cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14045026
title: 2010_2023_RelativeHumidity_ERA5_FourirerProcess_1k

description: This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5  Relative Humidity data:

ERA5:  Relative Humidity

The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.This series of ERA5  data, processed according to Scharlemann et al (2008), has been updated to include imagery for 2010-2023.

 

Process:

Image values were extracted from ERA5(  Relative Humidity) 1 km imagery for 2010-2023.  Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)  Sea pixels were masked with a VIIRS land/sea layer . The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.

 

This new RH   Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 

File names:

The last two characters of each file name denote the output from Fourier processing:a0 - meanmn - minimummx - maximuma1 - amplitude of annual cyclea2 - amplitude of bi-annual cyclea3 - amplitude of tri-annual cyclep1 - phase of annual cyclep2 - phase of bi-annual cyclep3 - phase of tri-annual cycled1 - variance in annual cycled2 - variance in bi-annual cycled3 - variance in tri-annual cycleda - combined variance in annual, bi-annual, and tri-annual cyclesvr - variance in raw dataParameter Fourier Variable Image values areERA5  A0, A1, A2, A3, Min, Max, Vr Reflectance values  monthly total RH in mmALL D1,D2,D3,Da PercentagesALL E1,E2,E3 PercentagesALL P1,P2.P3 Months*100. (Jan=100)

 

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)Spatial extent:north: 82:00:30Nsouth: 18Nwest: 32:00:30Weast: 70E

Spatial resolution:30 arc seconds (approx. 1000 m)

Temporal resolution:Monthly  from 2010 to 2023

Pixel values

Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)

Source: 

 Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data: https://zenodo.org/records/12699259

Software used: 

Codes are written in Python and C++The software used for map production is ESRI ArcMap 10.8

License: CC-BY-SA 4.0Processed by:ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ )
DOI: 10.5281/zenodo.14045027
title: 2010_2023_RelativeHumidity_ERA5_FourirerProcess_1k

description: This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5  Relative Humidity data:

ERA5:  Relative Humidity

The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.This series of ERA5  data, processed according to Scharlemann et al (2008), has been updated to include imagery for 2010-2023.

 

Process:

Image values were extracted from ERA5(  Relative Humidity) 1 km imagery for 2010-2023.  Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)  Sea pixels were masked with a VIIRS land/sea layer . The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.

 

This new RH   Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. 

File names:

The last two characters of each file name denote the output from Fourier processing:a0 - meanmn - minimummx - maximuma1 - amplitude of annual cyclea2 - amplitude of bi-annual cyclea3 - amplitude of tri-annual cyclep1 - phase of annual cyclep2 - phase of bi-annual cyclep3 - phase of tri-annual cycled1 - variance in annual cycled2 - variance in bi-annual cycled3 - variance in tri-annual cycleda - combined variance in annual, bi-annual, and tri-annual cyclesvr - variance in raw dataParameter Fourier Variable Image values areERA5  A0, A1, A2, A3, Min, Max, Vr Reflectance values  monthly total RH in mmALL D1,D2,D3,Da PercentagesALL E1,E2,E3 PercentagesALL P1,P2.P3 Months*100. (Jan=100)

 

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)Spatial extent:north: 82:00:30Nsouth: 18Nwest: 32:00:30Weast: 70E

Spatial resolution:30 arc seconds (approx. 1000 m)

Temporal resolution:Monthly  from 2010 to 2023

Pixel values

Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)

Source: 

 Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data: https://zenodo.org/records/12699259

Software used: 

Codes are written in Python and C++The software used for map production is ESRI ArcMap 10.8

License: CC-BY-SA 4.0Processed by:ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ )
DOI: 10.25625/cvuepo/1huvjp
title: 20.06.2024_yield_data_final.csv

description: :unav
DOI: 10.25625/cvuepo/aupuin
title: 06_Models_COA.R

description: GLMMs evaluating effects of grafting age and other predictors on arthropod diversity
DOI: 10.25625/cvuepo/n6qlvb
title: Old_yield_Sophie.tab

description: Historical data of crop yield for experimental plots obtained from Norandino Ltda.
DOI: 10.25625/cvuepo/pzxiex
title: 01_data_preparation.R

description: Preparation of the morphospp_with_groups data table
DOI: 10.25625/cvuepo/qfxypn
title: Grafts_Data_IC6.tab

description: Data collected by Fredy Yovera about the potential productivity of grafted cacao plants.
DOI: 10.25625/cvuepo/0xdhh3
title: 06.2024_yield_data_final.csv

description: :unav
DOI: 10.25625/cvuepo/cxibnl
title: Old_yield_values.tab

description: Historical data of crop yield for experimental plots obtained from Norandino Ltda.
DOI: 10.25625/cvuepo/ecd4gx
title: morphospp_with_groups.csv

