cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.rqq45y
title: Occurrence Download

description: A dataset containing 5237 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((6.475 52.925,6.525 52.925,6.525 52.975,6.475 52.975,6.475 52.925))",
 "DatasetKey is Observation.org, Nature data from around the World",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 5237 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0307613-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2wvrqt
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aptosimum molle Skan"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012541-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kttn01
title: Occurrence Download

description: A dataset containing 6508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is VectorBase (Bioinformatics Resource for Invertebrate Vectors of Human Pathogens)",
 "TaxonKey is Anopheles gambiae Giles, 1900"
 ]
}

 The dataset includes 6508 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010097-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.33bdf8
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ardisia suboppositifolia C.M.Hu & J.E.Vidal"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012545-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p2qh2z
title: Occurrence Download

description: A dataset containing 6837249 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is China"
 ]
}

 The dataset includes 6837249 records from 4191 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0365808-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10946415
title: Sample data for analysis of demographic potential of the 15-minute city in northern and southern France

description: v1 deprecated

v2 : https://zenodo.org/records/13456826
DOI: 10.5281/zenodo.10946414
title: Sample data for analysis of demographic potential of the 15-minute city in northern and southern France

description: This upload contains two Geopackage files of raw data used for urban analysis in the outskirts of Lille and Nice, France. The data include building footprints (layer "building"), roads (layer "road"), and administrative boundaries (layer "adm_boundaries")extracted from version 3.3 of the French dataset BD TOPO®3 (IGN, 2023) for the municipalities of Santes, Hallennes-lez-Haubourdin,Haubourdin, and Emmerin in northern France (Geopackage "DPC_59.gpkg") and Drap, Cantaron and La Trinité in southern France (Geopackage "DPC_06.gpkg").

 

Metadata for these layers is available here: https://geoservices.ign.fr/sites/default/files/2023-01/DC_BDTOPO_3-3.pdf

 

Additionally, this upload contains the results of the following algorithms available in GitHub (https://github.com/perezjoan/emc2-WP2?tab=readme-ov-file)

  

1.  The identification of main streets using the QGIS plugin Morpheo (layers "road_morpheo" and "buffer_morpheo") https://plugins.qgis.org/plugins/morpheo/ 

2.  The identification of main streets in local contexts – connectivity locally weighted (layer "road_LocRelCon")

3.  Basic morphometry of buildings (layer "building_morpho")

4.  Evaluation of the number of dwellings within inhabited buildings (layer "building_dwellings")

5.  Projecting population potential accessible from main streets (layer "road_pop_results")

 

Project website: http://emc2-dut.org/

 

Publications using this sample data: Perez, J. and Fusco, G., 2024. Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance. In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M.A.C. Rocha and M.N. Faginas Lago, eds. Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14817. Cham: Springer, pp.50-60. https://doi.org/10.1007/978-3-031-65238-7_4.

Acknowledgement. This work is part of the emc2 project, which received the grant ANR-23-DUTP-0003-01 from the French National Research Agency (ANR) within the DUT Partnership.
DOI: 10.5281/zenodo.12804748
title: Reciprocal best hits BLAST files for "EXCRETE workflow enables deep proteomics of the microbial extracellular environment"

description: .fasta file input and tabular output from a BLAST reciprocal best hits analysis on https://usegalaxy.eu/
DOI: 10.5281/zenodo.13382797
title: Public Data files for DeepCDM

description: Public data files for experiments in DeepCDM.

 

merged_MoNA_ESI-MSMS_spectra.sdf is used for model pre-training, replicate_from_mona.sdf is used for validation in pre-training.

 

fine_tuning_example.sdf is a demo training set used for model fine-tuning, while replicate_from_dansylation_example.sdf is a demo for validation. Larger training set is recommended for better performance. Moreover, the training set for fine-tuning can be changed for variable CDMs.

