Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

Combined ground-based total ozone data at three Norwegian sites (2000 to 2020)

Combined total column ozone (TCO) at three Norwegian sites (Oslo, Andøya, Ny-Ålesund), using three measurement techniques (Brewer (DS and GI), SAOZ, GUV). The daily means are composed of noon-averages (+-2h around local noon) for Brewer (DS and GI) and GUV, and of sunrise- and sunset averages for SAOZ. In Oslo and Andøya, Brewer DS measurements build the baseline, and missing measurement days are then filled with Brewer GI data and then with GUV. In Ny-Ålesund, SAOZ measurements build the baseline, and missing measurement days are filled with Brewer DS data (starting in 2013) and GUV. This dataset has been used in Bernet et al. (2022). The initial data of each of the instruments are available at: Brewer DS (daily means): https://woudc.org Brewer GI: https://doi.org/10.5281/zenodo.6760244 SAOZ: www.ndacc.org GUV: https://doi.org/10.5281/zenodo.4773478 Responsible institute: NILU - Norwegian Institute for Air Research Bernet, L., Svendby, T., Hansen, G., Orsolini, Y., Dahlback, A., Goutail, F., Pazmiño, A., Petkov, B., and Kylling, A., Total ozone trends at three northern high-latitude stations, 2022.

opencc-by-4.0Jul 2022View details →

Data from: Influence of the properties of different graphene-based nanomaterials dispersed in polycaprolactone membranes on astrocytic differentiation

Abstract Composites of polymer and graphene-based nanomaterials (GBNs) combine easy processing onto porous 3D membrane geometries due to the polymer and cellular differentiation stimuli due to GBNs fillers. Aimingto step forward to the clinical application of polymer/GBNs composites, this study performs a systematic and detailed comparative analysis of the influence of the properties of four different GBNs: i) graphene oxide obtained from graphite chemically processes (GO); ii) reduced graphene oxide (rGO); iii) multilayered graphene produced by mechanical exfoliation method (Gmec); and iv) low-oxidized graphene via anodic exfoliation (Ganodic); dispersed in polycaprolactone (PCL) porous membranes to induce astrocytic differentiation. PCL/GBN flat membranes were fabricated by phase inversion technique and broadly characterized in morphology and topography, chemical structure, hydrophilicity, protein adsorption, and electrical properties. Cellular assays with rat C6 glioma cells, as model for cell-specific astrocytes, were performed. Remarkably, low GBN loading (0.67 %wt.) caused an important difference in the response of the C6 differentiation among PCL/GBN membranes. PCL/rGO and PCL/GO membranes presented the highest biomolecule markers for astrocyte differentiation. Our results pointed to the chemical structural defects in rGO and GO nanomaterials and the protein adsorption mechanisms as the most plausible cause conferring distinctive properties to PCL/GBN membranes for the promotion of astrocytic differentiation. Overall, our systematic comparative study provides generalizable conclusions and new evidences to discern the role of GBNs features for future research on 3D PCL/graphene composite hollow fiber membranes for in vitroneural models.

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Data from: imageseg: An R package for deep learning-based image segmentation

1. Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can for example be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. 2. Here, we present R package imageseg which implements a CNN-based workflow for general-purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understory vegetation images. 3. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes. 4. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on color or grayscale images, e.g. for applications in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.

opencc-zeroAug 2022View details →

A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)

Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018). Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain. Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.

Data and code for Freshwater corridors in the conterminous US: a coarse-filter approach based on lake-stream networks

This repository contains various datasets used to map and analyze freshwater connectivity (i.e., corridors) in the conterminous US based on networks of lakes, streams and rivers. We considered lake-stream networks as analogous to habitat corridors. Hub lakes are individual lakes that are disproportionately important for maintaining intact networks. We also analyzed the protection status of freshwater connectivity using the US Protected Areas Database v. 2.0. R analysis scripts can also be found in this repository. Much of the data we used came from published or soon-to-be published sources, which are referenced below.

opencc-by-4.0Apr 2022View details →

Raw Data to "Prediction of acid pKa values in the solvent acetone based on COSMO-RS"

This data is a supplement to the publication entitled "Prediction of Acid pKa Values in the Solvent Acetone based on COSMO-RS" in the Journal of Computational Chemistry (DOI:10.1002/jcc.26864). The data includes initial starting structures as inputs for conformer searches using COSMOconf (versions 2020 and 2021) in combination with TURBOMOLE (version 7.3). The corresponding output files serve as inputs for the calculation of Gibbs free energies using COSMO-RS as provided by COSMOtherm. Additional information on the file structure is given in the README file.