description: :unav
DOI: 10.25625/cvuepo/epxcxy
title: 07_Yield_prep_analyses.R

description: GLMMs evaluating the effect of grafting on cacao yield
DOI: 10.25625/cvuepo/mv87ox
title: IS.IM.tab

description: Pod index measured by Fredy Yovera. Used together with Grafts_Data_IC6 to calculate the yield and assess the effect of grafting on productivity
DOI: 10.25625/cvuepo/umbsky
title: table_arthropods.tab

description: Original data table with abundances of different arthropod morphospecies sampled per cacao tree, distinguishing sampling methods and times of the day
DOI: 10.25625/cvuepo/y9hr6p
title: 8.0_single_group_models.R

description: GLMMs evaluating the effects of grafting age and other predictors on the abundance and diversity of selected arthropod taxa
DOI: 10.5281/zenodo.11049534
title: Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2018-11-05 to 2018-12-31 [RAW]

description: Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. The phenocam "hartheim2" was put into operation on November 5, 2018. There are no phenocam images before that date at this site.

Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the ICOS Associate Ecosyste Site DE-Har, Germany recording the phenology and state of in-canopy vegetation.
description: TECHNICAL INFO

Files were submitted and recorded as part of the global Phenocam Network with the site name "hartheim2" (47.9338ºN, 7.5981ºE, 201 m, ICOS ecosystem site code: DE-Har, UFR 6 letter code: HARTHM).

Access to the "hartheim2" phenocam on the global phenocam network through: https://phenocam.nau.edu/webcam/sites/hartheim2/

Files are organised in sub-directories per year and month of the year.

CREATOR

This work was created by the University of Freiburg, Germany.

LICENSE

Creative Commons Attribution 4.0 International. 

Please read the Phenocam Fair Use Policy.

ATTRIBUTION NOTICE

The site is operated as part of the Integrated Carbon Observation System (ICOS).

The site and phenocams are operated by the Chair of Environmental Meteorology, University of Freiburg, Germany.

Data were managed and attributed by the PhenoCam Network, which has been supported by the National Science Foundation, the Long-Term Agroecosystem Research (LTAR) network which is supported by the United States Department of Agriculture (USDA), the U.S. Department of Energy, the U.S. Geological Survey, the Northeastern States Research Cooperative, and the USA National Phenology Network. 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors and/or the involved institutions, accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The author and/or the involved institutions are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Hartheim Forest Research Site

Global Phenocam Network
DOI: 10.5281/zenodo.11049535
title: Data from Phenocam (PHE) measurements of in-canopy vegetation (hartheim2) at Hartheim Forest Research Site (DE-Har) from 2018-11-05 to 2018-12-31 [RAW]

description: Phenocam images from "hartheim2" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2022. The phenocam "hartheim2" was put into operation on November 5, 2018. There are no phenocam images before that date at this site.

Phenocam "hartheim2" shows the view from the main tower at 8.4 m height towards N at the ICOS Associate Ecosyste Site DE-Har, Germany recording the phenology and state of in-canopy vegetation.
description: TECHNICAL INFO

Files were submitted and recorded as part of the global Phenocam Network with the site name "hartheim2" (47.9338ºN, 7.5981ºE, 201 m, ICOS ecosystem site code: DE-Har, UFR 6 letter code: HARTHM).

Access to the "hartheim2" phenocam on the global phenocam network through: https://phenocam.nau.edu/webcam/sites/hartheim2/

Files are organised in sub-directories per year and month of the year.

CREATOR

This work was created by the University of Freiburg, Germany.

LICENSE

Creative Commons Attribution 4.0 International. 

Please read the Phenocam Fair Use Policy.

ATTRIBUTION NOTICE

The site is operated as part of the Integrated Carbon Observation System (ICOS).

The site and phenocams are operated by the Chair of Environmental Meteorology, University of Freiburg, Germany.

Data were managed and attributed by the PhenoCam Network, which has been supported by the National Science Foundation, the Long-Term Agroecosystem Research (LTAR) network which is supported by the United States Department of Agriculture (USDA), the U.S. Department of Energy, the U.S. Geological Survey, the Northeastern States Research Cooperative, and the USA National Phenology Network. 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors and/or the involved institutions, accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The author and/or the involved institutions are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Hartheim Forest Research Site

Global Phenocam Network
DOI: 10.15468/dl.s8xuad
title: Occurrence Download

description: A dataset containing 6046 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Grimmia montana Bruch & Schimp."
 ]
}

