Find research datasets worth reusing

Fig. 4 in Phylogeny, classification and biogeography of Philotheca sect. Erionema (Rutaceae) based on nrDNA sequences

Fig. 4. Examples of leaf variation within the two nrDNA groups in Philotheca myoporoides subsp. myoporoides. Drawn from representative leaves of samples used in this study. NP, National Park; SF, State Forest.

Fig. 3 in Phylogeny, classification and biogeography of Philotheca sect. Erionema (Rutaceae) based on nrDNA sequences

Fig. 3. Distribution of Philotheca myoporoides subsp. myoporoides, including the distribution of samples used in this study (filled circles, with locality names given) and the nrDNA groups these belong to. Grey shading shows the distribution of specimens that are morphologically assigned to the 'southern' nrDNA group; samples from other areas are assigned here to the 'northern' group. Map is based on records in the Australasian Virtual Herbarium and our examination of specimens from MEL, NSW and CANB (primarily in terms of leaf shape, leaf size and habitat). NP, National Park; SF, State Forest.

Fig. 1 in Phylogeny, classification and biogeography of Philotheca sect. Erionema (Rutaceae) based on nrDNA sequences

Fig. 1. Distributions of taxa of Philotheca sect. Erionema. Maps are based on those of Wilson (2013) and Rozefelds (2001a, 2001b) plus our examination of herbarium material. Note that infraspecific taxa for P. buxifolia, P. brucei and P. scabra are not mapped separately.

Fig. 2 in Phylogeny, classification and biogeography of Philotheca sect. Erionema (Rutaceae) based on nrDNA sequences

Fig. 2. Majority rule consensus tree based on BI analysis of combined ITS and ETS dataset, showing mean branch lengths. Bayesian posterior probabilities are shown (either first, or above branches) along with MP bootstrap values (either second or below branches; only shown where bootstrap support is>50%). Dashed branches were not present on the MP strict consensus tree.

opencc-zeroApr 2024View details →

dryad32/100

Data from: Estimating bloodstain age in the short term based on DNA fragment length using nanopore sequencer

We used a nanopore sequencer to quantify DNA fragments > 10,000 bp in size and then evaluated their relationship with short-term bloodstain age. Moreover, DNA degradation was investigated after bloodstains were wetted once with water. Bloodstain samples on cotton gauze were stored at room temperature and low humidity for up to 6 months. Bloodstains stored for 1 day were wetted with nuclease-free water, allowed to dry, and stored at room temperature and low humidity for up to 1 week. The proportion of fragments > 20,000 bp in dry bloodstains tended to decrease over time, particularly for fragments > 50,000 bp in size. This trend was modeled using a power approximation curve, with the highest R2 value (0.6475) noted for fragments > 50,000 bp in size; lower values were recorded for shorter fragments. The proportion of longer fragments was significantly reduced in bloodstains that were dried after being wetted once, and there was significant difference in fragments > 50,000 bp between dry conditions and once-wetted. This result suggests that even temporary exposure to water causes significant DNA fragmentation, but not extensive degradation. Thus, bloodstains that appear fresh but have a low proportion of long DNA fragments may have been wetted previously. Our results indicate that evaluating the proportion of long DNA fragments yields information on both bloodstain age and the environment in which they were stored.

opencc-by-4.0Apr 2024View details →

Inlist files for "Detectability of axisymmetric magnetic fields from the core to the surface of oscillating post-main sequence stars"

Concerned article: "Detectability of axisymmetric magnetic fields from the core to the surface of oscillating post-main sequence stars" by Bhattacharya et al. (submitted). Corresponding author: Shatanik Bhattacharya Inlists for the proof-of-concept stellar models used in this project have been provided here for reproducibility. For the red-giant model, the inlist was executed with MESA version r22.05.1 and MESA-SDK version x86 64-linux-22.6.1. Model 500 (age 4.056 Gyr) was used as the RG in this project. For the sub-giant models, the inlist was executed with MESA version r23.05.1 and MESA-SDK version x86 64-linux-22.6.1. Models 345 (age 3.624 Gyr) and 350 (age 3.702 Gyr) were used as the MSG and LSG models respectively.

dryad32/100

Data from: Demographic inference from whole-genome and RAD sequencing data suggests alternating human impacts on goose populations since the last ice age

