cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/pp5d-1036
title: mnist cifar10 fmnist

description: We evaluate the performance of our proposed protocol using three benchmark datasets. Each dataset is composed of 25 percent of the local data forming the test dataset and 75 percent of the local data forming the training dataset.MNIST: The dataset is made up of gray pictures that are digits which are written by hand containing ten different classes which provides 60000 training samples in total.Fashion-MNIST: Different from MNIST which is a dataset that consists of handwritten digits, Fashion-MNIST is a dataset that contains 10 different classes of images that are mainly fashion clothing products.CIFAR-10: Different from the datasets above, CIFAR-10 is a dataset that has a total of 60000 color images that can be divided into 10 categories. Each sample is a 32*32 color image.
DOI: 10.15468/dl.7qg58b
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Endecatomus reticulatus (Herbst, 1793)"
 ]
}

 The dataset includes 16 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042394-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/rtzbku0q
title: Aphidoidea Slide Collection

description: The Aphidoidea collection comprises the records of all the slide-mounted Aphidoidea material at the Natural History Museum, London at the time of the project completion (December 2023). This includes the main collection and discrete collection of Frederick Vincent Theobald.

Each specimen record has been published with an image capturing both the specimen and its associated labels and creating an inventory record with a unique identifier (Data Matrix barcode), taxon information, type status, country level collection locality. Additional images were taken to capture any information on the reverse of the slides and/or the paper envelopes that house the slides.

The data resource generated by the project is available using the link. Please search for the relevant taxa as this dataset if part of a larger project covering a range of Orders and Superfamilies.

This collection was digitised as part of the Museum's Digital Collections Programme.
DOI: 10.5519/c48sg64n
title: Data mobilisation for IUCN conservation assessments of global freshwater bioindicators

description: The project "Data mobilisation for IUCN conservation assessments of global freshwater bioindicators" involved the rehousing, imaging and label data transcription of all the Ephemeroptera, Plecoptera and Trichoptera specimens belonging at the Natural History Museum, London at the time of project completion (March 2022). In addition, it included the label data transcription of the slide mounted and wet material of specimens of the families mentioned above, creating a valuable resource for bioindication and conservation assessments. The output of this project is part of a much larger dataset with the same name, to which two other European institutions also contribute.

This collection was digitised as part of the Museum's Digital Collections Programme and was funded by Synthesys+ Virtual Access.
DOI: 10.5519/ns24w6om
title: Ceratopogonidae Slide Collection

description: The Ceratopogonidae Slide Collection comprises the records of all the slide-mounted Ceratopogonidae (Diptera) material at the Natural History Museum, London at the time of project completion (August 2022). This collection comprises substantial amounts of material from John Boorman.

Each specimen record has been published with an image capturing both the specimen and its associated labels and creating an inventory record with a unique identifier (Data Matrix barcode), taxon information, type status, country level collection locality. Additional images were taken to capture any information on the reverse of the slides and/or the paper envelopes that house the slides.

This collection was digitised as part of the Museum's Digital Collections Programme.
DOI: 10.15468/dl.esksby
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Côte d’Ivoire",
 "TaxonKey is Gymnema sylvestre (Retz.) R.Br. ex Sm."
 ]
}

 The dataset includes 40 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042395-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7097278
title: Preserving and sharing born-digital and hybrid objects from and across the National Collection (January 2022)

description: This report is one of a set of outputs from the Arts and Humanities Research Council funded project ‘Preserving and sharing born-digital and hybrid objects from and across the National Collection’. It has been designed to provide an extensive account of the project research activities and findings, to be useful to museum, heritage, and preservation professionals, as well as to scholars interested in born-digital materials.
DOI: 10.5281/zenodo.7097279
title: Preserving and sharing born-digital and hybrid objects from and across the National Collection (January 2022)

description: This report is one of a set of outputs from the Arts and Humanities Research Council funded project ‘Preserving and sharing born-digital and hybrid objects from and across the National Collection’. It has been designed to provide an extensive account of the project research activities and findings, to be useful to museum, heritage, and preservation professionals, as well as to scholars interested in born-digital materials.
DOI: 10.5281/zenodo.7079534
title: People and machines: co creating with heritage collections

