cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18710/czrknz/axypoc
title: 103.pdf

description: |
DOI: 10.18710/czrknz/d7jveb
title: NOU_1995_30.pdf
DOI: 10.18710/czrknz/h5zdmf
title: Meld_St_10_2015-2016.pdf
DOI: 10.18710/czrknz/hexzbl
title: 108.pdf
DOI: 10.18710/czrknz/kqm3uh
title: 104.pdf
DOI: 10.18710/czrknz/wgg3nr
title: 105.pdf
DOI: 10.18710/czrknz/xkmuxs
title: NOU_1995_23.pdf
DOI: 10.18710/czrknz/yqaxns
title: NOU_1996_2.pdf
DOI: 10.18710/czrknz/zeiohl
title: Nodes_public_documents_Research_Innovation_projects_08march.xlsx
DOI: 10.18710/gycwe6/k7fztn
title: 20200623_HOP_REDOBS_glo.txt
DOI: 10.18710/gycwe6/zg2kim
title: 20200801_HOP_REDOBS_glo.txt
DOI: 10.18710/hzkyl4/2wbybb
title: lemma.R
DOI: 10.18710/hzkyl4/7tgczy
title: notker-anno2.csv
DOI: 10.18710/hzkyl4/m8tazl
title: otfrid-1-3-anfangszeit.csv
DOI: 10.18710/hzkyl4/nscl1w
title: 0_README.txt
DOI: 10.18710/oxbb8m
title: The Norwegian Radar Oil Spill Experiment in 2015 - NORSE2015

description: The Norwegian Radar Oil Spill Experiment in 2015 (NORSE2015) was conducted in the North Sea 10 June 2015. NORSE2015 was a joint effort between UiT The Arctic University of Norway (UiT), the Jet Propulsion Laboratory (JPL)/National Aeronautics and Space Administration (NASA), and Norwegian Meteorological Institute performed in collaboration with the Norwegian Clean Seas Association for Operating Companies (NOFO) (see www.nofo.no). The experiment included release of four oil types; three mineral oil emulsion with different oil-to-water fraction (E80, E60, E40), and one plant oil (PO) (Radiagreen ebo). This data set covers; information about the oil release, drifter buoy, information and figures of the acquired SAR imageries, and link for downloading the UAVSAR time series covering the evolution of the four oil types.
DOI: 10.18710/udvrzm/knimna
title: 01_excluded_cases.pdf

description: Cases excluded from the data.
DOI: 10.18710/gycwe6/dn6twu
title: 20200917_HOP_REDOBS_gal.txt
DOI: 10.18710/q0f8pa/90m6bm
title: PLAG-66.stl
DOI: 10.18710/q0f8pa/egwefp
title: PLAG44.stl
DOI: 10.18710/q0f8pa/llbuz9
title: PLAG-149.stl
DOI: 10.18710/q0f8pa/rc2n23
title: OPX100.stl
DOI: 10.18710/q0f8pa/zupezj
title: PLAG88.stl
DOI: 10.18710/q0f8pa/zvare1
title: OPX220.stl
DOI: 10.18710/typjxu/3omjur
title: fimb704514.tif

description: projections from Disp model (see SDM.R and Dispersal.R)
DOI: 10.18710/8poyry
title: Replication data for "Electrojet estimates from mesospheric magnetic field measurements"

