cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.qp57ph
title: Occurrence Download

description: A dataset listing the 121 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, EOD – eBird Observation Dataset)",
 "Geometry POLYGON((-124.86818 49.55066,-124.86994 49.54961,-124.86611 49.54433,-124.86334 49.54008,-124.86215 49.53817,-124.86013 49.53529,-124.85764 49.53252,-124.85442 49.52907,-124.85019 49.52437,-124.8481 49.52262,-124.84435 49.52037,-124.84128 49.51892,-124.83714 49.51603,-124.83623 49.513,-124.83501 49.51139,-124.83261 49.50942,-124.83032 49.50748,-124.82826 49.50538,-124.82641 49.50383,-124.82448 49.50194,-124.82445 49.49828,-124.82336 49.49693,-124.82011 49.49371,-124.8168 49.49127,-124.81149 49.48919,-124.81063 49.48859,-124.8091 49.48729,-124.80778 49.48579,-124.80475 49.48875,-124.80176 49.49083,-124.80351 49.49204,-124.80764 49.48944,-124.80961 49.49068,-124.81527 49.49244,-124.82236 49.49883,-124.82225 49.50266,-124.83024 49.50997,-124.83437 49.51336,-124.83503 49.51709,-124.84076 49.52081,-124.84836 49.52567,-124.85734 49.53535,-124.86205 49.54265,-124.86818 49.55066))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2014-2024"
 ]
}

 The dataset's 121 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017769-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sanxrh
title: Occurrence Download

description: A dataset containing 6407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Andorra, Albania, Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czechia, Germany, Denmark, Algeria, Estonia, Egypt, Greece, Spain, Finland, Faroe Islands, France, Georgia, Guernsey, Gibraltar, Croatia, Hungary, Ireland, Israel, Isle of Man, Iceland, Italy, Jersey, Lebanon, Liechtenstein, Lithuania, Luxembourg, Latvia, Libya, Morocco, Monaco, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Palestine, State of, Portugal, Romania, Serbia, Russian Federation, Sweden, Slovenia, Svalbard and Jan Mayen, Slovakia, San Marino, Syrian Arab Republic, Tunisia, Türkiye, Ukraine, United Kingdom of Great Britain and Northern Ireland, French Guiana, Guadeloupe, Martinique, Mayotte, Réunion, Saint Martin (French part), Holy See)",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Machine Observation, Occurrence evidence, Observation)",
 "ScientificName is one of (Asclepias syriaca, Asclepias syriaca L.)"
 ]
}

 The dataset includes 6407 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017776-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fgk42n93ks.1
title: Bayesian network analysis for maintenance strategy selection

description: Maintenance strategy selection plays a key role in the efficient management of industrial assets. The objective of this research was to develop a Bayesian network-based maintenance strategy selection model using criteria from the scientific literature.
DOI: 10.15468/dl.auanv8
title: Occurrence Download

description: A dataset containing 6407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Andorra, Albania, Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czechia, Germany, Denmark, Algeria, Estonia, Egypt, Greece, Spain, Finland, Faroe Islands, France, Georgia, Guernsey, Gibraltar, Croatia, Hungary, Ireland, Israel, Isle of Man, Iceland, Italy, Jersey, Lebanon, Liechtenstein, Lithuania, Luxembourg, Latvia, Libya, Morocco, Monaco, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Palestine, State of, Portugal, Romania, Serbia, Russian Federation, Sweden, Slovenia, Svalbard and Jan Mayen, Slovakia, San Marino, Syrian Arab Republic, Tunisia, Türkiye, Ukraine, United Kingdom of Great Britain and Northern Ireland, French Guiana, Guadeloupe, Martinique, Mayotte, Réunion, Saint Martin (French part), Holy See)",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Machine Observation, Occurrence evidence, Observation)",
 "ScientificName is one of (Asclepias syriaca, Asclepias syriaca L.)"
 ]
}