description: Machine-learning technologies such as Handwritten Text Recognition (HTR) are beginning to be used to open and transform cultural heritage material, enabling search, discovery and the creation of linked data. Whereas in 2014, Ridge described cultural heritage crowdsourcing projects as asking the public to undertake tasks that cannot be done automatically,1 today, some projects are asking volunteers to undertake tasks which could be completed by a machine with a high level of speed and accuracy. A deeper exploration of the opportunities and challenges presented by increased incorporation of machine-learning technologies into crowdsourcing projects has yet to be undertaken. This workshop was designed to start these discussions.
The ‘People and Machines’ interdisciplinary workshop explored the best routes to fuse digital innovations with the dedication and enthusiasm of volunteers through discussion of what makes an effective crowdsourcing task, how to maintain volunteer motivation, and methods of supporting volunteers to produce useful data, both as part of traditional crowdsourcing, and as part of workflows incorporating machine learning.
This report uses the term ‘machine learning’ to refer to a range of technical approaches which use statistical models and algorithms to analyse and draw inferences from patterns in data. Machine learning models and algorithms ‘learn’ from the data they are given, and autonomously adapt and improve their accuracy in response. Machine learning, which includes supervised learning, unsupervised learning, and deep learning, is a subfield of Artificial Intelligence.
DOI: 10.5281/zenodo.7079535
title: People and machines: co creating with heritage collections

description: Machine-learning technologies such as Handwritten Text Recognition (HTR) are beginning to be used to open and transform cultural heritage material, enabling search, discovery and the creation of linked data. Whereas in 2014, Ridge described cultural heritage crowdsourcing projects as asking the public to undertake tasks that cannot be done automatically,1 today, some projects are asking volunteers to undertake tasks which could be completed by a machine with a high level of speed and accuracy. A deeper exploration of the opportunities and challenges presented by increased incorporation of machine-learning technologies into crowdsourcing projects has yet to be undertaken. This workshop was designed to start these discussions.
The ‘People and Machines’ interdisciplinary workshop explored the best routes to fuse digital innovations with the dedication and enthusiasm of volunteers through discussion of what makes an effective crowdsourcing task, how to maintain volunteer motivation, and methods of supporting volunteers to produce useful data, both as part of traditional crowdsourcing, and as part of workflows incorporating machine learning.
This report uses the term ‘machine learning’ to refer to a range of technical approaches which use statistical models and algorithms to analyse and draw inferences from patterns in data. Machine learning models and algorithms ‘learn’ from the data they are given, and autonomously adapt and improve their accuracy in response. Machine learning, which includes supervised learning, unsupervised learning, and deep learning, is a subfield of Artificial Intelligence.
DOI: 10.5281/zenodo.7079082
title: Citizen research landscape documentation

description: This report reflects on existing citizen research activity in the UK heritage sector by summarising both published research and current citizen research activity at Independent Research Organisations (IROs) and other heritage organisations, addressing the following questions:
How do institutions and volunteers experience online citizen research; what are the motivations and rewards? Were projects successful in what they set out to do? What challenges have projects faced? What recommendations can be made based on the experiences of these projects?
In December 2020, we circulated a call for information from cultural heritage practitioners in the UK running citizen research projects online. This report draws on the responses generously sent in answer to our call, focusing primarily on online projects based in the UK. A full list of the featured projects can be found in the Appendix. In July 2021, members of the Engaging Crowds project team coordinated a workshop at the Discovering Collections, Discovering Communities (DCDC) conference to gather insights from practitioners and researchers with experience of enabling and supporting online volunteering. The discussions from this workshop are also summarised in this report.
A broad range of experience of citizen research in the UK is included in this report, from small-scale short-term projects, such as those set up to move pre-established volunteering projects online in response to the onset of the Covid-19 pandemic, to large-scale long-term citizen research projects. What is evident from all responses received to the call for information is that online citizen research projects bring many benefits to both volunteers and organisations, and also present a number of challenges. Successes relate to volunteer recruitment and engagement, as well as opportunities for increased data production and data quality. Data quality, however, was also reported by a number of respondents as an area of challenge, together with issues relating to chosen citizen research platforms and open access. In synthesising the experiences reported by responding projects and workshop participants, this report has identified a number of recommendations for future citizen research projects.
DOI: 10.5281/zenodo.7079083
title: Citizen research landscape documentation