 The dataset includes 6046 records from 224 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029864-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.scs73z
title: Occurrence Download

description: A dataset containing 3748 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-156.9921 20.94283,-157.03885 20.9356,-157.0701 20.91833,-157.06352 20.87228,-157.03967 20.85336,-157.01583 20.84021,-157.00513 20.80896,-156.99115 20.77277,-156.9854 20.74317,-156.97224 20.72343,-156.92656 20.71274,-156.85521 20.72584,-156.80278 20.76808,-156.78822 20.81759,-156.83336 20.88166,-156.89744 20.93555,-156.9921 20.94283))"
 ]
}

 The dataset includes 3748 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029865-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-4355
title: Getting the right clones in an automated manner: an alternative to sophisticated colony-picking robotics

description: In recent years, the Design-Build-Test-Learn (DBTL) cycle has become a key concept in strain engineering. Modern biofoundries enable automated DBTL cycling using robotic devices. However, both highly automated facilities and semi-automated facilities encounter bottlenecks in clone selection and screening. While fully automated biofoundries can take advantage of expensive commercially available colony picker, semi-automated facilities have to fall back on affordable alternatives. Therefore, our clone selection method is particularly well-suited for academic settings, requiring only basic infrastructure of a biofoundry.
The automated liquid clone selection method (ALCS) represents a straightforward approach for clone selection. Similar to sophisticated colony picking robots, the ALCS approach aims to achieve high selectivity. Investigating the time analogue of five generations, the model-based set-up reached a selectivity of 98 ± 0.2 % for correctly transformed cells. Moreover, the method is robust to variations in cell numbers at the start of ALCS. Beside Escherichia coli, promising chassis organisms, such as Pseudomonas putida and Corynebacterium glutamicum, were successfully applied. In all cases, ALCS enables the immediate use of the selected strains in follow-up applications. In essence, our ALCS approach provides a ‘low-tech’ method to be implemented in biofoundry settings without requiring additional devices.
description: This dataset serves as supplementary data to the related publication.
DOI: 10.15468/dl.dky2r2
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dalbergia calderonii Standl."
 ]
}

 The dataset includes 22 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0117149-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10665220
title: [wrong version, find the dataset here: https://doi.org/10.5281/zenodo.13789648] Migration and Expulsion in the Reichsgau Wartheland

description: Find the dataset here: https://doi.org/10.5281/zenodo.13789648
DOI: 10.5281/zenodo.10665221
title: [wrong version, find the dataset here: https://doi.org/10.5281/zenodo.13789648] Migration and Expulsion in the Reichsgau Wartheland

description: Find the dataset here: https://doi.org/10.5281/zenodo.13789648
DOI: 10.15468/dl.enzbr5
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Grimmia obtusifolia C.Gao & T.Cao"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029868-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7tmh4x
title: Occurrence Download

description: A dataset containing 297 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Vespa soror Buysson, 1905)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 297 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029869-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-4121
title: Source Code: A Chemistry Load Balancing Model for OpenFOAM

description: &lt;p&gt;Efficient simulation tools are crucial for studying complex systems such as
reacting flows where computational costs of computing chemical reaction rates can vastly exceed the costs for the integration of the convective and diffusive transport terms. Load imbalance in parallel computing poses a significant challenge for massively parallel reacting flow simulations. In response, a novel load balancing library has been developed to enhance OpenFOAM’s solver performance in parallel environments. This library seamlessly integrates with OpenFOAM, offering ease of use and applicability to any OpenFOAM reacting solver incorporating finite-chemistry. In addition, it supports the standard and the dynamic adaptive chemistry model (TDAC) of OpenFOAM. The newly developed load-balanced standard and TDAC models address significant load imbalances by exchanging information between processes via MPI calls and tracking ODE solution times on a cell level. The TDAC model introduces dual tables on each core and enables immediate addition of computed solutions, enhancing computational efficiency. Validation on various test cases, including simulations on the HLRS Hawk supercomputer up to 8000 cores, confirm identical results compared to the original unbalanced models, with notable speed-up factors of up to 6 for the standard and 5 for the TDAC model. Despite non-linear scaling at lower cell count per processor, load-balanced models consistently outperform unbalanced counterparts, making them the preferred choice for reacting flow simulations in OpenFOAM.&lt;/p&gt;

&lt;p&gt;See more information about the usage of this library in the &lt;a href="https://github.com/ITV-Stuttgart/loadBalancedChemistryModel/blob/main/README.md"&gt;README&lt;/a&gt;.&lt;/p&gt;
DOI: 10.15468/dl.ykffc9
title: Occurrence Download

description: A dataset containing 67393 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Pycnonotus sinensis (Gmelin, 1789)"
 ]
}