LPSE data for ray-based CBET test cases

This dataset contains field data from LPSE simulations for the purpose of validating ray-based CBET models.  The input parameters required to replicate these results are given in the Physics of Plasmas paper "Validation of ray-based cross-beam energy transfer models."  All of the data is stored in HDF5 files.  Each file has three data sets: Ez, xAxis, and yAxis (the 1-D datset does not have yAxis). Here is an example of the Matlab code to open and plot one of the 2-D files: <pre><code>filename = 'two_beam_at_caustic.h5'; hInfo = h5info(filename ); data = h5read(filename , '/Ez'); xAxis = h5read(filename , '/x_axis'); yAxis = h5read(filename , '/y_axis'); figure(1); clf; imagesc(yAxis, xAxis, data), colorbar, axis xy </code></pre> Here is an example of the Python code to open and plot one of the 2-D files: <pre><code class="language-python">import h5py import matplotlib.pyplot as plt filename = 'two_beam_at_caustic.h5' f = h5py.File(filename, 'r') Ez = list(f["Ez"]) x_axis = list(f["x_axis"]) y_axis = list(f["y_axis"]) plt.figure() plt.pcolormesh(x_axis,y_axis,Ez) plt.colorbar() plt.show() </code></pre>

opencc-zeroSep 2022View details →

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Extracting abundance information from DNA-based data

The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to the reconstruction of diets and quantitative foodwebs, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, capture bias, capture noise, species pipeline biases, and pipeline noise all combine to inject error into DNA-based datasets. This review focuses on methods for correcting the latter two error sources, as the first two are addressed extensively in the ecological survey literature. To extract abundance information from DNA-based data, it is useful to distinguish two concepts. (1) Across-species quantification describes relative species abundances within a single sample. (2) In contrast, within-species quantification describes how the abundance of each individual species varies across samples, where the samples could be a time series, an environmental gradient, or different experimental treatments. In the first part of this paper, we review methods to remove species pipeline biases and pipeline noise. In the second part, we provide a detailed protocol and demonstrate experimentally how to use a 'DNA spike-in' (an internal standard) to remove pipeline noise and recover within-species abundance information.

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Data from: Network-based biostratigraphy for the late Permian to mid-Triassic Beaufort Group (Karoo Supergroup) in South Africa enhances biozone applicability and stratigraphic correlation

The Permo-Triassic vertebrate assemblage zones (AZs) of South Africa's Karoo Basin are a standard for local and global correlations. However, temporal, geographical, and methodological limitations challenge the AZs reliability. We analyze a unique fossil dataset comprising 1408 occurrences of 115 species grouped into 19 stratigraphic bin intervals from the Cistecephalus, Daptocephalus, Lystrosaurus declivis, and Cynognathus AZs. Using network science tools we compare six frameworks: Broom, Rubidge, Viglietti, Member, Formation, including a framework suggesting diachroneity of the Daptocephalus/Lystrosaurus AZ boundary (Gastaldo). Our results demonstrate that historical frameworks (Broom, Rubidge) still identify the Karoo AZs. No scheme supports the Cistecephalus AZ, and it likely comprises two discrete communities. The Lystrosaurus declivis AZ is traced across all frameworks, despite many shared species with the underlying Daptocephalus AZ, suggesting the extinction event across this interval is not a statistical artifact. A community shift at the upper Katberg to lower Burgersdorp formations may indicate a depositional hiatus, which has important implications for regional correlations and Mesozoic ecosystem evolution. The Gastaldo model still identifies a Lystrosaurus and Daptocephalus AZ community shift, does not significantly improve recent AZ models (Viglietti), and highlights important issues with some AZ studies. Localized bed-scale lithostratigraphy (sandstone datums), and singleton fossils cannot be used to reject the patterns shown by hundreds of fossils, and regional chronostratigraphic markers of the Karoo foreland basin. Meter-level occurrence data suggest that 20–50 m sampling intervals capture Karoo AZs, unifying the use of meter-level placements of singleton fossils to delineate biozone boundaries and make regional correlations.

