Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Sample data FRPPIXEL

Sample data containing some 8080 fires from September 1st, 2020 extracted from EUMESAT Surface Analysis, Satellite Application Facility on Land Surface Analysis (<a href="https://www.eumetsat.int/lsa-saf">LSA SAF</a>) fire database.

Spectroscopic ellipsometry mapping of PAAO (AJ-1-04-20 sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 3 minutes and 16 seconds. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-1-04-20 Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method after being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.7059281

Spectroscopic ellipsometry mapping of PAAO (AJ-3-04-20 sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 8 seconds. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-3-04-20 Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method after being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.7115166

Spectroscopic ellipsometry mapping of PAAO (AJ-2-04-20 sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 3 minutes and 37 seconds. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-2-04-20 Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method after being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.7059828

opencc-byNov 2021View details →

Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network

Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, "Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"

Spectroscopic ellipsometry mapping of PAAO (AJ-4-04-20 sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide (PAAO). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 30 seconds. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-4-04-20 Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method after being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.7115401

opencc-by-4.0Oct 2019View details →

Fig. 1. Map showing the sampling sites. A in Two New Species of Microloxoconcha (Crustacea: Ostracoda) from the Sublittoral Zone in Western Japan

Fig. 1. Map showing the sampling sites. A, western area of Japan; B, type locality of Microloxoconcha toyoshioae n. sp. (solid circle); C, type locality of Microloxoconcha sublittoralis n. sp. (open circle).

opencc-by-4.0Nov 2021View details →

Audio samples from generative models trained on the TIMIT speech data.

This is a posting of audio snippets to accompany the paper "Benchmarking Generative Latent Variable Models for Speech". The snippets include samples and reconstructions. All samples are completely unconditional and utilise only the prior internal representations learned by the model. Reconstructions are computed from a given test audio snippet by first encoding it to a learned representation and then decoding that to a reconstruction of the audio. All models are trained on the TIMIT speech dataset (<a href="https://catalog.ldc.upenn.edu/LDC93s1">https://catalog.ldc.upenn.edu/LDC93s1</a>). Some snippets are from models trained at different temporal resolutions denoted by `s1` and `s64`. We refer to the paper for details. The files include: <ul> <li>`clockwork-vae-s64-reconstruction-*` <ul> <li>Four reconstructions using a two-layered Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`clockwork-vae-s64-sample-*` <ul> <li>Four samples from the prior of a Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`original-*` <ul> <li>Four original samples from TIMIT corresponding in pairs to the reconstructions.</li> </ul> </li> <li>`vrnn-s64-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`vrnn-s1-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`srnn-s64-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`srnn-s1-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s64-sample-*` <ul> <li>Four samples from a WaveNet trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s1-sample-*` <ul> <li>Two samples from a WaveNet trained with temporal resolution s=64.</li> </ul> </li> </ul>

Lithologic Compilation of Basins Sampled for Cosmogenic 10Be in the Greater Caucasus Mountains

Document ('Litho_Compilation.pdf') describing the geologic map compilation process for areas covering a suite of catchments sampled for cosmogenic 10Be within the Greater Caucasus Mountains. A shapefile ('mapunits.shp') which includes these mapped regions is provided.

Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Norway

This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.

Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Luxembourg

Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - the United kingdom

Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Finland

Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Sweden

openDec 2021View details →

Particulate methylsulfonic acid (MSA), sodium and chloride concentrations from high-volume air filter samples over the Southern Ocean during austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract Aerosol particles come from a variety of sources: a look at the chemical composition gives insights on the particle origin. Ion chromatography was performed for aerosol particles smaller than 10 micrometers (PM10 inlet), giving concentrations of sodium and chloride, as well as particulate methylsulfonic acid (MSA). For this, aerosol particles where sampled on quartz fibre filters for 24 hours each. The sampled filters were stored at -20 degrees C on the research vessel, transported frozen back to the chemistry lab of TROPOS and analysed for main ions. Temporal coverage is from December 20, 2016 to March 20, 2017. We give 24-hour quality controlled particulate MSA, sodium and chloride concentrations in microgram per cubic meter for the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project. Dataset contents <ul> <li>ACESPACE_ particulate_MSA_Sodium_Chloride_PM10, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul>

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Calibrating phylogenies assuming bifurcation or budding alters inferred macroevolutionary dynamics in a densely sampled phylogeny of bivalve families

Analyses of evolutionary dynamics can be profoundly affected by age calibrations of phylogenetic nodes under different models of lineage branching. Most time-calibrated molecular phylogenies of extant taxa assume a purely bifurcating model, where nodes are calibrated using the daughter lineage with the older first occurrence in the fossil record. Lineages can also split via budding, in which a parent lineage persists following the origin of a daughter lineage, and nodes are calibrated using the age of the lineage with the younger first occurrence. Here, we use the extensive fossil record of bivalve molluscs for a large-scale empirical test of how the choice of branching model affects macroevolutionary analyses. We time-calibrated 91% of nodes in a phylogeny of 97 extant bivalve families using 86 calibration points ranging in age from 2.59 to 485 Ma. Allowing budding-based calibrations minimizes conflict between the tree topology and timing of evolutionary events in the fossil record, reducing the summed duration of inferred "ghost lineages," from 6.76 billion yrs (Gyr; bifurcating model) to 1.00 Gyr (budding model). Adding 31 extinct paraphyletic families – many major groups contain such extinct taxa – shifts deep splits further back in time and raises ghost-lineage totals to 7.86 Gyr (bifurcating) and 1.92 Gyr (budding), but more accurately reflects the time since separation of lineages. Lineage-through-time plots from phylogenetic data scaled under a bifurcating model of evolution push more inferred bivalve diversification into the Paleozoic, conflicting with other palaeontological evidence on the magnitude of the end-Paleozoic extinction and subsequent recovery, and strongly reduce the magnitude of the Cenozoic diversification of the group. Consideration of the hypothesized branching model within a given clade is essential when node-calibrating phylogenies, and for a major clade with a robust fossil record, an evolutionary model that allows budding and does not force bifurcations is the most appropriate one, and likely common for many other clades as well.

opencc-zeroDec 2021View details →

Fig. 45. Similarity between samples from the BANGAL 0711 in The Mollusca of Galicia Bank (NE Atlantic Ocean)

Fig. 45. Similarity between samples from the BANGAL 0711 campaign, based on presence/absence data of living specimens for molluscan species and on the Bray-Curtis similarity index (A, all samples; B, beam trawl samples only). Sample labels with depth in brackets. A similar topology was obtained using the abundance data transformed to fourth root.

opencc-by-4.0Jan 2022View details →

Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"

Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.

opencc-by-4.0Jan 2022View details →

Netflow data with sampling 500 for training (D2)

NetFlow traffic generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic) NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device. NetFlow flows have been captured with sampling 500 at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued. The version of NetFlow used to build the datasets is 5.