 The dataset includes 6407 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017777-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10822629
title: NemNet try out data set

description: This data set contains publicly available monitoring data of NEMNET

National Environmental Monitoring Network | Grootschalige wetenschappelijke infrastructuur (onderzoeksfaciliteiten.nl)
DOI: 10.5281/zenodo.10822630
title: NemNet try out data set

description: This data set contains publicly available monitoring data of NEMNET

National Environmental Monitoring Network | Grootschalige wetenschappelijke infrastructuur (onderzoeksfaciliteiten.nl)
DOI: 10.5281/zenodo.12697812
title: Real-time and programmable transcriptome sequencing with PROFIT-seq

description: Source data for our PROFIT-seq manuscript.
DOI: 10.5281/zenodo.10727555
title: Electrical and hydraulic properties estimation from complex conductivity measurements of limestones from Beauce aquifer (O-ZNS, France)

description: This the dataset (.zip) contains the data used in paper and supplementary materials of the same name intended to be submitted at Water Resource Research. In this folder you will find multiple datasets of different format including SIP measurements of carbonate samples, hydraulic parameters (porosity, permeability) measurements and image analysis (CT-scans and SEM) data. There is a MATLAB code allowing to read the raw data (SIP data) regrouped according the water conductivity. The same code also allows to obtain the different plots find in the paper and supplementary materials based on the processed data in excel files. Then, a Python code locating in the folder ‘’Fitting Cole-Cole model’’ allows to fit and compute Cole-Cole parameters. Finally, in the ‘’Image analysis’’ folder, there excel files showing the results of image analysis find in the paper and supplementary materials.
DOI: 10.5281/zenodo.10727556
title: Electrical and hydraulic properties estimation from complex conductivity measurements of limestones from Beauce aquifer (O-ZNS, France)

description: This the dataset (.zip) contains the data used in paper and supplementary materials of the same name intended to be submitted at Water Resource Research. In this folder you will find multiple datasets of different format including SIP measurements of carbonate samples, hydraulic parameters (porosity, permeability) measurements and image analysis (CT-scans and SEM) data. There is a MATLAB code allowing to read the raw data (SIP data) regrouped according the water conductivity. The same code also allows to obtain the different plots find in the paper and supplementary materials based on the processed data in excel files. Then, a Python code locating in the folder ‘’Fitting Cole-Cole model’’ allows to fit and compute Cole-Cole parameters. Finally, in the ‘’Image analysis’’ folder, there excel files showing the results of image analysis find in the paper and supplementary materials.
DOI: 10.15468/dl.kbxrfq
title: Occurrence Download

description: A dataset containing 6407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Andorra, Albania, Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czechia, Germany, Denmark, Algeria, Estonia, Egypt, Greece, Spain, Finland, Faroe Islands, France, Georgia, Guernsey, Gibraltar, Croatia, Hungary, Ireland, Israel, Isle of Man, Iceland, Italy, Jersey, Lebanon, Liechtenstein, Lithuania, Luxembourg, Latvia, Libya, Morocco, Monaco, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Palestine, State of, Portugal, Romania, Serbia, Russian Federation, Sweden, Slovenia, Svalbard and Jan Mayen, Slovakia, San Marino, Syrian Arab Republic, Tunisia, Türkiye, Ukraine, United Kingdom of Great Britain and Northern Ireland, French Guiana, Guadeloupe, Martinique, Mayotte, Réunion, Saint Martin (French part), Holy See)",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Machine Observation, Occurrence evidence, Observation)",
 "ScientificName is one of (Asclepias syriaca, Asclepias syriaca L.)"
 ]
}

 The dataset includes 6407 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017779-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6aywve
title: Occurrence Download

description: A dataset containing 57 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "GadmGid is CHN.19_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ribes diacanthum Pall."
 ]
}

 The dataset includes 57 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017780-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10165940
title: Pizzaïolo Dataset

description: Pizzaïolo Dataset



This dataset contains 4800 samples of synthetic pizza images generated from and annotated with the Pizzaïolo Ontology.


The synthetic pizza images were generated with the Pizzaiolo Python library.

 

If you use Pizzaiolo or the Pizzaïolo Dataset, please cite as :






Grégory Bourguin, Arnaud Lewandowski. Pizzaïolo Dataset : Des Images Synthétiques Ontologiquement Explicables. https://hal.science/hal-04401953.