opencc-zeroSep 2022View details →

opencc-by-4.0Sep 2022View details →

CRAAS: Cloud Regime dAtAset based on the CLAAS-2.1 climate data record

The Cloud Regime dAtAset based on the CLAAS-2.1 climate data record (CRAAS) is a dataset of cloud regimes derived from cloud properties from the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=CLAAS_V002_01">CLAAS-2.1</a> climate data record. Such a cloud regime dataset can provide detailed insight in the cloud climatology over the region of interest and also in climate monitoring through the concept of the cloud regimes. CRAAS covers a region over Europe (30°N to 60°N and from 11°W to 37°E) and it extends from 2004 to 2017. The generated Joint Cloud Histograms (JCHs) of Cloud Top Pressure (CTP) and Cloud Optical Thickness (COT),  as well as the derived labeled data points of the cloud regime classification are available on a 1°x1° degree resolution and every 15 minutes. Two sets of yearly files can be found in the dataset. Those containing the generated JCHs from the CLAAS-2.1 climate data record (example filename: 'craas_jch_2004.v1.nc')  and those including the labeled data points of the cloud regime classification (example filename: 'craas_label_2004.v1.nc'). The files are provided in netCDF4 format, following the NetCDF Climate and Forecast Metadata Conventions-Version 1.8 (CF-1.8).

opencc-by-4.0Jan 2022View details →

Data for "Researchers and their data. A study based on the use of the word data in scholarly articles"

Data is one of the most used terms in scientific vocabulary. This article focusses on the relationship between data and research by analyzing the contexts of occurrence of the word data in a corpus of 72,471 research articles (1980-2012) from two distinct fields (Social sciences, Physical sciences). The aim is to shed light on the issues raised by research on data, namely the difficulty of defining what is considered as data, the transformations that data undergo during the research process and how they gain value for researchers who hold them. Relying on the distribution of occurrences throughout the texts and over time, it demonstrates that the word data mostly occurs at the beginning and at the end of research articles. Adjectives and verbs accompanying the noun data turn out to be even more important than data itself in specifying data. The increase in the use of possessive pronouns at the end of the articles reveals that authors tend to claim ownership of their data at the very end of the research process. Our research demonstrates that even if data handling operations are increasingly frequent, they are still described with imprecise verbs that do not reflect the complexity of these transformations.

opencc-by-4.0Oct 2022View details →

C-RIDGE: Indoor CO2 Data Collection System for Large Venues Based on Prior Knowledge

This CO2 of C-RIDGE system dataset contains the high spatial and temporal resolution of the CO2 measures with the corresponding timestamp  of static wireless sensors.  45 sensors are densely deployed on the stand in the venue. The id and relative positions of sensors  are shown in device message. The sampling rate is adaptively adjusted according to the competition schedule.  The sampling interval is 5 minutes during the competition and 15 minutes otherwise. Please refer to  the description of the experimental setup in the data descriptor paper. In the process of data collection, the data cleaning process is performed  to remove  and calibrate outliers and abnormal trend data. The script of data cleaning algorithm is  provided in this repository. For details about the data cleaning process, please refer to the script in  this repository and data descriptor paper. The dataset in this repository is processed version. The raw dataset is not included in this repository. Data is stored as CSV file. Each device is numbered in order of placement. There are 45 sensors in total, 1 to 45 in the csv file are sensor numbers, timestamp as the China Standard Time (GMT+8), Each timestamp  corresponds to 45 CO2 concentration data from different sensors. To access the dataset, any programming language that can access the CSV file is appropriate. Users can  also directly open the CSV file. To successfully execute script files, Pycharm with Python 3.0 is required.

