cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18602967
title: Joint Beamforming and RIS Optimization

description: Measurements obtained from RIS-assisted CF-MIMO experiments at the TUBS testbed. The setup integrates six RIS panels (16×16 elements, 5.8 GHz) controlled via Raspberry Pis and optimized jointly with distributed beamforming strategies. The datainclude SINR, CQI, and throughput metrics, enabling performance evaluation of RIS-assisted and cascaded RIS configurations. The experiments aim to quantify coverage uniformity, signal quality gains, and the feasibility of chained RIS setups.
DOI: 10.5281/zenodo.18602953
title: Time Synchronization

description: Dataset collected from time synchronization experiments on the CF-MIMO testbed. The TUBS setup provides hardware-based synchronization via OctoClock and GNSSdisciplined clock generators, while UC algorithms enable distributed synchronization control using a hierarchical Master and sub-Master AP structure. The dataset includes synchronization logs, phase and frequency offset data, and PHY/MAC-layer metrics (e.g., SINR, CQI, latency, throughput) for evaluating synchronization performance and reciprocity calibration accuracy.
DOI: 10.5281/zenodo.18602955
title: Time Synchronization

description: Dataset collected from time synchronization experiments on the CF-MIMO testbed. The TUBS setup provides hardware-based synchronization via OctoClock and GNSSdisciplined clock generators, while UC algorithms enable distributed synchronization control using a hierarchical Master and sub-Master AP structure. The dataset includes synchronization logs, phase and frequency offset data, and PHY/MAC-layer metrics (e.g., SINR, CQI, latency, throughput) for evaluating synchronization performance and reciprocity calibration accuracy.
DOI: 10.5281/zenodo.18602935
title: Evaluation of Distributed Beamforming Strategies

description:  Measurements collected from distributed beamforming experiments on the CFMIMO testbed using OAI. The data includes PHY/MAC-layer parameters such as SINR, CQI, RSRP, MCS, HARQ statistics, code rate, and data rate, extracted from both gNB and UE traces. Independent, joint, and coordinated beamforming modes are compared to assess performance differences in throughput, reliability, and spectral efficiency.
DOI: 10.5281/zenodo.18602934
title: Evaluation of Distributed Beamforming Strategies

description:  Measurements collected from distributed beamforming experiments on the CFMIMO testbed using OAI. The data includes PHY/MAC-layer parameters such as SINR, CQI, RSRP, MCS, HARQ statistics, code rate, and data rate, extracted from both gNB and UE traces. Independent, joint, and coordinated beamforming modes are compared to assess performance differences in throughput, reliability, and spectral efficiency.
DOI: 10.5281/zenodo.18602924
title: Sensing traffic

description: Sensing traffic that will coexist at the transport network with legacy communication traffic flows. Specifically, the dataset will embrace sensing traffic between the RU and DU. It can be further exploited to define traffic management solutions over the transport network.
DOI: 10.5281/zenodo.18602923
title: Sensing traffic

description: Sensing traffic that will coexist at the transport network with legacy communication traffic flows. Specifically, the dataset will embrace sensing traffic between the RU and DU. It can be further exploited to define traffic management solutions over the transport network.
DOI: 10.5281/zenodo.18602889
title: Trajectory prediction

description: Trajectory prediction of UEs based on sensing information over time. It will also include ground truth information for evaluation purposes.
DOI: 10.5281/zenodo.18602888
title: Trajectory prediction

description: Trajectory prediction of UEs based on sensing information over time. It will also include ground truth information for evaluation purposes.
DOI: 10.5281/zenodo.18602868
title: Resource scheduling

description: Utilization of radio resources over time along with synchronized with surrounding sensing information and network metrics. It can be exploited to define better scheduling schemes; the results yielded by the ones developed in the PoC will be also released for comparison purposes.
DOI: 10.5281/zenodo.18602867
title: Resource scheduling

description: Utilization of radio resources over time along with synchronized with surrounding sensing information and network metrics. It can be exploited to define better scheduling schemes; the results yielded by the ones developed in the PoC will be also released for comparison purposes.
DOI: 10.5281/zenodo.18603222
title: HISTOGENESIS OF THE MALE REPRODUCTIVE SYSTEM

description: Histogenesis of the male reproductive system is a tightly coordinated developmental sequence in which genetic sex determination is translated into organ-level differentiation of the testes, the ductal system, accessory glands, and external genitalia. The process begins with formation of the urogenital ridge and migration of primordial germ cells, proceeds through testis determination driven by a somatic cell program, and culminates in androgen- and peptide-mediated remodeling of embryonic ducts and the urogenital sinus.
DOI: 10.5281/zenodo.18603223
title: HISTOGENESIS OF THE MALE REPRODUCTIVE SYSTEM

