Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,145
datasets available to search
ShareScore release 0.7.1
Dataset results
5,145 results for “CO₂”
Figure 4 in Co-occurrence of three Aristolochia-feeding Papilionids (Archon apollinus, Zerynthia polyxena and Zerynthia cerisy) in Greek Thrace
Figure 4. Results of model for eggs and larval records. (A) Interaction plot showing average egg batch sizes for individual butterfly species on individual species of Aristolochia plants; (B) box-plots (medians and quartiles) showing the amount of canopy closure (variable Trees10: see Material and methods) above Aristolochia plants bearing eggs of the respective butterflies; (C) numbers of larvae of the three studied butterfly species recorded during searches for larvae, note the unbalanced scale on the x-axis; (D) interaction plot showing average number of larvae of the three studied butterfly species in individual instars. For panels (A, C, D) dotted line, Archon apollinus; dashed line, Zerynthia cerisy; full line, Zerynthia polyxena.
Figure 2 in Co-occurrence of three Aristolochia-feeding Papilionids (Archon apollinus, Zerynthia polyxena and Zerynthia cerisy) in Greek Thrace
Figure 2. Adults of the studied butterflies: (A) Archon apollinus; (B) Zerynthia cerisy; (C) Zerynthia polyxena (the small inserts stand for host plant species used by the respective species at the study locality); and drawings of their Aristolochia host plants (D) Aristolochia pallida; (E) Aristolochia rotunda; (F) Aristolochia clematitis; (G) Aristolochia hirta; (H) Dissected subterranean Aristolochia hirta flower with A. apollinus first-instar larvae; (I) Habitat mosaic at the Greek Thrace study site, showing a field in the front, and scrub with open forest in the background; (J) Silk-woven Aristolochia hirta leaves with A. apollinus larvae.
Figure 3 in Co-occurrence of three Aristolochia-feeding Papilionids (Archon apollinus, Zerynthia polyxena and Zerynthia cerisy) in Greek Thrace
Figure 3. Estimates of the adult daily population sizes based on mark–recapture data: year 2010, when only data for Archon apollinus (most of flight period) and Zerynthia cerisy (late tail of flight period) allowed the estimation; year 2011, A. apollinus, Z. cerisy, Zerynthia polyxena. The error lines present standard errors of estimates, see Table 3 for model parameters.
Figure 4 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938
Figure 4. Effect of carbon dioxide (CO2)-driven seawater acidification on total number of nauplii (N = 3) produced by Tigriopus japonicus females over the duration of the experiment (median indicated with a bar; quartiles, minimum and maximum shown).
Figure 3 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938
Figure 3. Proportion of egg sacs that successfully produced nauplii (N = 3) at four pH levels (median indicated with a bar; quartiles, minimum and maximum also shown).
Figure 6 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938
Figure 6. Variation in number of nauplii [mean ± standard deviation (SD), N = 3] produced by Tigriopus japonicus females over successive broods at four pH levels.
Figure 2 in Influence of CO -induced seawater acidification on the development and lifetime reproduction of Tigriopus japonicus Mori, 1938
Figure 2. Effect of carbon dioxide (CO2)-induced seawater acidification on the number of broods (N = 3) produced by females of Tigriopus japonicus over the duration of the experiment (median indicated with a bar; quartiles, minimum and maximum also shown).
Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework
<p>Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"</p>
Accounting for environmental variation in co‐occurrence modelling reveals the importance of positive interactions in root‐associated fungal communities
<p>Understanding the role of interspecific interactions in shaping ecological communities is one of the central goals in community ecology. In fungal communities, measuring interspecific interactions directly is challenging because these communities are composed of large numbers of species, many of which are unculturable. An indirect way of assessing the role of interspecific interactions in determining community structure is to identify the species co-occurrences that are not constrained by the environmental conditions. In this study, we investigated co-occurrences among root-associated fungi, asking whether fungi co-occur more or less strongly than expected based on the environmental conditions and the host plant species examined. For this purpose, we generated molecular data on root-associated fungi of five plant species evenly sampled along an elevational gradient at a high Arctic site. We analysed the data using a joint species distribution modelling approach that allowed us to identify those co-occurrences that could be explained by the environmental conditions and the host plant species, as well as those co-occurrences that remained unexplained and thus more likely reflect interactive associations. Our results indicate that positive interactions play an important role in shaping microbial communities in arctic plant roots. In particular, we found that mycorrhizal fungi are especially prone to positively co-occur with other fungal species. Our results bring new understanding to the structure of arctic interaction networks by suggesting that interactions among root-associated fungi are predominantly positive.</p>
SmartUpLab- Co-Creation in sustainable mobility research - Systematic Reviews and Case Studies
<p>The current dataset presents the results of systematic reviews and case studies about co-creation tools best practice for sustainable mobility, carried out in the context of the research project SmartUpLab (funded by EFRE).</p>
Raw Data to "Density functional theory study of CO formation through reactions of polycyclic aromatic hydrocarbons with atomic oxygen (O(3P))"