 

example_molecule.sdf is a example of molecular structural information for spectrum prediction.
DOI: 10.12755/nrct.res.2022.2
title: แผ่นแปะเรดิชนาโนไฟเบอร์ลดเลือนฝ้า
DOI: 10.15468/dl.n57vz7
title: Occurrence Download

description: A dataset containing 6911 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Arctostaphylos glauca Lindl.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6911 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107424-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pvz777
title: Occurrence Download

description: A dataset containing 546 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Areca catechu L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 546 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107425-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.19130465
title: COins database

description: COins is a database of COI-5P sequences of insects that includes over 532,000 representative sequences of more than 106,000 species specifically formatted for the QIIME2 software platform. It was developed through a combination of automated and manually curated steps, starting from insects COI sequences available in the Barcode of Life Data System selecting sequences that comply to several standards, including a species-level identification.<br><br><br><br><b>seq-degapped.qza</b> --&gt; reference sequences<br><b>taxonomy.qza</b> --&gt; sequences taxonomy<br><b>SklearnClassifier_COins_QIIME2_v2024.5.qza (NEW!)</b> --&gt; naïve Bayes taxonomic classifier trained on CO<i>ins</i> (QIIME2 version 2024.5)<br><b>SklearnClassifier_COins_QIIME2_v2023.5.qza</b> --&gt; naïve Bayes taxonomic classifier trained on CO<i>ins</i> (QIIME2 version 2023.5)<br><b>SklearnClassifier_COins_QIIME2_v2022.2.qza</b> --&gt; naïve Bayes taxonomic classifier trained on CO<i>ins</i> (QIIME2 version 2022.2)<br><b>Sequences_metadata1.tsv</b> --&gt; Identification procedure of voucher specimens from which reference sequences were developed.Identification procedure is reported for each sequence included in CO<i>ins</i> (BOLD id reported in <i>BOLDid reference</i> column) and for all identical sequences within haplotypes that were removed at Step 5 of CO<i>ins</i> curation (those for which BOLD id is not available in <i>BOLDid reference </i>column). The haplotype to which each sequence belongs is reported in <i>Haplotype</i> column (haplotypes of each species are labeled with increasing numbers). Identification procedure information derived from sequences associated metadata provided by BOLD system.<br><b>Sequences_metadata2.tsv </b>--&gt;Identical sequences belonging to different species present within CO<i>ins</i>.Each row represents a cluster of identical sequences associated to different species, sequences included in the cluster are labeled with species name and BOLD id.<br>
DOI: 10.15468/dl.jaurv9
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Argania spinosa (L.) Skeels)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 105 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107426-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pnhenu
title: Occurrence Download

description: A dataset containing 1679 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is DEMNA-DNE : Exotic animal occurrences in Wallonia, Belgium"
 ]
}

 The dataset includes 1679 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107428-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2hhrlc
title: CCDC 2309053: Experimental Crystal Structure Determination

description: Related Article: Abinash Pradhan, Rajanikanta Rana, Gopalan Rajaraman, Monalisa Pradhan, Saroj L. Samal|2024|Dalton Trans.|53|13628|doi:10.1039/D4DT01692K
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.cc2hhs1v
title: CCDC 2309067: Experimental Crystal Structure Determination

description: Related Article: Abinash Pradhan, Rajanikanta Rana, Gopalan Rajaraman, Monalisa Pradhan, Saroj L. Samal|2024|Dalton Trans.|53|13628|doi:10.1039/D4DT01692K
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.cc2hhrpg
title: CCDC 2309056: Experimental Crystal Structure Determination

description: Related Article: Abinash Pradhan, Rajanikanta Rana, Gopalan Rajaraman, Monalisa Pradhan, Saroj L. Samal|2024|Dalton Trans.|53|13628|doi:10.1039/D4DT01692K
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.cc2jccdp
title: CCDC 2334622: Experimental Crystal Structure Determination

description: Related Article: Abinash Pradhan, Rajanikanta Rana, Gopalan Rajaraman, Monalisa Pradhan, Saroj L. Samal|2024|Dalton Trans.|53|13628|doi:10.1039/D4DT01692K
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.10963338
title: Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

description: Here are the datasets for our publication entitled "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis" published in Nature Communications. 

