi-isodynamicity near the magnetic axis of stellarators".
DOI: 10.5281/zenodo.6413889
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016

description: <strong># GPM_API 2016</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5281/zenodo.6413888
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016

description: <strong># GPM_API 2016</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.4121/9da99710-10b9-4221-a6d1-553f754a522f
title: Dataset underlying the study: High temperature and eutrophication alter biomass allocation of black mangrove seedlings

description: This dataset contains the data collected from mesocosm experiments studying the impact of temperature and eutrophication on black mangrove health and development.
DOI: 10.5281/zenodo.6413905
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017

description: <strong># GPM_API 2017</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.4121/9da99710-10b9-4221-a6d1-553f754a522f.v1
title: Dataset underlying the study: High temperature and eutrophication alter biomass allocation of black mangrove seedlings

description: This dataset contains the data collected from mesocosm experiments studying the impact of temperature and eutrophication on black mangrove health and development.
DOI: 10.5281/zenodo.6413904
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017

description: <strong># GPM_API 2017</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5281/zenodo.6413907
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018

description: <strong># GPM_API 2018</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5281/zenodo.6413906
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018

description: <strong># GPM_API 2018</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5517/ccdc.csd.cc29yxx3
title: CCDC 2143926: Experimental Crystal Structure Determination

description: Related Article: Pascal Komorr, Marian Olaru, Emanuel Hupf, Stefan Mebs, Jens Beckmann|2022|Chem.-Eur.J.|28|e202201023|doi:10.1002/chem.202201023
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.6413911
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020

description: <strong># GPM_API 2020</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5281/zenodo.6413910
title: GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020

description: <strong># GPM_API 2020</strong> GPM_API data root: https://zenodo.org/record/6489998 <strong># Related article:</strong> Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566. Article: https://doi.org/10.1080/01431161.2022.2162351 Free PDF: www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351
DOI: 10.5517/ccdc.csd.cc2b0lml
title: CCDC 2145529: Experimental Crystal Structure Determination

description: Related Article: Jan Schulz, Menyhárt B. Sárosi, Evamarie Hey-Hawkins|2022|Chem.-Eur.J.|28|e202200531|doi:10.1002/chem.202200531
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28zhqh
title: CCDC 2114687: Experimental Crystal Structure Determination

description: Related Article: Jan Schulz, Menyhárt B. Sárosi, Evamarie Hey-Hawkins|2022|Chem.-Eur.J.|28|e202200531|doi:10.1002/chem.202200531
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28zh1t
title: CCDC 2114666: Experimental Crystal Structure Determination

description: Related Article: Jan Schulz, Menyhárt B. Sárosi, Evamarie Hey-Hawkins|2022|Chem.-Eur.J.|28|e202200531|doi:10.1002/chem.202200531
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.