description: Dataset used to develop and demonstrate inversion technique for electrojet based on simulated Zeeman magnetic field measurements from the mesosphere.&lt;br&gt;&lt;br&gt;The dataset contains 1) a file with detailed information about the data in the other files (00_ReadMe), 2) a dataset of MHD simulation output (including metadata ), and 3) a dataset of simulated Zeeman magnetic field observations. &lt;br&gt;&lt;br&gt;More details are available in related publication.
description: The auroral electrojet is traditionally measured remotely with magnetometers on ground or in low Earth orbit (LEO). The sparse distribution of measurements, combined with a vertical distance of some 100 km to ground and typically &gt;300 km to LEO satellites, means that smaller scale sizes can not be detected. Because of this, our understanding of the spatiotemporal characteristics of the electrojet is incomplete. Recent advances in measurement technology give hope of overcoming these limitations by multi-point remote detections of the magnetic field in the mesosphere, very close to the electrojet. We present a prediction of the magnitude of these disturbances, inferred from the spatiotemporal characteristics of magnetic field-aligned currents. We also discuss how a small satellite that carries Zeeman magnetic field sensors [Yee et al., 2020] could be used to essentially image the equivalent current at unprecedented spatial resolution. The electrojet imaging is demonstrated by combining carefully simulated measurements with a spherical elementary current representation using a novel inversion scheme.
DOI: 10.18710/8poyry/5i4yyk
title: sigmaH.2430.mix.reduced.xmf

description: Metadata for MHD simulation output
DOI: 10.18710/gycwe6/5rwfc2
title: 20200326_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/8hg8wu
title: 20200603_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/godvv3
title: 20200915_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/ogxshf
title: 20201005_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/qzwyog
title: 20200111_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/tid7ji
title: 20201210_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/ygpdfc
title: 20200718_HOP_REDOBS_gal.txt
DOI: 10.18710/l41igq
title: UAV Database
title: An overview of typical UAV specifications

description: This database gives an overview of the specifications of 93 unmanned aerial vehicles (UAVs). This database can be used to characterize different types of UAVs and their properties. The database contains the following information for each UAV: 
- Maximum takeoff weight (MTOW)
- Payload
- Wingspan
- Lenght
- Cruise speed
- Maximum speed
- Stall speed
- Range
- Endurance
- Altitude 
- Airfoil
- Aspect ratio
- Chord length (estimated)
- Propeller diameter
- UAV type
DOI: 10.18710/l41igq/sfb7dv
title: 02_Database.csv
DOI: 10.18710/q0f8pa/1qo9ga
title: PLAG153.stl
DOI: 10.18710/q0f8pa/35lzdv
title: OPX-28.stl
DOI: 10.18710/q0f8pa/4fo2bb
title: OPX212.stl
DOI: 10.18710/q0f8pa/cbqlvr
title: PLAG-3.stl
DOI: 10.18710/q0f8pa/ckg8rw
title: A-3.jpg
DOI: 10.18710/q0f8pa/hxsohh
title: OPX-264.stl
DOI: 10.18710/q0f8pa/jobzpf
title: OPX-19.stl
DOI: 10.18710/q0f8pa/k7n9qf
title: OPX-231.stl
DOI: 10.18710/q0f8pa/oje3f2
title: PLAG4.stl
DOI: 10.18710/q0f8pa/pvdpzo
title: PLAG109.stl
DOI: 10.18710/q0f8pa/qzhbwh
title: PLAG47.stl
DOI: 10.18710/q0f8pa/tsx0e0
title: OPX204.stl
DOI: 10.18710/q0f8pa/uflbyy
title: PLAG40.stl
DOI: 10.18710/q0f8pa/ur02yh
title: OPX-245.stl
DOI: 10.18710/q0f8pa/xcadg8
title: OPX-175.stl
DOI: 10.18710/q0f8pa/ykgdl4
title: OPX278.stl
DOI: 10.18710/typjxu/7lngkj
title: Interactive_maps.html

description: HTML file with interactive maps (download/unzip the whole root folder to open it)
DOI: 10.18710/typjxu/c9znqf
title: Presence_plantarius.tab

description: Dataframe of records of D. plantarius in Fennoscandia after cleaning
DOI: 10.18710/typjxu/fralsj
title: plan7045CS13.tif

description: projections from DispCS model (see SDM.R, Connectivity.R and Dispersal.R)
DOI: 10.18710/typjxu/i7cohg
title: fimb7045CS29.tif