<p>This data is a supplement to the publication <a href="https://doi.org/10.1016/j.fuel.2018.12.047">https://doi.org/10.1016/j.fuel.2018.12.047</a>. The data includes Turbomole input and output files. The calculations are performed using DFT/TPSSh-D3/TZVP method and Turbomole version 7.2. The equilibrium structures for the reactions of polyaromatics are named as following:<br> C<sub>X</sub>H<sub>Y</sub> (<strong>S1</strong>) + O -> C<sub>X</sub>H<sub>Y</sub>O (<strong>S2</strong>) -> C<sub>X</sub>H<sub>Y-1</sub>O (<strong>S3</strong>) + H (i) O addition and H abstraction<br> C<sub>X</sub>H<sub>Y-1</sub>O (<strong>S3</strong>) [-> <strong>S4</strong> -> <strong>S5</strong> ] -> CX-1HY-1 (<strong>S6</strong>) + CO (ii) Single, two, or three step CO elimination</p> <p>The transition state structures are named according to the naming of the corresponding reactant and product. For example, the transition state connecting the structure S3 to S5 is named as T35. Under some of the transition state directories, intrinsic reaction coordinate calculation output can be found under the directories named as "IRC".<br> <br> </p> <p><br> The LibreOffice Calc spreadsheet "SUPPINFO.ods" includes the activation and reaction energies to the reaction steps.</p>
Data and code for the manuscript: "Varying richness need not imply non-random species co-occurrence: implications for specifying null models"
<p>Data and R code for the manuscript "Varying richness need not imply non-random species co-occurrence: implications for specifying null models".</p>
Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome
<p><strong>The dataset from the article </strong><strong>Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome</strong></p>
Identification of co-infections in a cohort of patients diagnosed with Lyme Disease
<p>Serlogy test data used in the study: Identification of co-infections in a cohort of patients diagnosed with Lyme Disease</p>
On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects
<p>This is a replication package of our paper submission to the Saner 2021 entitled:</p> <p>On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects</p>
Data archive for the journal article: "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites"
<p>Data archive accompanying the peer-reviewed journal article "Comparison of co–located rBC and EC mass concentration measurements during field campaigns at several European sites". In January 2021 this article was accepted for publication in the journal <em>Atmospheric Measurement </em><em>Techniques</em>. Data are uploaded in the form of Igor 8.0 graphics source files (.pxp) and data exported to Excel spreadsheet (.xlsx).</p>
Data from: Discordant patterns of genetic and phenotypic differentiation in five grasshopper species co-distributed across a microreserve network
<p>Conservation plans can be greatly improved when information on the evolutionary and demographic consequences of habitat fragmentation is available for several co-distributed species. Here, we study spatial patterns of phenotypic and genetic variation among five grasshopper species that are co-distributed across a network of microreserves but show remarkable differences in dispersal-related morphology (body size and wing length), degree of habitat specialization and extent of fragmentation of their respective habitats in the study region. In particular, we tested the hypothesis that species with preferences for highly fragmented microhabitats show stronger genetic and phenotypic structure than co-distributed generalist taxa inhabiting a continuous matrix of suitable habitat. We also hypothesized a higher resemblance of spatial patterns of genetic and phenotypic variability among species that have experienced a higher degree of habitat fragmentation due to their more similar responses to the parallel large-scale destruction of their natural habitats. In partial agreement with our first hypothesis, we found that genetic structure, but not phenotypic differentiation, was higher in species linked to highly fragmented habitats. We did not find support for congruent patterns of phenotypic and genetic variability among any studied species, indicating that they show idiosyncratic evolutionary trajectories and distinctive demographic responses to habitat fragmentation across a common landscape. This suggests that conservation practices in networks of protected areas require detailed ecological and evolutionary information on target species in order to focus management efforts on those taxa that are more sensitive to the effects of habitat fragmentation.</p>
Archaeobotanical results from Caherdrinny 3, Co. Cork, Ireland
<p>Identified charred plant remains from a multi-period site in north Cork, Ireland saved as a .csv file.</p>
Plant remains (identifications) from Ballinglanna North 3, Co. Cork Ireland
<p>.csv file of identification and quantification of plant remains from Early Neolithic and Bronze Age site at Ballinglanna North 3 in north County Cork, Ireland.</p>
Satellite images of the 17 July 2016 Aru Co glacier collapse
<p>These satellite images were made to visualize the Aru Co glacier avalanche. Some of them were used in these blog posts:</p> <ul> <li>Séries Temporelles (2016, August 25) Sentinel-2A captures a giant ice avalanche in Tibet. http://www.cesbio.ups-tlse.fr/multitemp/?p=8294</li> <li>Séries Temporelles (2016, August 25) Sentinel-2A (and Landsat-8) capture a giant ice avalanche in Tibet http://www.cesbio.ups-tlse.fr/multitemp/?p=8327</li> </ul> <p>Files description:</p> <ul> <li>File 2016-07-21_S2.tif: Sentinel-2A image of the Aru Co glacier avalanche acquired on 21-Jul-2016 (4 days after the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File 2016-06-24_L8mos.tif: Landsat-8 image of the Aru Co area acquired on 24-Jun-2016 (23 days before the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File anim.gif: animated sequence of both images using the lowest resolution image (Landsat-8)</li> <li>File diff_S2minusL8_band3.tif: difference between the band 3 of the 2016-07-21 Sentinel-2A image and the 2016-06-24 Landsat-8 image after a nearest neighbour resampling of the Sentinel-2 image to the same resolution as the Landsat-8 image (30 m).</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The images was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-01_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 07-Jul-2016 (10 days before the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-21-01_S1_diff_smoothed_Lee.tif : difference between both Sentinel-1 images after applying a refined Lee filter on the radar intensities</li> </ul> <p>Spatial extent of all the images in WGS 84 UTM 44N and lon/lat coordinates :</p> <p>Upper Left ( 602260.000, 3777670.000) ( 82d 6'32.64"E, 34d 8' 5.67"N)<br> Lower Left ( 602260.000, 3755030.000) ( 82d 6'23.08"E, 33d55'50.74"N)<br> Upper Right ( 640720.000, 3777670.000) ( 82d31'33.86"E, 34d 7'49.56"N)<br> Lower Right ( 640720.000, 3755030.000) ( 82d31'20.72"E, 33d55'34.75"N)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.