Figure 4 in A new water mite species of the genus Teutonia Koenike, 1889 from Corsica, France, based on morphological data and DNA barcodes (Acari, Hydrachnidia, Teutoniidae)

Figure 4 Teutonia corsicanasp. nov., ♂ [CCDB 38559 F09], Ruisseau de Battesta, France: A – coxal and genital field, partial view; B – photograph of ejaculatory complex; C – palp, medial view; D – gnathosoma. Scale bars = 100 μm.

Figure 2 in A new water mite species of the genus Teutonia Koenike, 1889 from Corsica, France, based on morphological data and DNA barcodes (Acari, Hydrachnidia, Teutoniidae)

Figure 2 Results of ASAP analysis for COI sequences. (A) Distribution of pairwise differences, (B) Ranked pairwise differences.

Figure 1 in A new water mite species of the genus Teutonia Koenike, 1889 from Corsica, France, based on morphological data and DNA barcodes (Acari, Hydrachnidia, Teutoniidae)

Figure 1 Neighbour-Joining tree of the genusTeutonia, obtained from 17 nucleotide COI sequences.and the results of species delimitation analyses. Values near branches show bootstrap support (BS). The results of species delimitation by ASAP procedure are indicated by vertical bars. Country codes (alpha-2 code): DE – Germany, FR – France, MN – Montenegro, NO – Norway, TR – Turkey.

Figure 3 in A new water mite species of the genus Teutonia Koenike, 1889 from Corsica, France, based on morphological data and DNA barcodes (Acari, Hydrachnidia, Teutoniidae)

Figure 3 Teutonia corsicanasp. nov. (A-B, D-G – holotype ♀, Ruisseau de Tuara, France; C – ♀ [CCDB 38559 D12], preserved specimen, Riviere La Solenzara, France): A – coxal and genital field; B, C – genital field; D – palp, medial view (P-1 lacking); E – palp, lateral view; F – I-L-5 and -6; G – IV-L-5 and -6. Scale bars = 100 μm.

Figure 5 in A new water mite species of the genus Teutonia Koenike, 1889 from Corsica, France, based on morphological data and DNA barcodes (Acari, Hydrachnidia, Teutoniidae)

Figure 5 Teutonia cometes(Koch, 1837), ♀, Danilovgrad, spring under the bridge over the Zeta river, Montenegro: A – genital field; B – palp, medial view. Scale bar = 100 μm.

Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.

Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT

Text-fig. 4. Juglandaceae Carya (a–w). Scale bars = 1 cm. a–d: USNM PAL 772352, reflected light, palladium coated. a: Obliquelateral view of nut, apex up. b: Basal view with damage to left and clear depiction of meridional grooves. c, d: Two lateral views oriented about 130° from each other and avoiding the area of damage; the meridional grooves clear in (c). e–l: USNM PAL 772350. e: Intact nut, lateral view, apex up, reflected light. f: One half of split nut revealing in situ chalcedony locule cast, reflected light. g–k: Virtual sections from micro-CT data. g: Longitudinal section parallel to the exposed face in (f). h: Longitudinal section at 90° from (g). i: Transverse section in apical 1/3 showing locule bracketed by C-shaped lacunae (arrows). j: Equatorial transverse section showing two lobes of the locule separated by primary septum, lacuna evident below as white line. k: Transverse section near base in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.

Text-fig. 4. Juglandaceae Carya (a–w). Scale bars = 1 cm. a–d: USNM PAL 772352, reflected light, palladium coated. a: Obliquelateral view of nut, apex up. b: Basal view with damage to left and clear depiction of meridional grooves. c, d: Two lateral views oriented about 130° from each other and avoiding the area of damage; the meridional grooves clear in (c). e–l: USNM PAL 772350. e: Intact nut, lateral view, apex up, reflected light. f: One half of split nut revealing in situ chalcedony locule cast, reflected light. g–k: Virtual sections from micro-CT data. g: Longitudinal section parallel to the exposed face in (f). h: Longitudinal section at 90° from (g). i: Transverse section in apical 1/3 showing locule bracketed by C-shaped lacunae (arrows). j: Equatorial transverse section showing two lobes of the locule separated by primary septum, lacuna evident below as white line. k: Transverse section near base