description: projections from DispCS model (see SDM.R, Connectivity.R and Dispersal.R)
DOI: 10.18710/typjxu/ljqo0w
title: fimb5045CS16.tif

description: projections from DispCS model (see SDM.R, Connectivity.R and Dispersal.R)
DOI: 10.18710/typjxu/nohs9f
title: fimb7045CS24.tif

description: projections from DispCS model (see SDM.R, Connectivity.R and Dispersal.R)
DOI: 10.18710/typjxu/twzdfn
title: plan70452.tif

description: projections from Disp model (see SDM.R and Dispersal.R)
DOI: 10.18710/typjxu/uipphb
title: plan708527.tif

description: projections from Disp model (see SDM.R and Dispersal.R)
DOI: 10.18710/typjxu/wvoddy
title: plan508527.tif

description: projections from Disp model (see SDM.R and Dispersal.R)
DOI: 10.18710/czrknz/wuycwv
title: Ot_prp_nr_76_2000-2001.pdf
DOI: 10.18710/gycwe6/1c4y5x
title: 20200119_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/c3ks2u
title: 20200325_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/seru5j
title: 20200403_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/v2g5em
title: 20200924_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/xnhnaw
title: 20200705_HOP_REDOBS_glo.txt
DOI: 10.18710/q0f8pa/21b77o
title: PLAG-78.stl
DOI: 10.18710/q0f8pa/bcmvco
title: PLAG-28.stl
DOI: 10.18710/q0f8pa/cdpljk
title: OPX209.stl
DOI: 10.18710/q0f8pa/d0j2ru
title: OPX60.stl
DOI: 10.18710/q0f8pa/mdjkit
title: OPX183.stl
DOI: 10.18710/q0f8pa/ocd2vb
title: OPX182.stl
DOI: 10.18710/q0f8pa/yc3nb4
title: OPX104.stl
DOI: 10.18710/q0f8pa/yhxoki
title: OPX92.stl
DOI: 10.18710/gycwe6/0iwrxt
title: 20200526_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/6rvdkz
title: 20200715_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/bpnoj6
title: 20200117_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/uqtejy
title: 20200721_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/yo3v4h
title: 20200418_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/z4fdkr
title: 20200507_HOP_REDOBS_gal.txt
DOI: 10.18710/q0f8pa/6tiy6z
title: PLAG150.stl
DOI: 10.18710/q0f8pa/fhfzj6
title: OPX57.stl
DOI: 10.18710/q0f8pa/jz4zyr
title: OPX270.stl
DOI: 10.18710/q0f8pa/lg3xaf
title: OPX268.stl
DOI: 10.15468/dl.nknvby
title: Occurrence Download

description: A dataset containing 1105 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chrotopterus auritus"
 ]
}

 The dataset includes 1105 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054557-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18710/5e8vui/52alac
title: SizeST_Day15_fr_T3_2.pdf
DOI: 10.18710/gycwe6/6lacg5
title: 20200824_HOP_REDOBS_glo.txt
DOI: 10.18710/gycwe6/apihog
title: 20200501_HOP_REDOBS_glo.txt
DOI: 10.18710/gycwe6/dgjgma
title: 20200302_HOP_REDOBS_glo.txt
DOI: 10.18710/gycwe6/dizeig
title: 20201221_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/klvl3s
title: 20200401_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/r5acxy
title: 20201206_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/uloh3k
title: 20200404_HOP_REDOBS_gal.txt
DOI: 10.18710/gycwe6/vojbxg
title: 20200501_HOP_REDOBS_gps.txt
DOI: 10.18710/gycwe6/vxmobe
title: 20200809_HOP_REDOBS_gal.txt
DOI: 10.18710/typjxu/9k5xws
title: fimb5045CS3.tif

description: projections from DispCS model (see SDM.R, Connectivity.R and Dispersal.R)
DOI: 10.18710/typjxu/jrrenr
title: fimb5085CS6.tif