The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi0.5Co0.2Mn0.3O2. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature.  A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.

Other details can be found in "Data Introduction.pdf" file.

The Python Code for reading and preprocessing this dataset is available at: https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library

Summary of articles using the this dataset: https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary

 

If you find this data helpful, please consider citing our paper:

Wang, F., Zhai, Z., Zhao, Z. et al. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat Commun 15, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z
DOI: 10.5281/zenodo.10963339
title: Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

description: Here are the datasets for our publication entitled "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis" published in Nature Communications. 

The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi0.5Co0.2Mn0.3O2. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature.  A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.

Other details can be found in "Data Introduction.pdf" file.

The Python Code for reading and preprocessing this dataset is available at: https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library

Summary of articles using the this dataset: https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary

 

If you find this data helpful, please consider citing our paper:

Wang, F., Zhai, Z., Zhao, Z. et al. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat Commun 15, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z
DOI: 10.5281/zenodo.13382926
title: Robust-Minisets

description: Abstract

We introduce Robust-Minisets, a collection of robust benchmark classification datasets in the low resolution realm based on well-established image classification benchmarks, such as CIFAR, Tiny ImageNet, EuroSAT and the MedMNIST collection. We port existing robustness and generalization benchmarks (ImageNet-C, -R, -A and v2) to the small dataset domain introducing novel benchmarks to comprehensively evaluate the robustness and generalization capabilities of image classification models on low resolution datsets. This results in an extensive collection consisting of already existing test sets (e.g. CIFAR-10.1 and Tiny ImageNet-C) as well as the novel benchmarks EuroSAT-C, MedMNIST-C, and Tiny ImageNet-A, -R and -v2 introduced in our ICPR 2024 paper "GenFormer - Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets".

 

Installation and Requirements



We recommend our official code to download, parse and use the Robust-Minisets datasets:



% pip install robust-minisets% python



To use a standard version utilizing the downloaded files:


>>> from robust_minisets import TinyImageNetR

>>> test_dataset = TinyImageNetR(split="test")


To enable automatic downloading by setting `download=True`:


>>> from robust_minisets import EuroSATC

>>> test_dataset = EuroSATC(split="test", download=True)


Additionally, we include training and validation datasets that are not provided in common hubs (e.g. torchvision):


>>> from robust_minisets import EuroSAT

>>> train_dataset = EuroSAT(split="train", download=True)

>>> val_dataset = EuroSAT(split="val", download=True)

>>> test_dataset = EuroSAT(split="test", download=True)




 

License

The code is under Apache-2.0 License.

 

The publication licenses of the datasets can be found within the info dictionary of our official code via robust_minisets.INFO[<dataset_flag>] or here.

 

Citation

If you find this work useful, please consider citing us:



Sven Oehri, Nikolas Ebert, Ahmed Abdullah, Didier Stricker, Oliver Wasenmüller. "GenFormer - Generated Images are All You Need to Improve Robustness on Small Datasets". International Conference on Pattern Recognition (ICPR), 2024.


or using bibtex:

@inproceedings{oehri2024genformer,    title = {GenFormer – Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets},    author = {Oehri, Sven and Ebert, Nikolas and Abdullah, Ahmed and Stricker, Didier and Wasenm{\"u}ller, Oliver},    booktitle = {International Conference on Pattern Recognition (ICPR)},    year = {2024},}

 

DISCLAIMER: Robust-Minisets is based on a wide range of existing datasets and benchmarks. Thus, please also cite source data paper(s) of the Robust-Minisets subset(s):





CIFAR-10.1

EuroSAT

ImageNet-A

ImageNet-C

ImageNet-R

ImageNetv2

MedMNIST, the respective source datasets (described here)



 