description: Histogenesis of the male reproductive system is a tightly coordinated developmental sequence in which genetic sex determination is translated into organ-level differentiation of the testes, the ductal system, accessory glands, and external genitalia. The process begins with formation of the urogenital ridge and migration of primordial germ cells, proceeds through testis determination driven by a somatic cell program, and culminates in androgen- and peptide-mediated remodeling of embryonic ducts and the urogenital sinus.
DOI: 10.5281/zenodo.18602852
title: Location estimation based on SRS signals

description: Algorithmic and NN UE location estimation based in UL SRS signal. The signal will be extracted using O-RAN LLCv1.0 SM.
DOI: 10.5281/zenodo.18602842
title: Monostatic sensing data

description: CSI and heatmaps generated by sub-6 and mmWave sensing solutions upon different scenarios and sensing configurations. The dataset will include ground truth information so that it can be used to develop new processing schemes.
DOI: 10.5281/zenodo.18602843
title: Monostatic sensing data

description: CSI and heatmaps generated by sub-6 and mmWave sensing solutions upon different scenarios and sensing configurations. The dataset will include ground truth information so that it can be used to develop new processing schemes.
DOI: 10.5281/zenodo.18602851
title: Location estimation based on SRS signals

description: Algorithmic and NN UE location estimation based in UL SRS signal. The signal will be extracted using O-RAN LLCv1.0 SM.
DOI: 10.5281/zenodo.18602777
title: Gesture recognition

description: Wi-Fi CSI evolution over time to evaluate gesture recognition solutions. The dataset will also comprise ground truth information and the outcome of the developed solution to be used as benchmark.
DOI: 10.5281/zenodo.18602776
title: Gesture recognition

description: Wi-Fi CSI evolution over time to evaluate gesture recognition solutions. The dataset will also comprise ground truth information and the outcome of the developed solution to be used as benchmark.
DOI: 10.5281/zenodo.18404800
title: Dataset for: Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction

description: This dataset contains the input data used in the study:

“Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction”

The dataset includes monthly time series of drought areas (DA) and seasonal rice crop yield (CY) data for three regions in eastern India:-Region 1: Bihar and Jharkhand-Region 2: West Bengal-Region 3: Odisha

Drought areas are expressed as percentages (%) of the region affected by drought and were derived from the Standardised Precipitation Evapotranspiration Index (SPEI) at multiple temporal aggregation periods: 1, 3, 6, 9, and 12 months. These data represent the spatial extent of drought conditions at different time scales.

Seasonal crop yield data correspond to rice yield and are expressed in kg/ha.

The dataset is provided in three Excel files:-DAs_and_CY_Bihar_and_Jharkhand.xlsx-DAs_and_CY_West_Bengal.xlsx-DAs_and_CY_Odisha.xlsx

Each file contains two sheets:-DA: monthly drought area time series for different aggregation periods-CY: seasonal rice crop yield time series

These data were used as inputs to train and evaluate machine learning models (artificial neural networks and polynomial regression) for crop yield prediction.

How to cite

Please cite both this dataset and the associated article:

Vitali Diaz, Ahmed A. Osman, Gerald A. Corzo Perez, Shreedhar Maskey, Dimitri Solomatine; Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction. Hydrology Research 2026; nh2026150. doi: https://doi.org/10.2166/nh.2026.150

Diaz, V., Osman, A. A. A., Corzo Perez, G. A., Maskey, S., & Solomatine, D. (2026). Dataset for: Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction [Data set]. In Hydrology Research. Zenodo. https://doi.org/10.5281/zenodo.18404801

Notes:The dataset is intended for research and educational purposes.

Users are encouraged to cite the dataset when used in derivative works.
DOI: 10.5281/zenodo.18404801
title: Dataset for: Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction

description: This dataset contains the input data used in the study:

“Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction”

The dataset includes monthly time series of drought areas (DA) and seasonal rice crop yield (CY) data for three regions in eastern India:-Region 1: Bihar and Jharkhand-Region 2: West Bengal-Region 3: Odisha

Drought areas are expressed as percentages (%) of the region affected by drought and were derived from the Standardised Precipitation Evapotranspiration Index (SPEI) at multiple temporal aggregation periods: 1, 3, 6, 9, and 12 months. These data represent the spatial extent of drought conditions at different time scales.

Seasonal crop yield data correspond to rice yield and are expressed in kg/ha.

The dataset is provided in three Excel files:-DAs_and_CY_Bihar_and_Jharkhand.xlsx-DAs_and_CY_West_Bengal.xlsx-DAs_and_CY_Odisha.xlsx

Each file contains two sheets:-DA: monthly drought area time series for different aggregation periods-CY: seasonal rice crop yield time series

These data were used as inputs to train and evaluate machine learning models (artificial neural networks and polynomial regression) for crop yield prediction.

How to cite

Please cite both this dataset and the associated article:

Vitali Diaz, Ahmed A. Osman, Gerald A. Corzo Perez, Shreedhar Maskey, Dimitri Solomatine; Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction. Hydrology Research 2026; nh2026150. doi: https://doi.org/10.2166/nh.2026.150

Diaz, V., Osman, A. A. A., Corzo Perez, G. A., Maskey, S., & Solomatine, D. (2026). Dataset for: Spatiotemporal changes of drought area as input for a machine-learning approach for crop yield prediction [Data set]. In Hydrology Research. Zenodo. https://doi.org/10.5281/zenodo.18404801

Notes:The dataset is intended for research and educational purposes.

