cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2023-09.2893984/2457542
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2893988/2457543
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2893990/2457544
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2893992/2457545
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2893993/2457546
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2893994/2457547
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2893995/2457548
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2893997/2457549
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894001/2457550
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894002/2457551
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894003/2457552
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894004/2457553
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25912/rdf_1728354637292
title: Dataset from: Framework for Autonomous UAV Navigation and Target Detection in Global-Navigation-Satellite-System-Denied and Visually Degraded Environments
DOI: 10.25582/data.2023-09.2894005/2457554
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894006/2457555
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894008/2457556
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894012/2457557
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894015/2457558
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894016/2457559
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894019/2457560
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894020/2457561
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894021/2457562
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894023/2457563
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894029/2457564
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894030/2457565
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894031/2457566
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894034/2457567
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894035/2457568
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894039/2457569
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894044/2457570
title: Data for EMSL Project 51287 from September 2023
DOI: 10.15468/dl.cfty6k
title: Occurrence Download

description: A dataset containing 385 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Thailand",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Elephas maximus Linnaeus, 1758",
 "Year 2000-2024"
 ]
}

 The dataset includes 385 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001899-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-09.2894045/2457571
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894046/2457572
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894048/2457573
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894049/2457574
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894050/2457575
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894051/2457576
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894053/2457577
title: Data for EMSL Project 51287 from September 2023
DOI: 10.15468/dl.b4g63m
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material citation, Material sample)",
 "DatasetKey is one of (Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (vertebrados), KUBI Herpetology Collection)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "PublishingOrg is one of (ff90b050-c256-11db-b71b-b8a03c50a862, b554c320-0560-11d8-b851-b8a03c50a862)",
 "TaxonKey is Thorius insperatus Hanken & Wake, 1994",
 "Year 1950-2024"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001900-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-09.2894059/2457578
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894103/2457579
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894112/2457580
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894118/2457581
title: Data for EMSL Project 51287 from September 2023
DOI: 10.15468/dl.szbcwt
title: Occurrence Download

description: A dataset containing 17281076 species occurrences available in GBIF matching the query:
{
 "BasisOfRecord" : [
 "is Observation"
 ]
}

 The dataset includes 17281076 records from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001870-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-09.2894168/2457582
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894184/2457583
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894218/2457584
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894234/2457585
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894247/2457586
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894256/2457587
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894262/2457588
title: Data for EMSL Project 51287 from September 2023
DOI: 10.15468/dl.4k6nmy
title: Occurrence Download

description: A dataset containing 120141 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen)",
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus Wiegmann, 1828"
 ]
}

 The dataset includes 120141 records from 142 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001874-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13883485
title: Ocular Toxoplasmosis Fundus Images Dataset

description: The Ocular Toxoplasmosis (OT) Fundus Images Dataset contains a set of eye images collected at the Hospital de Clínicas and Hospital General Pedriático Acosta Ñu medical centers from Asunción, Paraguay.


The dataset is used in the generation of models for automatic detection of ocular toxoplasmosis. Used as a tool for OT diagnosis, a predictive model could save time, help diagnose atypical cases and also assist ophthalmologists, being particularly useful for those with less experience.


Dataset structure:


+-- Ocular_Toxoplasmosis_Data
|   +-- masks
|   +-- images
|   +-- dataset_labels.csv


The dataset contains two major folders, one with all the collected images and another one with the masks for lesions of eyes images with ocular toxoplasmosis. The dataset also includes a csv file with the labels for each image: healthy, active and inactive lesions, this two being non healthy.


Notes about the masks: the dataset includes masks for all non healthy images (active and inactive lesions). In order to differentiate the active lesions which are less common that the inactive ones, the mask includes the suffix -a for all the masks of active lesions.
DOI: 10.5281/zenodo.4441200
title: Ocular Toxoplasmosis Fundus Images Dataset