 The dataset includes 67393 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029871-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-4339
title: Modell zur Vorhersage von Prozessparametern eines Laserprozesses auf Basis von RGB-Farbwerten
title: Künstliches neuronales Netz

description: &lt;p&gt;Künstliches neuronales Netz mit zwei verdeckten Schichten mit 50 bzw. 25 Neuronen und einer ReLU-Aktivierungsfunktion. Die Ausgabeschicht hat eine sigmoide Aktivierungsfunktion. Für das Training wird ein Adam-Optimierer verwendet, der den mittleren quadratischen Fehler der Vorhersage gegenüber der erwarteten Ausgabe minimiert und so die Gewichte des Netzes bestimmt.&lt;/p&gt;

&lt;br&gt;

&lt;h3&gt;Eingänge zw. 0 und 1:&lt;/h3&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;stong&gt;b:&lt;/stong&gt; Blau im RGB-Farbraum&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;g:&lt;/stong&gt; Grün im RGB-Farbraum&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;r:&lt;/stong&gt; Blau im RGB-Farbraum&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;Ausgänge zw. 0 und 1:&lt;/h3&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;stong&gt;hatch_um:&lt;/stong&gt; Der Hatch-Abstand zwischen Scanlinien in µm&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;power_mw:&lt;/stong&gt; Die Laserleistung in mW&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;pulse_us:&lt;/stong&gt; Die Pulsrate in µs&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;speed_um_s:&lt;/stong&gt; Die Scangeschwindigkeit in µm/s&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;Skaliert wie folgt:&lt;/h3&gt;
&lt;ul&gt;
 &lt;li&gt;r, b, g: von 0 bis 255 [-]&lt;/li&gt;
 &lt;li&gt;hatch_um: von 1 bis 100 in [µs]&lt;/li&gt;
 &lt;li&gt;power_mw: von 1000 bis 20000 in [mW]&lt;/li&gt;
 &lt;li&gt;pulse_us: von 2 bis 10 in [µs]&lt;/li&gt;
 &lt;li&gt;speed_um_s: von 41666 bis 3333333 in [µm/s]&lt;/li&gt;
&lt;/ul&gt;

&lt;br&gt;

Das Modell kann mit dem folgenden Python-Code ausgeführt werden:
&lt;pre&gt;
&lt;code&gt;
from keras.models import load_model

def scale_value(x, new_min, new_max):
 scaled_value = (x * (new_max - new_min)) + new_min
 return scaled_value

r = 123
b = 231
g = 85
model = load_model('model.keras')
output = model.predict(np.array([b / 255, g / 255, r / 255]).reshape(1, -1))
 
hatch_um = int(scale_value(output[0][0], 1, 100))
power_mw = int(scale_value(output[0][1], 1000, 20000))
pulse_us = int(scale_value(output[0][2], 2, 10))
speed_um_s = int(scale_value(output[0][3], 41666, 3333333))
&lt;/code&gt;
&lt;/pre&gt;
DOI: 10.18419/darus-4338
title: Prozessparameter und Farbwerte für einen Laserprozess auf Edelstahl

description: Bei diesem Versuch wurde eine Edelstahlplatte mit unterschiedlichen Prozessparametern eines Lasersystems belichtet und die resultierende Farbe mit einem Mikroskop ausgewertet.

Die zentralen Komponenten der Laseranlage sind ein Galvanometerscanner (MINISCAN III mit einer Strahleintrittsapertur von 14 mm) der Firma Raylase, ein f -Theta Objektiv (Brennweite 160 mm) und ein gepulster Laser (TruPulse 2002 nano - FK10-RM mit einer mittleren Ausgangsleistung von 20 W und einer Wellenlänge von
1064 nm) der Firma Trumpf. 

Jedes belichtete Feld hatte eine Größe von 2 mm.

In Summe wurden 625 Felder mit einer einzigartigen Parameterkombination belichtet. Die verwendeten Parameter sind dabei:

&lt;ul&gt;
 &lt;li&gt;&lt;stong&gt;Geschwindigkeiten in mm/min:&lt;/stong&gt; 30000, 20000, 15000, 10000, 5000&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;Laserleistung in ‱ (1 pro Zehntausend) von 20 W:&lt;/stong&gt; 6000, 5000, 4000, 3000, 2000&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;Pulsrate in μs:&lt;/stong&gt; 2, 3, 4, 5, 6&lt;/li&gt;
 &lt;li&gt;&lt;stong&gt;Hatch-Abstand in mm:&lt;/stong&gt; 0.01, 0.005, 0.0025, 0.002, 0.001&lt;/li&gt;
&lt;/ul&gt;
DOI: 10.15468/dl.kyq6pj
title: Occurrence Download

description: A dataset containing 834 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coptis quinquefolia Miq."
 ]
}

 The dataset includes 834 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029872-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.