Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Identifying the Fusarium species involved in foot rot disease of beans in the UK using a combined molecular and microbiological approach

Materials and methods Fungal isolation Isolates (113) were prepared from both soil and infected plant samples that were received from the Plant Clinic at the Processors and Growers Research Organisation (PGRO). The samples were from different regions of England, United Kingdom (Table 1). <a name="_Ref165642771"></a>Table 1: The locations and number of isolates obtained for infected faba bean and soil samples used in the study. * = includes soil isolates <table> <tbody> <tr> <td> Location </td> <td> Number of isolates obtained </td> <td> Month(s) </td> </tr> <tr> <td> PGRO experimental plots </td> <td> 29* (13 soil, 16 plant) </td> <td> November 2022 </td> </tr> <tr> <td> Cambridgeshire </td> <td> 2 </td> <td> June 2023 </td> </tr> <tr> <td> Oxfordshire </td> <td> 3 </td> <td> June-July 2023 </td> </tr> <tr> <td> Durham </td> <td> 3 </td> <td> July 2023 </td> </tr> <tr> <td> Shropshire </td> <td> 4 </td> <td> July and August2023 </td> </tr> <tr> <td> Lincolnshire </td> <td> 5 </td> <td> July and August 2023 </td> </tr> <tr> <td> Northumberland </td> <td> 5 </td> <td> July and August 2023 </td> </tr> <tr> <td> Staffordshire </td> <td> 4 </td> <td> July  and August 2023 </td> </tr> <tr> <td> Suffolk </td> <td> 4 </td> <td> July 2023 </td> </tr> <tr> <td> Leicestershire </td> <td> 2 </td> <td> July 2023 </td> </tr> <tr> <td> Essex </td> <td> 4 </td> <td> July 2023 </td> </tr> <tr> <td> Norfolk </td> <td> 34 </td> <td> August 2023 </td> </tr> <tr> <td> Yorkshire </td> <td> 6 </td> <td> September 2023 </td> </tr> <tr> <td> Hampshire </td> <td> 2 </td> <td> July 2023 </td> </tr> <tr> <td> Undisclosed PGRO locations </td> <td> 6 </td> <td> Undisclosed </td> </tr> </tbody> </table>   Infected plant samples were disinfected by placing pieces of infected stems/root in 10 % sodium hypochlorite solution for 5 min. The samples were rinsed twice using sterilised distilled water and placed on sterilised filter paper to be dried for 10 min at room temperature inside a laminar flow hood. The dried samples were moved to potato dextrose agar medium (PDA) inside 9 cm diameter plastic Petri dishes. Petri dishes were incubated at 22<a name="_Hlk134433128"></a> °C with 12 h fluorescent photoperiod, <a name="_Hlk165887610"></a>and light intensity of 20-25 µMol.m-2.s-1. Once colonies had formed, clonal isolates were prepared from the colonies as follows. Approximately 1 mm2 of the colony was collected using a flame-sterilised inoculation loop and was sequentially spread onto three Petri dishes containing 2 % water agar, to dilute the inoculum gradually. The Petri dishes were incubated for 24 h as described above. The third Petri dish for each isolate was examined under a stereoscope, and one separated hypha was transferred using a flame-sterilised scalpel to another Petri dish containing PDA medium, and incubated for seven days to provide a clonal isolate. The isolates were stored for future use using two methods, for routine or long-term storage. For routine storage (months), three discs of the PDA medium containing the clonal isolate were transferred to a 2 ml microcentrifuge tube containing 1 ml of sterilised distilled water, the tube sealed with parafilm and stored at -20 °C. For long-term storage (3-4 years) the clonal isolate was plated onto a Petri dish containing many pieces of 1 cm long sterilised filter paper on PDA medium, and the colony was allowed to grow for seven days to cover the filter paper. The pieces of filter paper were removed and placed inside an empty Petri dish and dried for seven days at room temperature. The filter paper pieces were then transferred to an empty 2 ml plastic microcentrifuge tube and stored at -20 °C. Koch`s postulates were confirmed for each of the isolates by re-isolation, inoculation, and identification.   