We investigated how population changes and fluctuations in the pink-footed goose might have been affected by climatic and anthropogenic factors. First, genomic data confirmed the existence of two separate populations: western (Iceland) and eastern (Svalbard/Denmark). Second, emographic inference suggests that the species survived the last glacial period as a single ancestral population with a low population size (100-1,000 individuals) that split into the current populations at the end of the Last Glacial Maximum with Iceland being the most plausible glacial refuge. While population changes during the last glaciation were clearly environmental, we hypothesize that more recent demographic changes are human-related: (1) the inferred population increase in the Neolithic is due to deforestation to establish new lands for agriculture, increasing available habitat for pink-footed geese (2) the decline inferred during the Middle Ages is due to human persecution and (3) improved protection explains the increasing demographic trends during the 20th century. Our results suggest both environmental (during glacial cycles) and anthropogenic effects (more recent) can be a threat to species survival.

opencc-zeroDec 2016View details →

opencc-by-4.0Apr 2024View details →

Identification of a novel non-coding deletion in Allan-Herndon-Dudley syndrome by long-read HiFi genome sequencing

These are the VCF files of structural variant (SV) calls for two sibling patients (II:2 [GMPB009_1] and II:3 [GMPB009_4]) generated by PacBio HiFi long-read genome sequencing. Sequence reads were processed using the <a href="https://github.com/PacificBiosciences/pb-human-wgs-workflow-snakemake">PacBio Human WGS workflow</a> with the human reference genome (hg38), and SVs were identified using '<a href="https://github.com/PacificBiosciences/svpack">svpack</a>'.

opennotspecifiedAug 2022View details →

Fig. 3 in Sequence capture data support the taxonomy of Pogonolepis (Asteraceae: Gnaphalieae) and show unexpected genetic structure

Fig. 3. Likelihood phylogeny of concatenated supermatrix of Pogonolepis and outgroups. Numbers above branches indicate UltraFast Bootstrap values, gene Concordance Factors, and site Concordance Factors. The dashed branch was shortened for the figure. Western Australian specimens of P. muelleriana are marked with (WA), all others are from eastern states. A and B indicate informally named clades inside P. stricta.

opennotspecifiedAug 2022View details →

Fig. 2 in Sequence capture data support the taxonomy of Pogonolepis (Asteraceae: Gnaphalieae) and show unexpected genetic structure

Fig. 2. Geographic spread of the specimens of Pogonolepis at CANB sampled for molecular analysis (large, pale circles) and ranges of species according to the Australasian Virtual Herbarium (dots; see https://doi.org/10.26197/ala.6556e234- 7160-49de-bf63-5123af624e94, accessed 31 May 2022). Red: P. muelleriana; blue: P. stricta. Note that specimens of P. muel-leriana geocoded in Canberra and Hobart were likely cultivated.

opennotspecifiedAug 2022View details →

Fig. 1 in Sequence capture data support the taxonomy of Pogonolepis (Asteraceae: Gnaphalieae) and show unexpected genetic structure

Fig. 1. (a) Pogonolepis muelleriana, New South Wales, SchmidtLebuhn 1633 (CANB); (b) Pogonolepis stricta, Western Australia, Schmidt-Lebuhn 1474 (CANB).

opencc-by-4.0Nov 2023View details →

SeQUeNCe Parallel Data

This is the SeQUeNCe data used in preparation of our parallel simulation publication. The corresponding preprint may be found on <a href="https://doi.org/10.48550/arXiv.2111.03918">arXiv</a>.

opencc-by-4.0Apr 2024View details →

BrGMGT peak areas, concentrations and %brGMGT from DeepCHALLA core sequence (Lake Chala, Kenya/Tanzania)

Open the record for dataset details and reuse information.

Figure 4 in Genetic diversity of Undaria pinnatifida populations from China and their genetic relationship with those from Įapan and Korea as revealed by mitochondrial and nuclear DNA sequences

Figure 4: Maximum likelihood phylogenetic tree inferred from the alignment of ITS1 sequences. Support values are shown as in Figure 3. The ribotypes detected in the Chinese samples in the present study are indicated with bold italicized fonts. Alaria esculenta was used as an outgroup to root the tree. The branch length is proportional to the sequence divergence indicated by the scale bar (substitutions per site).

Figure 3 in Genetic diversity of Undaria pinnatifida populations from China and their genetic relationship with those from Įapan and Korea as revealed by mitochondrial and nuclear DNA sequences

Figure 3: Maximum likelihood phylogenetic tree inferred from the alignment of the combined cox3 and tatC–tLeu sequences. Bootstrap values and Bayesian posterior probabilities>50% are shown, and "-" indicates a value <50%. The branch length is proportional to the sequence divergence indicated by the scale bar (substitutions per site). Refer to Uwai et al. (2006a) for explanation of the haplotype names and classification of the clades I to IV. The haplotypes detected in the Chinese samples in the present study are indicated with bold italicized fonts. Lessoniopsis littoralis was used as an outgroup to root the tree.