description: This report reflects on existing citizen research activity in the UK heritage sector by summarising both published research and current citizen research activity at Independent Research Organisations (IROs) and other heritage organisations, addressing the following questions:
How do institutions and volunteers experience online citizen research; what are the motivations and rewards? Were projects successful in what they set out to do? What challenges have projects faced? What recommendations can be made based on the experiences of these projects?
In December 2020, we circulated a call for information from cultural heritage practitioners in the UK running citizen research projects online. This report draws on the responses generously sent in answer to our call, focusing primarily on online projects based in the UK. A full list of the featured projects can be found in the Appendix. In July 2021, members of the Engaging Crowds project team coordinated a workshop at the Discovering Collections, Discovering Communities (DCDC) conference to gather insights from practitioners and researchers with experience of enabling and supporting online volunteering. The discussions from this workshop are also summarised in this report.
A broad range of experience of citizen research in the UK is included in this report, from small-scale short-term projects, such as those set up to move pre-established volunteering projects online in response to the onset of the Covid-19 pandemic, to large-scale long-term citizen research projects. What is evident from all responses received to the call for information is that online citizen research projects bring many benefits to both volunteers and organisations, and also present a number of challenges. Successes relate to volunteer recruitment and engagement, as well as opportunities for increased data production and data quality. Data quality, however, was also reported by a number of respondents as an area of challenge, together with issues relating to chosen citizen research platforms and open access. In synthesising the experiences reported by responding projects and workshop participants, this report has identified a number of recommendations for future citizen research projects.
DOI: 10.5281/zenodo.7151973
title: User engagement analysis report

description: This study looks at classification (transcribed) data from the three citizen research projects in Engaging Crowds, which were set up on the Zooniverse platform: HMS NHS: The Nautical Health Service, Scarlets and Blues and The RBGE Herbarium: Exploring Gesneriaceae, the African violet family.


Each project presents volunteers with a series of tasks (known as ‘workflows’ in Zooniverse terminology). The projects and their workflows are summarised in Tables 1, 2 and 3 below. From these we can see that HMS NHS is the largest and longest-running of the three projects and so gives us our best sample of volunteer behaviour. We can also see that the workflows in The RBGE Herbarium were completed most quickly by volunteers, possibly aided by the project’s familiarity: it is perhaps most similar to the majority of Zooniverse projects, which deal with observational scientific data in simple workflows with short tasks. Volunteers tended to contribute to just one of the Engaging Crowds projects. Within the data analysed, only 13 volunteers contributed to all three projects.
DOI: 10.5281/zenodo.7151974
title: User engagement analysis report

description: This study looks at classification (transcribed) data from the three citizen research projects in Engaging Crowds, which were set up on the Zooniverse platform: HMS NHS: The Nautical Health Service, Scarlets and Blues and The RBGE Herbarium: Exploring Gesneriaceae, the African violet family.


Each project presents volunteers with a series of tasks (known as ‘workflows’ in Zooniverse terminology). The projects and their workflows are summarised in Tables 1, 2 and 3 below. From these we can see that HMS NHS is the largest and longest-running of the three projects and so gives us our best sample of volunteer behaviour. We can also see that the workflows in The RBGE Herbarium were completed most quickly by volunteers, possibly aided by the project’s familiarity: it is perhaps most similar to the majority of Zooniverse projects, which deal with observational scientific data in simple workflows with short tasks. Volunteers tended to contribute to just one of the Engaging Crowds projects. Within the data analysed, only 13 volunteers contributed to all three projects.
DOI: 10.15468/dl.e27fxx
title: Occurrence Download