description: The <strong>Ocular Toxoplasmosis (OT) Fundus Images Dataset</strong> contains a set of eye images collected at the <em>Hospital de Clínicas</em> medical center from Asunción, Paraguay. The dataset is used in the generation of models for automatic detection of ocular toxoplasmosis. Used as a tool for OT diagnosis, a predictive model could save time, help diagnose atypical cases and also assist ophthalmologists, being particularly useful for those with less experience. Dataset structure: <pre><code>+-- Ocular_Toxoplasmosis_Data | +-- masks | +-- images | +-- classification | +-- healthy | +-- non_healthy | +-- dataset_labels.csv</code></pre> The dataset contains two major folders, one with all the collected images and another one with the masks for lesions of eyes images with ocular toxoplasmosis. The classification folder contains the <strong>healthy</strong> images and <strong>non healthy</strong> images. The non healthy set contains images with both active and inactive lesions caused by ocular toxoplasmosis. Finally, it also includes a <strong>csv</strong> file with the labels for each image.
DOI: 10.5281/zenodo.4479724
title: Ocular Toxoplasmosis Fundus Images Dataset

description: The <strong>Ocular Toxoplasmosis (OT) Fundus Images Dataset</strong> contains a set of eye images collected at the <em>Hospital de Clínicas</em> medical center from Asunción, Paraguay. The dataset is used in the generation of models for automatic detection of ocular toxoplasmosis. Used as a tool for OT diagnosis, a predictive model could save time, help diagnose atypical cases and also assist ophthalmologists, being particularly useful for those with less experience. Dataset structure: <pre><code>+-- Ocular_Toxoplasmosis_Data | +-- masks | +-- images | +-- classification | +-- healthy | +-- non_healthy | +-- dataset_labels.csv</code></pre> The dataset contains two major folders, one with all the collected images and another one with the masks for lesions of eyes images with ocular toxoplasmosis. The classification folder contains the <strong>healthy</strong> images and <strong>non healthy</strong> images. The non healthy set contains images with both active and inactive lesions caused by ocular toxoplasmosis. Finally, it also includes a <strong>csv</strong> file with the labels for each image.
DOI: 10.5281/zenodo.5156940
title: Ocular Toxoplasmosis Fundus Images Dataset

description: The <strong>Ocular Toxoplasmosis (OT) Fundus Images Dataset</strong> contains a set of eye images collected at the <em>Hospital de Clínicas</em> and <em>Hospital General Pedriático Acosta Ñu </em>medical centers from Asunción, Paraguay. The dataset is used in the generation of models for automatic detection of ocular toxoplasmosis. Used as a tool for OT diagnosis, a predictive model could save time, help diagnose atypical cases and also assist ophthalmologists, being particularly useful for those with less experience. Dataset structure: <pre><code>+-- Ocular_Toxoplasmosis_Data | +-- masks | +-- images | +-- dataset_labels.csv</code></pre> The dataset contains two major folders, one with all the collected images and another one with the masks for lesions of eyes images with ocular toxoplasmosis. The dataset also includes a <strong>csv</strong> file with the labels for each image: healthy, active and inactive lesions, this two being non healthy. Notes about the masks: the dataset includes masks for all non healthy images (active and inactive lesions). In order to differentiate the active lesions which are less common that the inactive ones, the mask includes the suffix <strong>-a </strong>for all the masks of active lesions.
DOI: 10.5281/zenodo.4439566
title: Ocular Toxoplasmosis Fundus Images Dataset

description: The Ocular Toxoplasmosis (OT) Fundus Images Dataset contains a set of eye images collected at the Hospital de Clínicas and Hospital General Pedriático Acosta Ñu medical centers from Asunción, Paraguay.


The dataset is used in the generation of models for automatic detection of ocular toxoplasmosis. Used as a tool for OT diagnosis, a predictive model could save time, help diagnose atypical cases and also assist ophthalmologists, being particularly useful for those with less experience.


Dataset structure:


+-- Ocular_Toxoplasmosis_Data
|   +-- masks
|   +-- images
|   +-- dataset_labels.csv


The dataset contains two major folders, one with all the collected images and another one with the masks for lesions of eyes images with ocular toxoplasmosis. The dataset also includes a csv file with the labels for each image: healthy, active and inactive lesions, this two being non healthy.