Release versions





v1.0.0: Robust-Minisets v1 release
DOI: 10.5281/zenodo.13382927
title: Robust-Minisets

description: Abstract

We introduce Robust-Minisets, a collection of robust benchmark classification datasets in the low resolution realm based on well-established image classification benchmarks, such as CIFAR, Tiny ImageNet, EuroSAT and the MedMNIST collection. We port existing robustness and generalization benchmarks (ImageNet-C, -R, -A and v2) to the small dataset domain introducing novel benchmarks to comprehensively evaluate the robustness and generalization capabilities of image classification models on low resolution datsets. This results in an extensive collection consisting of already existing test sets (e.g. CIFAR-10.1 and Tiny ImageNet-C) as well as the novel benchmarks EuroSAT-C, MedMNIST-C, and Tiny ImageNet-A, -R and -v2 introduced in our ICPR 2024 paper "GenFormer - Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets".

 

Installation and Requirements



We recommend our official code to download, parse and use the Robust-Minisets datasets:



% pip install robust-minisets% python



To use a standard version utilizing the downloaded files:


>>> from robust_minisets import TinyImageNetR

>>> test_dataset = TinyImageNetR(split="test")


To enable automatic downloading by setting `download=True`:


>>> from robust_minisets import EuroSATC

>>> test_dataset = EuroSATC(split="test", download=True)


Additionally, we include training and validation datasets that are not provided in common hubs (e.g. torchvision):


>>> from robust_minisets import EuroSAT

>>> train_dataset = EuroSAT(split="train", download=True)

>>> val_dataset = EuroSAT(split="val", download=True)

>>> test_dataset = EuroSAT(split="test", download=True)




 

License

The code is under Apache-2.0 License.

 

The publication licenses of the datasets can be found within the info dictionary of our official code via robust_minisets.INFO[<dataset_flag>] or here.

 

Citation

If you find this work useful, please consider citing us:



Sven Oehri, Nikolas Ebert, Ahmed Abdullah, Didier Stricker, Oliver Wasenmüller. "GenFormer - Generated Images are All You Need to Improve Robustness on Small Datasets". International Conference on Pattern Recognition (ICPR), 2024.


or using bibtex:

@inproceedings{oehri2024genformer,    title = {GenFormer – Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets},    author = {Oehri, Sven and Ebert, Nikolas and Abdullah, Ahmed and Stricker, Didier and Wasenm{\"u}ller, Oliver},    booktitle = {International Conference on Pattern Recognition (ICPR)},    year = {2024},}

 

DISCLAIMER: Robust-Minisets is based on a wide range of existing datasets and benchmarks. Thus, please also cite source data paper(s) of the Robust-Minisets subset(s):





CIFAR-10.1

EuroSAT

ImageNet-A

ImageNet-C

ImageNet-R

ImageNetv2

MedMNIST, the respective source datasets (described here)



 

Release versions





v1.0.0: Robust-Minisets v1 release
DOI: 10.15468/dl.3pzdfh
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "OccurrenceStatus is Present",
 "TaxonKey is Aedes mcintoshi Huang, 1985",
 "Year 2000-2024"
 ]
}

 The dataset includes 20 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107430-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x7zbwb
title: Occurrence Download

description: A dataset containing 566096 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Argentina anserina (L.) Rydb.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 566096 records from 1325 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107427-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/z3gb3k
title: Checklist of vascular plants indicated for Tyumen Oblast in Flora Sibiri (1987-2003): accepted names

description: A list of the tracheophyte names accepted in "Flora Sibiri" (1987-2003) and indicated for Tyumen Oblast. Yamalo-Nenets Autonomous Okrug and Khanty-Mansi Autonomous Okrug are not covered by this source. The dataset was compiled as a first step towards further processing of the modern checklist for Tyumen Oblast, Russia.
Stable archival version (not edited, not curated).
The dataset includes 1,070 accepted species and subspecies names. Fourteen volumes of "Flora Sibiri" (1987-2003) are regarded as a single data source, therefore corrections presented in vol. 14 are used here throughout.
Several minor orthographic errors in either scientific names or their authors were corrected manually. Some names were not automatically cross-linked with the GBIF backbone.