Pathogenicity testing Pathogenicity testing was conducted using susceptible faba bean seedlings (cv. Lynx) grown in test tubes in a mixture of perlite/vermiculite. The growth media was prepared by adding one volume of vermiculite (the capacity of a 1000 ml plastic beaker) to one volume of perlite inside an autoclave bag; this was mixed to ensure equal distribution of each component, and 1 litre of distilled water was added. The autoclave bag was closed and autoclaved for 20 min at 121 °C, and the mixture was transferred to fill 2/3 of the test tubes (150 x 24 mm, 1.2 ml wall; borosilicate glass 150 x 24 mm, rimless, Appleton Woods Ltd), which were then sealed with cotton wool and aluminium foil, prior to being autoclaved. Seeds were soaked in sterilized distilled water overnight and placed in 10 % sodium hypochlorite for 5 min. The seeds were washed three times with sterilised distilled water and placed on sterilised filter paper until dry. The seeds were then transferred to 9 cm petri dishes containing 1.2 % Tap Water Agar (12 g agar in 1 l of tap water, autoclaved in a 2 l conical flask), where they were allowed to germinate for four days in the incubator at 24 °C before being transferred to test tubes containing the vermiculite/perlite mixture. Following transfer, the seedlings were allowed to grow for five to seven days until they were suitable for inoculation (4-5 cm root length). The seedlings were inoculated by placing a 10 mm block of PDA medium containing a ten-day old fungal culture against the stem base. A small piece of sterilised cotton was placed around the stem to ensure adequate moisture at the inoculation site. The inoculated seedlings were incubated at 24 °C, with a 12 h photoperiod and light intensity of 20-25 µMol.m-2.s-1. Disease severity was monitored daily from 5-6 days after inoculation. Root and stem infection were scored on days 15 and 25 using a 5-point scale (Figure 2): 0 = healthy roots, no discolouration. 1 = up to 20 % root or stem base discoloured. 2 = 20-40 % root or stem base discoloured. 3 = 40-60 % root or stem base discoloured. 4 = 60-80 % root or stem base discoloured, stunting of plant. 5 = total discoloration, dead plant. DNA extraction Clonal colonies were cultured on 50 ml of Potato Dextrose Broth (PDB) medium (FORMEDIUMTM) in a 250 ml flask and incubated for seven days on a rotary shaker (22 °C with 70 RPM). The mycelium for each isolate was harvested by transferring the contents of each flask into a 50 ml falcon tube and centrifuging at 5000 RPM for 10 min. The supernatant was discarded, and the mycelium pellet stored at -20 °C. Mycelium (1 ml) was transferred to a 2 ml safe-lock microcentrifuge tube. The tubes were covered with parafilm which was pierced to allow moisture to evaporate. Samples were freeze-dried (-45 °C, 0.133 mbar) for 48 h. Freeze-dried mycelium (15 mg) was transferred to a 2 ml safe lock microcentrifuge tube containing two carbon steel ball bearings (3 mm diameter, grade 1000, SimplyBearings). The mycelium was homogenised for 4 min using a TissueLyser (Retsch MM400; 30 RPS), after which 120 µl of TNES buffer was added and the samples were homogenised again as before. Total DNA was extracted from the samples following the BOMB-Bio nucleic acid tissue DNA extraction protocol (Oberacker et al., 2019). The samples were incubated at 55 °C overnight after adding 2 µl of proteinase K and 3 µl of RNAase A. Following incubation, 240 µl of GITC lysis buffer was added, mixed and samples incubated at room temperature for 5 min. Isopropanol (480 µl) was added and, following centrifugation (5000 RPM; 5 min), 650 µl was transferred to a new tube with 200 µl of 1X BOMB-Bio magnetic bead solution (1:50 carboxylated SeraMag Speed Beads magnetic beads in TE) and mixed. The solution was placed on a magnetic rack to hold the beads with DNA bound to them in place whilst the supernatant was removed. The beads with bound DNA were washed once with isopropanol (400 µl) and twice with 80 % ethanol (400 µl). The solution was removed from the magnet, beads were allowed to dry briefly and 70 µl of nuclease free water was added to elute the DNA. The beads were removed by placing the samples back on the magnetic rack and transferring the supernatant to a new tube. The extracted DNA concentration was estimated using a Qubit 4 and 1X dsDNA High Sensitivity (HS) Assay Kit (Invitrogen) according to the manufacturer’s instructions. Polymerase Chain Reaction (PCR) DNA was amplified using PCR with three sets of primers: Internal Transcribed Spacer (ITS) primers ITS1/ITS4 (Raja et al., 2017) and two sets of Translation Elongation Factor one α primers, 1018F/1620R and EF1/EF2 (O’Donnell et al., 1998; Raja et al., 2017). Prior to PCR, the template DNA concentration was adjusted to<a name="_Hlk133674492"></a> 5 ng/μl using molecular grade water. The PCR mix <a name="_Hlk133674557"></a>contained: 12.5 µl of 2x MyTaq Red Mix (Meridian Bioscience), 3 µl of template DNA, 1 µl of each primer (10 µM) and 7.5 µl of nuclease free water to a final volume of 25 µl. The cycling conditions and sequence of each primer are given in Table 2. The PCR products were separated alongside a 1 kbp ladder (GeneRuler 1 kb Plus DNA Ladder Thermo scientific SM1331) using gel electrophoresis in a 1 % agarose gel in TAE buffer stained with GelRed® nucleic acid stain (Sigma Aldrich). Gels were visualised with a UV transilluminator (BioRad) to confirm successful amplification. <a name="_Ref162427061"></a>Table 2: The ITS (ITS1 and ITS4) and TEF1α (1018F, 1620R, EF1 and EF2) primer sequences and PCR conditions. <table> <tbody> <tr> <td> Primer </td> <td> Sequence (5’-3’) </td> <td> Initial Melt </td> <td> Melt </td> <td> Anneal </td> <td> Extension </td> <td> Final extension </td> </tr> <tr> <td> ITS1: ITS4:  (Raja et al., 2017) </td> <td> CGTAGGTGAACCTGCGG TCCTCCGCTTATTGATATGC </td> <td> 94 °C 5 min </td> <td> 94°C 30 sec </td> <td> 55 °C 1 min </td> <td> 72 °C 2 min </td> <td> 72 °C 7 min </td> </tr> <tr> <td> 35 cycles </td> </tr> <tr> <td> 1018F: 1620R: (O’Donnell et al., 1998; Raja et al., 2017) </td> <td> GAYTTCATCAAGAACATGAT GACGTTGAADCCRACRTTGTC </td> <td> 94 °C 5 min   </td> <td> 94 °C 30 sec </td> <td> Touch down 66-56 °C 1 min   </td> <td> 72 °C 1 min </td> <td> 72 °C 10 min </td> </tr> <tr> <td> 9 cycles </td> </tr> <tr> <td> 94 °C 30 sec </td> <td> 56 °C 1 min   </td> <td> 72 °C 1 min </td> </tr> <tr> <td> Remaining 26 cycles </td> </tr> <tr> <td> EF1: EF2: (O’Donnell et al., 1998) (Raja et al., 2017) </td> <td> ATGGGTAAGGARGACAAGAC GGARGTACCAGT SATCATGTT   </td> <td> 95 °C 2 min </td> <td> 95 °C 30 sec </td> <td> 54.1 °C 1 min </td> <td> 72 °C 1 min </td> <td> 72 °C 5 min </td> </tr> <tr> <td> 35 cycles </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>   Prior to sequencing, the PCR products were purified using solid-phase reversible immobilisation (SPRI) magnetic beads. To each PCR product, 1 X SPRI beads were added at a 1:2 ratio of PCR product to bead solution (50 µl PCR product to 100 µl SPRI beads) and the mixture incubated for 5 min at room temperature. The tubes were transferred to the magnetic plate for 5 min until the solution was clear, at which point the supernatant was discarded and the beads were washed twice with 80% ethanol for 60 s each time. Ethanol was removed and the beads held on the magnetic plate were allowed to dry for 5-10 min at room temperature. The tubes containing the dry beads were removed from the magnetic plate, and 50 µl of molecular grade nuclease free water was added to each tube, mixed and incubated for 5 min, allowing the DNA to elute. The tubes were placed back on the magnetic plate for 5 min until the solution was clear, at which point the supernatant was transferred to a new 1.5 ml microcentrifuge tube. Sanger sequencing was carried out using ITS1, ITS4 primers and EF1, EF2 primers. All sequencing was carried out by Eurofins Genomics. Following sequencing, the chromatograms obtained were trimmed and analysed using Geneious Prime (version 2023.0.4). First, low-quality bases (e.g., overlapping peaks) were trimmed from each end and the sequence upstream from that site, including the primer sequence, was deleted. The forward and reverse sequence for each sample were assembled using the de novo assemble function in Geneious at the highest sensitivity to create a consensus sequence (highest threshold quality: 60%). A basic local alignment search tool (BLAST) search was carried out for the consensus sequences using the NCBI-NR database for the ITS sequences, and, for the TEF1α sequences, the data available on the Fusarium ID database (Torres-Cruz et al., 2022). All of the TEF1α consensus sequences from all samples were subsequently aligned with the available sequences on the Fusarium ID database (Geneious global alignment with free end gaps, 65% similarity), and a phylogenetic tree was generated (genetic distance model Tamura-Nei, neighbour joining method, bootstrap with 100 replicates).

Figure 5 in Combined-data phylogenetics and character evolution of Clitellata (Annelida) using 18S rDNA and morphology

Figure 5. Phylogenetic tree obtained from one of the three replicate Bayesian inference runs of the combined (18S rDNA, somatic, and spermatozoal) data set. Posterior probabilities ± 0.85 are indicated in front of the nodes.

Figure 4 in Combined-data phylogenetics and character evolution of Clitellata (Annelida) using 18S rDNA and morphology

Figure 4. Parsimony consensus tree of the combined (18S rDNA, somatic, and spermatozoal) data set. Bootstrap frequencies ± 50% are indicated above the branches.

Figure 2 in Combined-data phylogenetics and character evolution of Clitellata (Annelida) using 18S rDNA and morphology

Figure 2. Phylogenetic tree obtained from one of the three replicate Bayesian inference runs of the 18S rDNA sequences. Posterior probabilities ± 0.85 are indicated in front of the nodes.

Figure 1 in Combined-data phylogenetics and character evolution of Clitellata (Annelida) using 18S rDNA and morphology

Figure 1. Schematic representation of some of the 34 considered spermatozoal characters. Inset, hypothetical plesiomorphic spermatozoon for the Clitellata, as inferred from ancestral-state reconstruction analysis (modified from Jamieson et al., 1987).

opencc-zeroJan 2023View details →

dryad40/100

Data for: Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy

Understanding how species respond to human activities is paramount to ecology and conservation science, one outstanding question being how large-scale patterns in land use affect biodiversity. To facilitate answering this question, we propose a novel analytical framework that combines Environmental Niche Models, multi-grain analyses, and species traits. We illustrate the framework capitalizing on the most extensive dataset compiled to date for the butterflies of Italy (106,514 observations for 288 species), assessing how agriculture and urbanization have affected biodiversity of these taxa from landscape to regional scales (3–48 km grains) across the country while accounting for its steep climatic gradients. Multiple lines of evidence suggest pervasive and scale-dependent effects of land use on butterflies in Italy. While land use explained patterns in species richness primarily at grains ≤ 12 km, idiosyncratic responses in species highlighted "winners" and "losers" across human-dominated regions. Detrimental effects of agriculture and urbanization emerged from landscape (3-km grain) to regional (48-km grain) scales, disproportionally affecting small butterflies and butterflies with a short flight curve. Human activities have therefore reorganized the biogeography of Italian butterflies, filtering out species with poor dispersal capacity and narrow niche breadth not only from local assemblages but also from regional species pools. These results suggest that global conservation efforts neglecting large-scale patterns in land use risk falling short of their goals, even for taxa typically assumed to persist in small natural areas (e.g., invertebrates). Our study also confirms that consideration of spatial scales will be crucial to implementing effective conservation actions in the Post-2020 Global Biodiversity Framework. In this context, applications of the proposed analytical framework have broad potential to identify which mechanisms underlie biodiversity change at different spatial scales. Funding statement: FR is supported by the PROBAE project "Protect butterflies across Europe through climate refugia" funded by the European Commission through Horizon 2020, Marie Skłodowska-Curie Actions (MSCA) individual fellowship, reintegration panel (Grant agreement ID: 101024579). Open Access Funding provided by Universita degli Studi di Torino within the CRUI-CARE Agreement.

opencc-by-4.0Nov 2021View details →

Green and Controllable Preparation of Cu/Zn Alloys Using Combined Electrodeposition and Redox Replacement

Dataset of journal paper  Green and Controllable Preparation of Cu/Zn Alloys Using Combined Electrodeposition and Redox Replacement

ClinicalTrials.gov40/100

Use of the Combination of Olmesartan and Hydrochlorothiazide in Essential Hypertension

ClinicalTrials.gov study NCT00430508. IPD Sharing: YES. Countries: 7. Publications: 1.

controlledIPD-YESFeb 2026View details →

dryad40/100

Data for: Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy

Open the record for dataset details and reuse information.

publicJan 2023View details →

opencc-by-nc-4.0Nov 2015View details →

Supporting datasets PubFig05 for: "Heterogeneous Ensemble Combination Search using Genetic Algorithm for Class Imbalanced Data Classification"

Faces Dataset: PubFig05 This is a subset of the ''PubFig83'' dataset [1] which provides 100 images each of 5 most difficult celebrities to recognise (referred as class in the classification problem). For each celebrity persons, we took 100 images and separated them into training and testing sets of 90 and 10 images, respectively: Person: Jenifer Lopez; Katherine Heigl; Scarlett Johansson; Mariah Carey; Jessica Alba   Feature Extraction To extract features from images, we have applied the HT-L3-model as described in [2] and obtained 25600 features. Feature Selection Details about feature selection followed in brief as follows: <ol> <li> Entropy Filtering: First we apply an implementation of Fayyad and Irani's [3] entropy base heuristic to discretise the dataset and discarded features using the minimum description length (MDL) principle and only 4878 passed this entropy based filtering method. </li> <li> Class-Distribution Balancing: Next, we have converted the dataset to binary-class problem by separating into 5 binary-class datasets using one-vs-all setup. Hence, these datasets became imbalanced at a ratio of 1:4. Then we converted them into balanced binary-class datasets using random sub-sampled method. Further processing of the dataset has been described in the paper. </li> <li> (alpha,beta)-k Feature selection: To get a good feature set for training the classifier, we select the features using the approach based on the (alpha,beta)-k feature selection [4] problem. It selects a minimum subset of features that maximise both within class similarity and dissimilarity in different classes. We applied the entropy filtering and (alpha,beta)-k feature subset selection methods in three ways and obtained different numbers of features (in the Table below) after consolidating them into binary class dataset. </li> </ol> <ul> <li> UAB: We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the union of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features. </li> <li> IAB: We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the intersection of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features. </li> <li> UEAB: We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets. Then, we applied the entropy filtering and (alpha,beta)-k feature set selection method on each of the balanced binary-class datasets. Finally, we took the union of selected features for each balanced binary-class datasets and get a set of features. </li> </ul> All of these datasets are inside the compressed folder. It also contains the document describing the process detail.   References [1] Pinto, N., Stone, Z., Zickler, T., & Cox, D. (2011). Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on (pp. 35–42). [2] Cox, D., & Pinto, N. (2011). Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 8–15). [3] Fayyad, U. M., & Irani, K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In International Joint Conference on Artificial Intelligence (pp. 1022–1029). [4] Berretta, R., Mendes, A., & Moscato, P. (2005). Integer programming models and algorithms for molecular classification of cancer from microarray data. In Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 (pp. 361–370). 1082201: Australian Computer Society, Inc.

opencc-by-4.0Jan 2023View details →

Overcoming clinical resistance to EZH2 inhibition using rational epigenetic combination therapy

Supplementary data for Kazansky et al, "Overcoming clinical resistance to EZH2 inhibition using rational epigenetic combination therapy" and "Epigenetic targeting of PGBD5-dependent DNA damage in SMARCB1-deficient sarcomas." Raw RNA-seq data from G401 cells can be found at the Gene Expression Omnibus (GEO) repository, accession number GSE213845. RNA-seq data from patient tumor samples has been deposited to the Database of Genotypes and Phenotypes (dbGaP), accession number phs003188.v1.p1. All files are labeled with their corresponding figures. "DESeq_processing_FINAL.R" was used for analysis of patient RNA-seq data, using "20230131_SampleTable.csv" and all "*_htseq.txt" files as input. "Tumor_growth_analysis_FINAL_UPDATED" was used for analysis of tumor growth kinetics using the aucVardiTest function. This was used for both of the manuscripts desscribed above.

opencc-by-4.0Dec 2020View details →

Data assimilation products by using multiple climate model simulations and different combinations of proxies

This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000. Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies. The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science) [Data structure] X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151) Z(1): analyses Z(2): priors

opencc-by-4.0Feb 2022View details →

Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"