description: projections from DispCS model (see SDM.R, Connectivity.R and Dispersal.R)
DOI: 10.18710/typjxu/qcgxqu
title: plan504511.tif

description: projections from Disp model (see SDM.R and Dispersal.R)
DOI: 10.18710/5e8vui/4rhdgu
title: SizeST_Day60_fr_C4_1.pdf
DOI: 10.18710/5e8vui/movjg7
title: ISX_RAW_T1_50ugmL_6h.cif
DOI: 10.18710/5e8vui/rznrie
title: SizeST_Day30_fr_C3_1.pdf
DOI: 10.18710/czrknz/4dzsri
title: 159.pdf
DOI: 10.18710/czrknz/nf4zp5
title: NOU_2016_27.pdf
DOI: 10.18710/czrknz/w8quao
title: NOU_2004_22.pdf
DOI: 10.18710/q0f8pa/0q0udt
title: OPX-4.stl
DOI: 10.18710/q0f8pa/5lue84
title: OPX-36.stl
DOI: 10.18710/q0f8pa/6igrld
title: OPX-1.stl
DOI: 10.18710/q0f8pa/n5qqv2
title: OPX68.stl
DOI: 10.18710/q0f8pa/nlospu
title: OPX-45.stl
DOI: 10.18710/q0f8pa/u6azov
title: OPX-42.stl
DOI: 10.18710/q0f8pa/w0zeq2
title: OPX-158.stl
DOI: 10.18710/q0f8pa/wgzvzo
title: OPX-211.stl
DOI: 10.18710/q0f8pa/yxacic
title: OPX-93.stl
DOI: 10.18710/typjxu
title: Replication Data for: Explicit integration of dispersal-related metrics improves predictions of SDM in predatory arthropods

description: This dataset includes presences of both fishing spiders in Fennoscandia and the R scripts to prepare and run the species distribution models (SDMs). The different predictions are also available, together with an HTML file with interactive maps of the suitable/reachable habitat predictions.
Abstract: Fishing spiders (&lt;i&gt;Dolomedes spp.&lt;/i&gt;) make an interesting model to predict the impact of global changes because they are generalist, opportunistic predators, whose distribution is driven mostly by abiotic factors. Yet, the two European species are expected to react differently to forthcoming environmental changes, because of habitat specialization and initial range. We used an original combination of habitat and dispersal data to revisit these predictions under various climatic scenarios. We used the future range of suitable habitat, predicted with habitat variables only, as a base layer to further predict the range or reachable habitat by accounting for both dispersal ability and landscape connectivity. Our results confirm the northward shift in range and indicate that the area of co-occurrences should also increase. However, reachable habitat should expand less than suitable habitat, especially when accounting for landscape connectivity. In addition, the potential range expansion was further limited for the red-listed &lt;i&gt;D. plantarius&lt;/i&gt;, which is more habitat-specialist and has a lower ability to disperse. This study highlights the importance of looking beyond habitat variables to produce more accurate predictions for the future of arthropods populations.
description: R, R 4.0.2
DOI: 10.5281/zenodo.14329106
title: LIKE-A-PRO_Task 6.5_Campaign Results Week Without Meat Belgium 2024 (Semaine Sans Viande/Week Zonder Vlees)

description: As part of the LIKE-A-PRO project, Foundation Week Without Meat Europe (Amsterdam, Netherlands) introduced the behaviour change campaign 'Week Without Meat' in Belgium for the second time in 2024, under the local name 'Week Zonder Vlees' (Flemish)/'Semaine Sans Viande' (French). After the campaign took place, sample research was carried out in order to assess the visibility of the campaign, calculate the number of participants, assess their reasons for participating, participants' intention for behaviour change etc. An online survey was carried out by taking a representative sample of the population (of ca. 1000 people) by research institute Norstat. These reports contain the main findings (in the .pdf file) and the data tables (in the .xlsx file).
DOI: 10.5281/zenodo.14329107
title: LIKE-A-PRO_Task 6.5_Campaign Results Week Without Meat Belgium 2024 (Semaine Sans Viande/Week Zonder Vlees)