Users are encouraged to cite the dataset when used in derivative works.
DOI: 10.15468/dl.mw6pte
title: Occurrence Download

description: A dataset containing 2619 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)) isea3hcellcode,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)), "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)), "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)), "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)), "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)), "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000)), "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.basisofrecord = 'OCCURRENCE'
 AND occurrence.countrycode = 'ET'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND (occurrence.classificationdetails['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c']['specieskey'] &lt;&gt; '' AND occurrence."year" IS NOT NULL AND occurrence.hascoordinate = TRUE)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year",
 GBIF_ISEA3HCODE(9, decimallatitude, decimallongitude, COALESCE(coordinateuncertaintyinmeters, 1000))

 The dataset's 2619 records were derived from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006860-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vr96n3
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Characidium bolivianum",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 14 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006873-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9fuf6c
title: Occurrence Download

description: A dataset containing 27068 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mimosa pudica",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 27068 records from 559 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006872-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d3r2j9
title: Occurrence Download

description: A dataset containing 1107161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Belgium, Germany, Netherlands)",
 "TaxonKey is one of (Ondatra zibethicus, Myocastor coypus)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1107161 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006806-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dnhzzm
title: Occurrence Download

description: A dataset containing 20575 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Atriplex halimus"
 ]
}

 The dataset includes 20575 records from 254 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006870-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18603013
title: Supplementary Material for: Tracing the Ancestry of Northern Kurds: A Genetic Study Guided by Linguistic and Archaeological Evidence

description: This dataset contains the supplementary qpAdm modeling results, reference populations, and population IDs supporting the article: Seven, N. (2026). "Tracing the Ancestry of Northern Kurds: A Genetic Study Guided by Linguistic and Archaeological Evidence", Iran and the Caucasus (Accepted).
DOI: 10.5281/zenodo.18603012
title: Supplementary Material for: Tracing the Ancestry of Northern Kurds: A Genetic Study Guided by Linguistic and Archaeological Evidence

description: This dataset contains the supplementary qpAdm modeling results, reference populations, and population IDs supporting the article: Seven, N. (2026). "Tracing the Ancestry of Northern Kurds: A Genetic Study Guided by Linguistic and Archaeological Evidence", Iran and the Caucasus (Accepted).
DOI: 10.15468/dl.d9gp5f
title: Occurrence Download

description: A dataset containing 22516 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 66.79314043790278
 AND decimallatitude &lt;= 72.77152049497572
 AND decimallongitude &gt;= 62.86027460742454
 AND decimallongitude &lt;= 86.39962160260116
 AND GBIF_Within('POLYGON ((86.399622 66.793140, 86.399622 72.771520, 62.860275 72.771520, 62.860275 66.793140, 86.399622 66.793140))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 22516 records were derived from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006858-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q7k5dw
title: Occurrence Download

description: A dataset containing 378486 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Belgium",
 "TaxonKey is one of (Aponogeton distachyos, Arthurdendyus triangulatus, Cabomba caroliniana, Callosciurus finlaysonii, Cherax destructor, Corvus splendens, Crassula helmsii, Elodea densa, Eriocheir sinensis, Erythranthe guttata, Faxonius immunis, Faxonius juvenilis, Faxonius rusticus, Faxonius virilis, Gambusia affinis, Heracleum mantegazzianum, Heracleum persicum, Heracleum pubescens, Houttuynia cordata, , Impatiens glandulifera, Koenigia polystachya, Lagarosiphon major, Lepomis gibbosus, Limnoperna fortunei, Ludwigia grandiflora, Ludwigia peploides, Lysichiton americanus, Morone americana, Muntiacus reevesi, Myriophyllum aquaticum, Myriophyllum heterophyllum, Pacifastacus leniusculus, Petasites japonicus, Pontederia cordata, Procambarus acutus, Procambarus clarkii, Procambarus virginalis, Pseudorasbora parva, Pycnonotus cafer, Saururus cernuus, Sciurus carolinensis, Sciurus niger, Solenopsis invicta, Solenopsis richteri, Threskiornis aethiopicus, Vespa velutina, Zizania latifolia)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 378486 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006807-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60707/8xwt-0k68
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/wq3p-c633
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/xhnv-2q54
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/0rt6-2j46
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/5fcf-rb07
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/tgj9-6d29
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/axax-w647
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/d23j-4203
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/cze2-c056
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/y36h-0e80
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/4gpz-g478
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/mycr-hs66
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/ywdd-1a87
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/qb6f-gw95
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/mqcb-hb12
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/48zs-e914
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.60707/z2f6-kg57
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.15468/dl.2ju8k2
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Characidium crandellii",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 83 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006877-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60707/8ab4-qe87
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE