Find research datasets worth reusing

Fig. 20. Most parsimonious cladogram for the 58 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 20. Most parsimonious cladogram for the 58 myological characters (CI = 0.82, RI = 0.90). Numbers identify clades, which are discussed in the text and used in the apomorphy list (table 4).

Fig. 3 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 3. Dorsal view of facial muscles of Parascalops; superficial muscles on left, deeper muscles on right.

Fig. 6 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 6. Ventral view of neck muscles of Parascalops; superficial muscles on bottom, deeper muscles on top.

Fig. 8 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 8. Ventral view of chest muscles of Parascalops; superficial muscles on left, deeper muscles on right.

Fig. 21 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 21. Present continental distributions mapped on most parsimonious cladogram. Distributions from Corbet and Hill (1991).

Fig. 5 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 5. Lateral view of snout muscles of Parascalops. A, Superficial layer; B, middle layer; C, deep layer.

Fig. 18 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 18. Medial view of lower leg muscles of Parascalops. A, Superficial muscles; B, deeper muscles.

Fig. 17 in Comparative Myology of Moles and the Phylogeny of the Talpidae (Mammalia, Lipotyphla)

Fig. 17. Lateral view of lower leg muscles of Parascalops. A, Superficial muscles; B, deeper muscles.

opencc-by-4.0Jul 2018View details →

Comparing adaptive capacity index across scales: The case of Italy

Measuring adaptive capacity as a key component of vulnerability assessments has become one of the mostchallenging topics in the climate change adaptation context. Numerous approaches, methodologies and con-ceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assess-ments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless,they encompass various implications regarding scale specificity and the robustness issues embedded in thechoice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity indexdeveloped for Italy's regional and sub-regional administrative levels, as a part of the National Climate ChangeAdaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and tenindicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As aninnovative feature of our analysis, the sub-regional variability of the index feeds back into the regional levelassessment. The results show that composite indices estimated at higher administrative or statistical levels ne-glect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies.By considering the intra-regional variability, different patterns of adaptive capacity can be observed at regionallevel as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators thatreflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with dif-ferent compensability are preferable. Our results show that within-region variability can be better demonstratedby bottom-up aggregation methods.

opencc-by-4.0Dec 2016View details →

PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality

1. Orignal PERCEIVE survey data (STATA file) 2. description of survey questions, descriptive results (word file) 3. EU Deliverable document with descriptive analysis of survey questions   ***please cite the following when using the microdata: Bauhr, M., & Charron, N. (2020). The EU as a savior and a saint? Corruption and public support for redistribution. Journal of European Public Policy, 27(4), 509-527. https://www.tandfonline.com/doi/full/10.1080/13501763.2019.1578816

opencc-by-4.0Oct 2021View details →

Datasets and OpenLCA foreground data processes for the article: Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment

This data set contains the supplementary data sets (1-3) and exported foreground data processes from OpenLCA for the manuscript “Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment”, submitted to Nature Energy. This repository contains: <ul> <li>Supplementary data set 1: Ancillary calculations and numerical values for HT-Aq DAC</li> <li>Supplementary data set 2: Ancillary calculations and numerical values for TSA DAC</li> <li>Supplementary data set 3: Ancillary calculations and numerical values shown in plots and table 3</li> <li>Foreground data from OpenLCA. OpenLCA process model for different cases of HT-Aq DAC and TSA DAC. To re-run the LCA calculations, OpenLCA (freeware) and the Ecoinvent 3.5 database (license required) need to be installed on a standard desktop computer or laptop with at least 8 GB RAM.</li> </ul>

opencc-by-4.0Dec 2020View details →

"Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.

"Decision support systems halve fungicide  compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.https://doi.org/10.1038/s43247-021-00291-8 | www.nature.com/commsenv Dataset includes the results of 80 independent experiments reported in 22 articles and it  has a dimension of 329 rows x 42 columns. Further information in "Description Supplementary Data 1.pdf" file.  This dataset was assembled including also the data from the publication Agronomy 2020, 10(4), 560; https://doi.org/10.3390/agronomy10040560.

Figure 5 in Old lake versus young taxa: a comparative phylogeographic perspective on the evolution of Caspian Sea gastropods (Neritidae: Theodoxus )

Figure 5. EBSPs indicating population trends for the pooled Pontocaspian (blue) and southern Iranian (red) Theodoxus groups. The central line of each plot represents the median value and the shaded area indicates the 95% confidence interval. Note the EBSPs depict marginally different starting dates for each group when compared with the phylogeny. Importantly, however, there is a strong overlap of EBSP starting dates with the 95% HPDs established for the onset of intraspecific diversification in each group, as shown in the phylogeny (figure 3).

Figure 1 in Old lake versus young taxa: a comparative phylogeographic perspective on the evolution of Caspian Sea gastropods (Neritidae: Theodoxus )

Figure 1. Representative phenotypes of the Pontocaspian and southern Iranian Theodoxus species studied herein. Pontocaspian: (a,b) T. pallasi (UGSB 20712); (c,d) T. astrachanicus (UGSB 18130); (e,f) T. pallasi (UGSB 18091); (g,h) T. major (UGSB 20482); (i,j) T. major (UGSB 20496); (k,l) T. schultzii (UGSB 20791). Southern Iranian: (m,n) T. doriae (UGSB 21706); (o,p) T. pallidus (UGSB 22228). Scale bar, 1 mm.

Figure 4. Statistical haplotype networks for COI, 16S in Old lake versus young taxa: a comparative phylogeographic perspective on the evolution of Caspian Sea gastropods (Neritidae: Theodoxus )

Figure 4. Statistical haplotype networks for COI, 16S and ATPα sequence data for Pontocaspian and southern Iranian Theodoxus groups. The total number of sequences in each network is demarcated by 'n'. The circle sizes represent the relative frequency of sequences per haplotype. The number of site changes separating haplotypes is indicated by blank dots. Colours correspond to the sampling locations, as indicated in the key and in figure 2. Haplotype groupings are boxed and labelled according to the phylogroups determined through the dated phylogeny (I–VI; figure 3).

Figure 2 in Old lake versus young taxa: a comparative phylogeographic perspective on the evolution of Caspian Sea gastropods (Neritidae: Theodoxus )

Figure 2. Map depictingthelocationsof thesamplingsitesaround the Pontocaspiansystemandsouthern Iran. Coloursof dots correspond to the locations, as indicated in the key. Dashed lines encircle (I) the Pontocaspian and (II) the southern Iranian Theodoxus sampling localities. The size of the dots represents the sample size at each location (larger = 10 specimens; smaller = 5 specimens).

Figure 3 in Old lake versus young taxa: a comparative phylogeographic perspective on the evolution of Caspian Sea gastropods (Neritidae: Theodoxus )

Figure 3. Dated phylogeny of Pontocaspian and southern Iranian Theodoxus spp. constructed in BEAST based on COI, 16S and ATPα sequence data. Supported phylogroups of Pontocaspian and southern Iranian Theodoxus are labelled Ito VI. Node labels among these phylogroups and outgroup species denote divergence time in millions of years ago (Ma), with the 95% credibility interval given in parentheses and as grey bars for in-group taxa. Small red squares at nodes (with darkened node bars and, in some instances, dates) indicate significant posterior probabilities of divergence events. Parallel to each supported phylogroup, coloured bars indicate the localities and respective morphospecies of the included specimens as defined in the key on the left. Caspian Sea lake-level variations over the last 1.5 million years (relative to absolute sea level) and regional stratigraphy (following the 'short–Akchagylian' option) are adapted from Krijgsman et al. [1] (Khv., Khvalynian).

opencc-by-4.0Jul 2024View details →

Examples of applying a multivariate Wilson prior to comparative crystallography data

This folder contains four examples of merging crystallographic intensities with a bivariate prior: <ul> <li>time-resolved Laue crystallography of the photoactive yellow protein (pyp.zip)</li> <li>anomalous diffraction from serial XFEL crystallography of thermolysin (thermolysin_xfel.zip)</li> <li>anomalous diffraction from Laue crystallography of NaI-soaked lysozyme (lysozyme.zip)</li> <li>fragment screening monochromatic data of Nsp3 Mac1 (dfs.zip)</li> </ul> Additionally, we provide several auxilliary examples: <ul> <li>For PYP, an example where we set aside a test fraction to semi-independently optimize the double-Wilson r (pyp_test_fraction.zip)</li> <li>for lysozyme, two examples, one where we use Laue-DIALS instead of precognition (lysozyme-laue-dials.zip), and another where we set aside the first 90 images to semi-independently optimize the double-Wilson r (lysozyme_test_fraction.zip)</li> <li>For thermolysin, an example where we use a bivariate versus a univariate prior as the number of scaled images grows (thermolysin_xfel_frames_sweep.zip), and another where we set aside the first 395 images to semi-independently optimize the double-Wilson r (thermolysin_xfel_test_fraction.zip)</li> </ul> Finally, we provide zip files containing Careless repositories (careless_041.zip, careless_053.zip) and the <a href="https://github.com/Hekstra-Lab/dw">dw</a> repository containing Jupyter notebooks outlining the theory of the double-Wilson model (dw.zip). Every example includes scripts to run Careless as well as to analyze the outputs in order to reproduce the figures in the double-Wilson manuscript. For every example, there is a `README.md` that describes the contents of each example folder.

opencc-by-4.0Nov 2022View details →

Datasets of "Lipoxygenase (LOX) genes in angiosperms: a comparative genome-wide analysis"

This dataset contain protein subcellular localization results and FASTA files containing coding sequences (CDS) and protein sequences used for all phylogenetic analyses of the study " Lipoxygenase (LOX) genes in angiosperms: a comparative genome-wide analysis" (submitted).