description: As part of the LIKE-A-PRO project, Foundation Week Without Meat Europe (Amsterdam, Netherlands) introduced the behaviour change campaign 'Week Without Meat' in Belgium for the second time in 2024, under the local name 'Week Zonder Vlees' (Flemish)/'Semaine Sans Viande' (French). After the campaign took place, sample research was carried out in order to assess the visibility of the campaign, calculate the number of participants, assess their reasons for participating, participants' intention for behaviour change etc. An online survey was carried out by taking a representative sample of the population (of ca. 1000 people) by research institute Norstat. These reports contain the main findings (in the .pdf file) and the data tables (in the .xlsx file).
DOI: 10.5281/zenodo.14296587
title: Prepared binned DNA data storage datasets for reconstruction benchmarking.

description: This repository includes datasets from the following publications. 

1 Grass, R. N., Heckel, R., Puddu, M., Paunescu, D., & Stark, W. J. Robust chemical preservation of digital information on DNA in silica with error-correcting codes. Angewandte Chemie International Edition, 54, 8, 2552–2555 (2015)

2 Erlich, Y. & Zielinski, D. DNA fountain enables a robust and efficient storage architecture. Science, 355, 6328, 950–954 (2017).

3 Srinivasavaradhan, S. R., Gopi, S., Pfister, H. D. & Yekhanin S. Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage. in 2021 IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, 2453–2458 (2021).

 

The datasets are given in a binned format to enhance the reproducibility of the results presented in the paper. 

Bar-Lev, D., Orr, I., Sabary, O., Etzion T., & Yakkobi, E.   Scalable and robust DNA-based storage via coding theory and deep learning. 2024.

 

Detailed description of the format

The binned format was created using the binning step described in the paper ("Scalable and robust DNA-based storage via coding theory and deep learning"). Each cluster of reads appears in the file with a header followed by the reads. More specifically:





The header consists of 2 lines, the first corresponds to the encoded sequence of the clusters, and the second is a line of 18x“*” that should be ignored




The reads in the clusters are provided after the header, where each read is given in a separate line




Each cluster ends with two empty lines





Data processing

To ease the processing of our datasets, we also provide the following Python scripts (see https://github.com/itaiorr/Deep-DNA-based-storage)





reads_preprocessor.py includes our preprocessing procedure for the raw reads. The procedure detects  and truncates the primers 




binning.py - parses the file of the binned reads and creates two Python dictionaries. In the first dictionary, each key is an encoded sequence,  and the value is a  list of the reads in the cluster. In the second dictionary the keys are the index and the value is a list of the reads in the cluster.
DOI: 10.5281/zenodo.14296588
title: Prepared binned DNA data storage datasets for reconstruction benchmarking.

description: This repository includes datasets from the following publications. 

1 Grass, R. N., Heckel, R., Puddu, M., Paunescu, D., & Stark, W. J. Robust chemical preservation of digital information on DNA in silica with error-correcting codes. Angewandte Chemie International Edition, 54, 8, 2552–2555 (2015)

2 Erlich, Y. & Zielinski, D. DNA fountain enables a robust and efficient storage architecture. Science, 355, 6328, 950–954 (2017).

3 Srinivasavaradhan, S. R., Gopi, S., Pfister, H. D. & Yekhanin S. Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage. in 2021 IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, 2453–2458 (2021).

 

The datasets are given in a binned format to enhance the reproducibility of the results presented in the paper. 

Bar-Lev, D., Orr, I., Sabary, O., Etzion T., & Yakkobi, E.   Scalable and robust DNA-based storage via coding theory and deep learning. 2024.