Directories

csv/ :







pizzaiolo_dataset.csv : details for all the samples in the dataset

pizzaiolo_train.csv : partition for training

pizzaiolo_valid.csv : partition for validating

pizzaiolo_test.csv : partition for testing


images/ :







4800 pizza images (224*224) : 1 file / sample -> img_XXXXX.png NB: icons used to generate the images are coming from https://www.flaticon.com/.


ontology/ :







pizzaiolo.xml : the Pizzaïolo Ontology (OWL) used to generate the samples.NB: this ontology was derived from [1], and built/manipulated with [2].



labels/ :



Concepts Encoding : concepts.json

Bounding Boxes : 1 file / sample -> img_XXXXX_bboxes.json

Contours : 1 file / sample -> img_XXXXX_contours.json

Semantic Segmentation : 1 file / sample -> img_XXXXX_segmentation.txt



sample_annotations/ :







Images showing examples of samples with provided annnotations



Concepts


The concepts are the elements constituting pizzas according to the Pizzaïolo Ontology (i.e. the pizza base, the pizza toppings, and the - optional - country of origin).

 

The concepts.json file contains a Python dict for concepts encoding :



key(s) : a concept id (uint)

value(s) : the concept name (string) (i.e. the short name of the concept class in the Pizzaïolo Ontology)


Bouding Boxes
The bounding boxes represent the localization of the concepts instances constituting a pizza sample.

Each img_XXXXX_bboxes.json file contains a Python dict:



key(s) : the name (string) of each concept class present in the sample

value(s) : list of all the bouding boxes for the corresponding concept key


 

NB: each bounding box is encoded as a Python (sub)list : [ x_left, y_top, width, height ]


Contours
The contours represent the localization and shape of the concepts instances constituting a pizza sample.

Each img_XXXXX_contours.json file contains a Python dict :



key(s) : the name (string) of each concept class present in the sample

value(s) : a list of all the contours for the corresponding concept key



NB: contours are encoded as OpenCV Contours.


Semantic Segmentation
Each img_XXXXX_segmentation.txt file contains a Python Numpy array (dtype=uint)



the shape of the array is the (2D) size of the samples (224*224)

each "pixel" belongs to a concept encoded according to concepts.json.


References




[1] Horridge, M. (2011). Protégé OWL Tutorial | OWL research at the University of Manchester.


[2] Lamy, J.-B. (2017). Owlready : Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. Artificial Intelligence in Medicine 80, 11–28.
DOI: 10.5281/zenodo.10555618
title: Monitoring Campaign of Environmental Data in Spotrs Centres of Catalonia, Spain

description: The dataset contains the environmental data collected during a monitoring campaign between 2022 and 2023 in Catalonia, Spain. The work is further described in the research paper "Calculating comfort indexes and applying comfort models to predict thermal sensation vote in sports centres" published in Indoor Air by Hindawi-Wiley (https://doi.org/10.1155/1970/9142303).
DOI: 10.5281/zenodo.10555619
title: Monitoring Campaign of Environmental Data in Spotrs Centres of Catalonia, Spain

description: The dataset contains the environmental data collected during a monitoring campaign between 2022 and 2023 in Catalonia, Spain. The work is further described in the research paper "Calculating comfort indexes and applying comfort models to predict thermal sensation vote in sports centres" published in Indoor Air by Hindawi-Wiley (https://doi.org/10.1155/1970/9142303).
DOI: 10.5281/zenodo.10165941
title: Pizzaïolo Dataset

description: Pizzaïolo Dataset



This dataset contains 4800 samples of synthetic pizza images generated from and annotated with the Pizzaïolo Ontology.


The synthetic pizza images were generated with the Pizzaiolo Python library.

 

If you use Pizzaiolo or the Pizzaïolo Dataset, please cite as :






Grégory Bourguin, Arnaud Lewandowski. Pizzaïolo Dataset : Des Images Synthétiques Ontologiquement Explicables. https://hal.science/hal-04401953.



Directories

csv/ :







pizzaiolo_dataset.csv : details for all the samples in the dataset

pizzaiolo_train.csv : partition for training

pizzaiolo_valid.csv : partition for validating

pizzaiolo_test.csv : partition for testing


images/ :







4800 pizza images (224*224) : 1 file / sample -> img_XXXXX.png NB: icons used to generate the images are coming from https://www.flaticon.com/.


ontology/ :







pizzaiolo.xml : the Pizzaïolo Ontology (OWL) used to generate the samples.NB: this ontology was derived from [1], and built/manipulated with [2].



labels/ :



Concepts Encoding : concepts.json

Bounding Boxes : 1 file / sample -> img_XXXXX_bboxes.json

Contours : 1 file / sample -> img_XXXXX_contours.json

Semantic Segmentation : 1 file / sample -> img_XXXXX_segmentation.txt



sample_annotations/ :







Images showing examples of samples with provided annnotations



Concepts


The concepts are the elements constituting pizzas according to the Pizzaïolo Ontology (i.e. the pizza base, the pizza toppings, and the - optional - country of origin).

 

The concepts.json file contains a Python dict for concepts encoding :



key(s) : a concept id (uint)

value(s) : the concept name (string) (i.e. the short name of the concept class in the Pizzaïolo Ontology)


Bouding Boxes
The bounding boxes represent the localization of the concepts instances constituting a pizza sample.

Each img_XXXXX_bboxes.json file contains a Python dict:



key(s) : the name (string) of each concept class present in the sample

value(s) : list of all the bouding boxes for the corresponding concept key


 

NB: each bounding box is encoded as a Python (sub)list : [ x_left, y_top, width, height ]


Contours
The contours represent the localization and shape of the concepts instances constituting a pizza sample.

Each img_XXXXX_contours.json file contains a Python dict :



key(s) : the name (string) of each concept class present in the sample

value(s) : a list of all the contours for the corresponding concept key



NB: contours are encoded as OpenCV Contours.


Semantic Segmentation
Each img_XXXXX_segmentation.txt file contains a Python Numpy array (dtype=uint)



the shape of the array is the (2D) size of the samples (224*224)

each "pixel" belongs to a concept encoded according to concepts.json.


References




[1] Horridge, M. (2011). Protégé OWL Tutorial | OWL research at the University of Manchester.


[2] Lamy, J.-B. (2017). Owlready : Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. Artificial Intelligence in Medicine 80, 11–28.
DOI: 10.6084/m9.figshare.25729587
title: Open Data Package for Article "Exploring Complexity Issues in Junior Developer Code using Static Analysis and FCA"

description: The present dataset include the SonarQube issues uncovered as part of our exploratory research targeting code complexity issues in junior developer code written in the Python or Java programming languages. The dataset also includes the actual rule configurations and thresholds used for the Python and Java languages during source code analysis.
DOI: 10.5281/zenodo.8098992
title: Geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna, Italy.

description: This dataset contains all raw geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna. The dataset is divided in three separated excel worksheets: 

- Focus Area. Sediment composition of the 21 sediment samples collected in 2022 in the Focus Area. Refer to Figs. 1 and 2 in the manuscript Giambastiani et al., 2024 for the sample locations. Listed are also other information related to sampling, such as depositional facies (BR: beach ridge deposits; IF: Interfluvial floodplain deposits), distance from the sea, altimetry, amount of fertilizer applied based on the land use, and EC of drainage water. The sediment samples were collected in March 2022 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by the authors.

- LRC, Land Reclamation Consortium. PTEs composition of the sediment samples of the Land Reclamation Consortium dataset. Refer to Fig. 1 and 2 in the manuscript Giambastiani et al., 2024 for the location. Listed are also other information related to sampling, such as distance from the sea, altimetry, and amount of fertilizer applied based on the land use. The sediment samples were collected since 2010 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by The Land Reclamation Consortium of Romagna (Italy). No other uses apart from scientific purpose is allowed without notice to the authors.

- Wells. Physical and chemical groundwater parameters of 4 wells localted within the Focus Area. Refer to Fig.2  in the manuscript Giambastiani et al., 2024 for the location. Data were collected during previous studies by Greggio et al. (2020) and reprocessed to obtain vertical profiles of EC, pH, Eh, and chemical concentrations along the coastal aquifer depth.