Notes about the masks: the dataset includes masks for all non healthy images (active and inactive lesions). In order to differentiate the active lesions which are less common that the inactive ones, the mask includes the suffix -a for all the masks of active lesions.
DOI: 10.5281/zenodo.5156953
title: Ocular Toxoplasmosis Fundus Images Dataset

description: The <strong>Ocular Toxoplasmosis (OT) Fundus Images Dataset</strong> contains a set of eye images collected at the <em>Hospital de Clínicas</em> and <em>Hospital General Pedriático Acosta Ñu </em>medical centers from Asunción, Paraguay. The dataset is used in the generation of models for automatic detection of ocular toxoplasmosis. Used as a tool for OT diagnosis, a predictive model could save time, help diagnose atypical cases and also assist ophthalmologists, being particularly useful for those with less experience. Dataset structure: <pre><code>+-- Ocular_Toxoplasmosis_Data | +-- masks | +-- images | +-- dataset_labels.csv</code></pre> The dataset contains two major folders, one with all the collected images and another one with the masks for lesions of eyes images with ocular toxoplasmosis. The dataset also includes a <strong>csv</strong> file with the labels for each image: healthy, active and inactive lesions, this two being non healthy. Notes about the masks: the dataset includes masks for all non healthy images (active and inactive lesions). In order to differentiate the active lesions which are less common that the inactive ones, the mask includes the suffix <strong>-a </strong>for all the masks of active lesions.
DOI: 10.25582/data.2023-09.2894265/2457589
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894277/2457590
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894278/2457591
title: Data for EMSL Project 51287 from September 2023
DOI: 10.60781/u01/a020/m026
title: BERT
DOI: 10.25582/data.2023-09.2894279/2457592
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894281/2457593
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894282/2457594
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894283/2457595
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894284/2457596
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894285/2457597
title: Data for EMSL Project 51287 from September 2023
DOI: 10.15468/dl.dnjm4b
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scinax albicans (Bokermann, 1967)"
 ]
}

 The dataset includes 21 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001902-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-09.2894286/2457598
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894290/2457599
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894291/2457600
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894295/2457601
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894296/2457602
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894297/2457603
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894298/2457604
title: Data for EMSL Project 51287 from September 2023
DOI: 10.15468/dl.3u6w2b
title: Occurrence Download

description: A dataset listing the 41164 species recorded in GBIF matching the query:
{
 "BasisOfRecord" : [
 "is Observation"
 ]
}

 The dataset's 41164 records were derived from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001887-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-09.2894300/2457605
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894302/2457606
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894304/2457607
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894308/2457608
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894309/2457609
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894310/2457610
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894313/2457611
title: Data for EMSL Project 51287 from September 2023
DOI: 10.5281/zenodo.13901444
title: ckpt of FoldToken4
DOI: 10.5281/zenodo.13901445
title: ckpt of FoldToken4
DOI: 10.15468/dl.e2taf9
title: Occurrence Download

description: A dataset containing 821 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scinax alter (Lutz, 1973)"
 ]
}

 The dataset includes 821 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001903-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-09.2894315/2457612
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894320/2457613
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894325/2457614
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894326/2457615
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894327/2457616
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894328/2457617
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894329/2457618
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894336/2457619
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894337/2457620
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894338/2457621
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894339/2457622
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894343/2457623
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894345/2457624
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894347/2457625
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894350/2457626
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894351/2457627
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894352/2457628
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894353/2457629
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894355/2457630
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894358/2457631
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894364/2457632
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894368/2457633
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894369/2457634
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894371/2457635
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894372/2457636
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894373/2457637
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894374/2457638
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894375/2457639
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894376/2457640
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894381/2457641
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894383/2457642
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894384/2457643
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894385/2457644
title: Data for EMSL Project 51287 from September 2023
DOI: 10.15468/dl.r4hstj
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scinax angrensis (Lutz, 1973)"
 ]
}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001904-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-09.2894387/2457645
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894389/2457646
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894390/2457647
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894391/2457648
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894392/2457649
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894393/2457650
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894396/2457651
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894397/2457652
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894399/2457653
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894402/2457654
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894403/2457655
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894404/2457656
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894405/2457657
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894407/2457658
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894409/2457659
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894411/2457660
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894412/2457661
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894413/2457662
title: Data for EMSL Project 51287 from September 2023
DOI: 10.25582/data.2023-09.2894419/2457663
title: Data for EMSL Project 51287 from September 2023