 

Detailed description of the format

The binned format was created using the binning step described in the paper ("Scalable and robust DNA-based storage via coding theory and deep learning"). Each cluster of reads appears in the file with a header followed by the reads. More specifically:





The header consists of 2 lines, the first corresponds to the encoded sequence of the clusters, and the second is a line of 18x“*” that should be ignored




The reads in the clusters are provided after the header, where each read is given in a separate line




Each cluster ends with two empty lines





Data processing

To ease the processing of our datasets, we also provide the following Python scripts (see https://github.com/itaiorr/Deep-DNA-based-storage)





reads_preprocessor.py includes our preprocessing procedure for the raw reads. The procedure detects  and truncates the primers 




binning.py - parses the file of the binned reads and creates two Python dictionaries. In the first dictionary, each key is an encoded sequence,  and the value is a  list of the reads in the cluster. In the second dictionary the keys are the index and the value is a list of the reads in the cluster.
DOI: 10.15468/dl.wwxure
title: Occurrence Download

description: A dataset containing 294 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Maximising fish detection with eDNA metabarcoding, Browse Island, Australia (2017)"
 ]
}

 The dataset includes 294 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062144-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a9t4d9
title: Occurrence Download

description: A dataset containing 2610 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is eDNA metabarcoding survey of Cocos (Keeling) Islands, Indian Ocean (2017)"
 ]
}

 The dataset includes 2610 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062145-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/wgnv-s92t
title: Measurement of Aerosol_absorption_coefficient at Barrow

description: Measurement of Aerosol_absorption_coefficient at Barrow
DOI: 10.15468/dl.taxhgn
title: Occurrence Download

description: A dataset containing 10059 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is 12S eDNA from seawater samples collected within the coastal zone of the Belgian part of the North Sea"
 ]
}

 The dataset includes 10059 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062147-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bvudjr
title: Occurrence Download

description: A dataset containing 581 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding"
 ]
}

 The dataset includes 581 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062148-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y3fmkt
title: Occurrence Download

description: A dataset containing 501 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Megafauna at the Haakon Mosby mud volcano (the Barents Sea) based on image analysis."
 ]
}

 The dataset includes 501 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062149-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57754/fdat.rtpsy-w0k69
title: Iteration/Selfpaced reading: Testitems Experiment 2

description: Diese Datei beinhaltet die 38 Testitems von Experiment 2.
description: Research carried out in work package C02 of the SFB 833.
DOI: 10.15468/dl.kpdtdc
title: Occurrence Download

description: A dataset containing 94281 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Vertical and horizontal environmental DNA (eDNA) patterns of fish in a shallow and well-mixed North Sea area"
 ]
}

 The dataset includes 94281 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062146-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cmc58y
title: Occurrence Download

description: A dataset containing 501 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Megafauna at the Haakon Mosby mud volcano (the Barents Sea) based on image analysis."
 ]
}

 The dataset includes 501 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062150-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57754/fdat.7affa-kfx41
title: Selfpaced reading: Testitems Experiment 2

description: Diese Datei beinhaltet die 38 Testitems von Experiment 2.
This file contains the 38 testitems of experiment 2.
description: Research carried out in work package C02 of the SFB 833.
DOI: 10.57754/fdat.7mkjq-2q050
title: Selfpaced reading: Füllsätze Experiment 2

description: Diese Datei beinhaltet die 38 Füllsätze von Experiment 2.
This file contains the 38 fillers of experiment 2.
description: Research carried out in work package C02 of the SFB 833.
DOI: 10.6084/m9.figshare.28172411
title: YAMAEI Survey Data 2024

description: This data is collected in 2024 by independent online survey.
DOI: 10.6084/m9.figshare.28172411.v1
title: YAMAEI Survey Data 2024

description: This data is collected in 2024 by independent online survey.
DOI: 10.48597/s7gu-b2q4
title: Measurement of Aerosol_absorption_coefficient at Barrow

description: Measurement of Aerosol_absorption_coefficient at Barrow