More informations regarding the source, ownership, collection methodologies and analytical techniques are in Giambastiani et al., 2024.
description: The authors would like to thank the Land Reclamation Consortium (Consorzio di Bonifica della Romagna, Ravenna, Italy) for making available the drainage data and part of the sediment analysis.
DOI: 10.6084/m9.figshare.26212910
title: Supplementary Material for: A Case of Hepatocellular Carcinoma after Treatment for Chronic Myeloid Leukemia in a Patient Without Liver Disease

description: Introduction: Chronic myeloid leukemia (CML) is a myeloproliferative disease caused by the reciprocal translocation of chromosomes 9 and 22, which leads to a chimeric gene product known as BCR-ABL. Some studies have shown a higher incidence of secondary malignancies than the one seen in the general population in patients with CML. Hepatocellular carcinoma (HCC) is rarely reported in association with CML and/or CML related treatment. 

Case Presentation: We describe a case of a patient with no history of liver disease and CML on imatinib for ten years, who presented with worsening right upper quadrant abdominal pain. Imaging revealed a large hepatic mass highly suspicious of malignancy that later was confirmed to be HCC after biopsy. The patient was bracketed with advanced stage HCC (BCLC stage C) and given her advanced age and poor performance status; palliative care was offered.

Conclusion: Patients with CML have a common association with secondary malignancies. This is the first case report based on our extensive review of available literature that HCC was diagnosed in a patient with CML on treatment with imatinib without any clear or usual underlying cause.
DOI: 10.6084/m9.figshare.26212910.v1
title: Supplementary Material for: A Case of Hepatocellular Carcinoma after Treatment for Chronic Myeloid Leukemia in a Patient Without Liver Disease

description: Introduction: Chronic myeloid leukemia (CML) is a myeloproliferative disease caused by the reciprocal translocation of chromosomes 9 and 22, which leads to a chimeric gene product known as BCR-ABL. Some studies have shown a higher incidence of secondary malignancies than the one seen in the general population in patients with CML. Hepatocellular carcinoma (HCC) is rarely reported in association with CML and/or CML related treatment. 

Case Presentation: We describe a case of a patient with no history of liver disease and CML on imatinib for ten years, who presented with worsening right upper quadrant abdominal pain. Imaging revealed a large hepatic mass highly suspicious of malignancy that later was confirmed to be HCC after biopsy. The patient was bracketed with advanced stage HCC (BCLC stage C) and given her advanced age and poor performance status; palliative care was offered.

Conclusion: Patients with CML have a common association with secondary malignancies. This is the first case report based on our extensive review of available literature that HCC was diagnosed in a patient with CML on treatment with imatinib without any clear or usual underlying cause.
DOI: 10.5281/zenodo.10457137
title: On the cluster scales of hydrometeors in mixed-phase stratiform clouds [dataset]

description: Dataset used in "On the cluster scales of hydrometeors in mixed-phase stratiform clouds".
DOI: 10.4119/unibi/2906076
title: Data for the paper: Bubbles, Crashes and the Financial Cycle
DOI: 10.5066/f7tt4pvb
title: Hawaiian Islands Coastal Vegetation Survey 2013-2015

description: This dataset provides information on the current status and various other habitat and descriptive attributes of the native coastal vegetation for seven of the main Hawaiian Islands (i.e., does not include Niihau). This study was focused on identifying sites that still retain relatively intact and highly diverse native coastal plant communities throughout seven of the main Hawaiian Islands (Kauai, Oahu, Molokai, Lanai, Kahoolawe, Maui, and Hawaii) that may be further impacted by projected sea level rise. The island of Niihau was not included in the survey since there is very limited information for this island on its coastal plant community composition and structure, and very-high-resolution imagery is not available to use for assessing the status of its coastal vegetation. Within the identified threatened coastal sites we also attempted to determine if important cultural sites (e.g., Hawaiian house or wall structures, burial sites, etc.) found at there might also be threatened by the projected sea level rise. The results of this study are intended to provide a spatial foundation for identifying priority sites containing native coastal vegetation which can be used for the development of management strategies to help maintain the viability of native coastal plant communities and other important cultural resources found at these sites.