This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled: "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by  Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin, The README.txt describes the data sets in detail. The associated software can be found at https://github.com/ruppinlab/madhitter

opencc-by-4.0Dec 2021View details →

The scale of a Martian hydrothermal system explored using combined neutron and X-ray tomography

Neutron and X-ray tomography data of MIL 03346,230 and MIL 03346,231   Neutron data was acquired at the Institut Laue-Langevin, https://doi.ill.fr/10.5291/ILL-DATA.UGA-480 79  X-ray data was acquired at the 4D Imaging Lab at Lund University, Sweden

dryad36/100

comspat: an R package to analyze within-community spatial organization using species combinations

The diversity of species combinations observable in sampling units reflects a species' uneven distribution and preference for specific abiotic and biotic conditions – a phenomenon most commonly expressed in terms of ecological assembly rules of plant communities and other sessile organisms (e.g., subtidal algae, invertebrates and coral reefs). We present comspat, a new R package that uses grid or transect data sets to measure the number of realized (observed) species combinations (NRC) and the Shannon diversity of realized species combinations (compositional diversity; CD) as a function of spatial scale. NRC and CD represent two measures from a model family developed by Pál Juhász-Nagy based on Information Theory. Classical Shannon diversity measures biodiversity based on the number and relative abundance of species, whereas the specific version of Shannon diversity presented here characterizes biodiversity and provides information on species coexistence relationships; both measures operate at fine-scale within the sampling unit or within the community. comspat offers two commonly applied null models, complete spatial randomness and random shift, to disentangle the textural, intraspecific, and interspecific effects on the observed spatial patterns. Combined, these models assist users in detecting and interpreting spatial associations and inferring assembly mechanisms. Our open-sourced package provides a vignette that describes the method and reproduces the figures from this paper to help users contextualize and apply functions to their data.

opencc-zeroApr 2022View details →

opencc-by-4.0Jul 2021View details →

Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM

The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.

opencc-by-4.0Mar 2022View details →

Supporting Data for: Resource requirements for ecosystem conservation: A combined industrial and natural ecology approach to quantifying natural capital use in nature

Data used to derive allometric equations for land area use by mammals, birds, reptiles, and insects, and data for the analysis of natural resource use at the Natural Capital Laboratory site.

dryad36/100

Data from: Combined analysis of extant Rhynchonellida (Brachiopoda) using morphological and molecular data

Independent molecular and morphological phylogenetic analyses have often produced discordant results for certain groups which, for fossil-rich groups, raises the possibility that morphological data might mislead in those groups for which we depend upon morphology the most. Rhynchonellide brachiopods, with more than 500 extinct genera but only 19 extant genera represented today, provide an opportunity to explore the factors that produce contentious phylogenetic signal across datasets, as previous phylogenetic hypotheses generated from molecular sequence data bear little agreement with those constructed using morphological characters. Using a revised matrix of 66 morphological characters, and published ribosomal DNA sequences, we performed a series of combined phylogenetic analyses to identify conflicting phylogenetic signals. We completed a series of parsimony-based and Bayesian analyses, varying the data used, the taxa included, and the models used in the Bayesian analyses. We also performed simulation-based sensitivity analyses to assess whether the small size of the morphological data partition relative to the molecular data influenced the results of the combined analyses. In order to compare and contrast a large number of phylogenetic analyses and their resulting summary trees, we developed a measure for the incongruence between two topologies, and simultaneously ignore any differences in phylogenetic resolution. Phylogenetic hypotheses generated using only morphological characters differed amongst each other, and with previous analyses, while molecular-only and combined Bayesian analyses produced extremely similar topologies. Characters historically associated with traditional classification in the Rhynchonellida have very low consistency indices on the topology preferred by the combined Bayesian analyses. Overall, this casts doubt on the use of morphological systematics to resolve relationships among the crown rhynchonellide brachiopods. However, expanding our dataset to a larger number of extinct taxa with intermediate morphologies is necessary to exclude the possibility that the morphology of extant taxa is not dominated by convergence along long branches.

opencc-zeroDec 2016View details →

opencc-by-4.0Oct 2022View details →

Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin

This data includes inputs and outputs of a coupled carbon-nitrogen river corridor model (RCM) for the Columbia River Basin and sensitivity results with varying substrate concentrations, and random forest model results with key model inputs/watershed/stream variables. The detailed descriptions of the individual files are included in the readme.txt file.