description: A dataset containing 92100 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation)",
 "Geometry POLYGON((-7.94833 43.93047,-9.31511 43.25064,-9.51272 43.0464,-9.19984 42.21042,-8.9199 41.70836,-8.32708 42.03943,-8.26121 41.72065,-7.78365 41.73294,-7.2073 41.72065,-6.97676 41.85571,-6.63094 42.35661,-6.48274 42.76899,-6.68035 43.28661,-7.04263 43.79988,-7.68485 43.88302,-7.94833 43.93047))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Arenaria interpres (Linnaeus, 1758), Burhinus oedicnemus (Linnaeus, 1758), Calidris alba (Pallas, 1764), Calidris alpina (Linnaeus, 1758), Calidris canutus (Linnaeus, 1758), Charadrius alexandrinus Linnaeus, 1758, Charadrius hiaticula Linnaeus, 1758, Haematopus ostralegus Linnaeus, 1758, Limosa limosa (Linnaeus, 1758), Limosa lapponica (Linnaeus, 1758), Lymnocryptes minimus (Brünnich, 1764), Numenius arquata (Linnaeus, 1758), Numenius phaeopus (Linnaeus, 1758), Calidris pugnax (Linnaeus, 1758), Pluvialis squatarola (Linnaeus, 1758), Pluvialis apricaria (Linnaeus, 1758), Recurvirostra avosetta Linnaeus, 1758, Scolopax rusticola Linnaeus, 1758, Tringa nebularia (Gunnerus, 1767), Tringa totanus (Linnaeus, 1758))",
 "Year 2013-2023"
 ]
}

 The dataset includes 92100 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042396-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6dvfbw
title: Occurrence Download

description: A dataset containing 28876 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chamaecrista fasciculata (Michx.) Greene"
 ]
}

 The dataset includes 28876 records from 254 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042397-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6yq3nh
title: Occurrence Download

description: A dataset containing 716 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Pteropus conspicillatus Gould, 1850",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 716 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002488-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cusvmn
title: Occurrence Download

description: A dataset containing 29350 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sphagnum magellanicum Brid."
 ]
}

 The dataset includes 29350 records from 213 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0244549-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vsxurw
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eleocharis platypus C.B.Clarke"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0155705-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a4fuq9
title: Occurrence Download

description: A dataset containing 125849 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Stagnicola Jeffreys, 1830, Taraxacum Weber, Taraxacum sellandii Dahlst., Taraxacum serotinum (Waldst. & Kit.) Poir., Taraxacum stevenii (Spreng.) DC., Taraxacum tenebricans (Dahlst.) Dahlst., Taraxacum trilobatum Palmgr., Taraxacum undulatum H.H.Lindb. & Markl., Taraxacum vastisectum Markl. ex Puol., Taraxacum vetteri Soest)",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "DatasetKey is one of (Pl@ntNet automatically identified occurrences, Pl@ntNet observations, iNaturalist Research-grade Observations, Observation.org, Nature data from around the World, naturgucker, Artportalen (Swedish Species Observation System), Norwegian Species Observation Service, Programme d'observation naturaliste participative avec l'application mobile INPN Espèces - Application INPN Espèces: Observations naturalistes, participatives et opportunistes, fondées sur des photographies, Species recordings from the Danish National portal Arter.dk, Lajitietokeskus/FinBIF - Notebook, general observations, Vascular plant records verified via iRecord, iRecord Surveys)",
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 500",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 125849 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071137-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.johfvo
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Unknown evidence"
 ]
 },
 {
 "or" : [
 "Country is India",
 "Country is Bangladesh",
 "Country is Somalia",
 "Country is United Arab Emirates",
 "Country is Oman",
 "Country is Sri Lanka",
 "Country is Pakistan",
 "Country is Yemen"
 ]
 },
 "Year 1960-2019",
 "HasCoordinate is true",
 "TaxonKey is Sardinella longiceps Valenciennes, 1847",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 35 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002484-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4mwzfn
title: Occurrence Download

description: A dataset containing 721536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sphagnum L."
 ]
}

 The dataset includes 721536 records from 647 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0244530-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zjkj9n
title: Occurrence Download

description: A dataset containing 24970 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pomoxis annularis Rafinesque, 1818)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 24970 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071887-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uyzdzd
title: Occurrence Download

description: A dataset containing 665 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poeciliopsis turrubarensis (Meek, 1912)"
 ]
}

 The dataset includes 665 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013974-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.