Figure 2 in Genetic diversity of Undaria pinnatifida populations from China and their genetic relationship with those from Įapan and Korea as revealed by mitochondrial and nuclear DNA sequences

Figure 2: Geographic distribution of haplotypes in natural and farmed populations of Undaria pinnatifida from China (A) and statistical parsimony network (B) of ITS1 sequences. The color areas in the pie charts are proportional to the ribotype frequency in the map. Small circles indicate undetected ribotypes. Each line connecting ribotypes represents one base mutation. The ribotypes detected in the Chinese samples in the present study are indicated in the ribotype network by the same colors as those in the map.

Figure 1 in Genetic diversity of Undaria pinnatifida populations from China and their genetic relationship with those from Įapan and Korea as revealed by mitochondrial and nuclear DNA sequences

Figure 1: Geographic distribution of haplotypes in natural and farmed populations of Undaria pinnatifida from China (A) and statistical parsimony network (B) of the combined cox3 and tatC–tLeu sequences. The color areas in the pie charts are proportional to the haplotype frequency in the map. Refer to Uwai et al. (2006a) and Table 3 for explanation of the haplotype names and classification of the clades I to IV, which are enclosed by boxes with lines of different patterns. Small circles indicate undetected haplotypes. Each line connecting haplotypes represents one base mutation. The haplotypes detected in the Chinese samples in the present study are indicated in the haplotype network with the same colors as those in the map.

opencc-by-4.0Nov 2024View details →

Dataset for Systematic Review on Cross-Disciplinary Teaching Sequences in Biology and Geology

This dataset supports a systematic review evaluating the effectiveness of cross-disciplinary and chronological teaching sequences in secondary education, focusing on biology and geology. The dataset is divided into two main sheets: <ol> <li>GENERAL: Contains metadata for 82 studies, including study titles, authors, publication years, sources, search criteria, and inclusion/exclusion criteria. This sheet also includes notes on study focus and relevance, as well as DOIs for traceability.</li> <li>DATA EXTRACTION: Provides detailed information extracted from selected studies, such as objectives, methodologies, participants, interventions, comparators, outcomes, and results. Additionally, it includes quality assessments and notes to contextualize the findings.</li> </ol> Use Case: This dataset is intended for researchers and educators in science education to explore the impact of interdisciplinary teaching methods on learning outcomes, critical thinking, and student engagement in STEM fields. Format: The dataset is provided in Excel format (.xlsx) and is structured for easy access and reproducibility.

restrictedcc-by-sa-4.0Nov 2024View details →

RNA sequencing data from the prefrontal cortex and hippocampus of male (12 weeks old) hemizyguous CAG-HERV-W-env mice and wild-type controls

RNA sequencing data from the prefrontal cortex (PFC) and hippocampus (HIPP) of male (12 weeks old) hemizyguous CAGHERV-Wenv mice ( C57BL6/J;129P2/Ola-Hprt mice; n = 3) relative to wild-type ( n = 3) littermates. Total RNA was extracted from prefrontal and hippocampal samples using the SPLIT RNA extraction kit (Lexogen, Austria) following the manufacturer’s recommendations and was sent to the Functional Genomics Center in Zurich (FGCZ) for quality control and RNA sequencing. The quality of the isolated RNA was determined with a Fragment Analyzer (Agilent, Santa Clara, California, USA). Only those samples with a 260 nm/280 nm ratio between 1.8–2.1, a 28S/18S ratio within 1.5–2, and RIN (>8) values qualified for a Poly-A enrichment strategy in order to generate the sequencing libraries applying the TruSeq mRNA Stranded Library Prep Kit (Illumina, Inc, California, USA). After Poly-A selection using Oligo-dT beads the mRNA was reverse-transcribed into cDNA. The cDNA was fragmented, end-repaired and poly-adenylated before ligation of TruSeq UD Indices (IDT, Coralville, Iowa, USA). The quality and quantity of the amplified sequencing libraries were validated using a Fragment Analyzer SS NGS Fragment Kit (1–6000 bp) (Agilent, Waldbronn, Germany). The equimolar pool of the samples was spiked into a NovaSeq6000 run targeting ~15M reads per sample on a S1 FlowCell (Novaseq S1 Reagent Kit, 100 cycles, Illumina, Inc, California, USA). Reads were quality-checked with FastQC. Sequencing adapters were removed with Trimmomatic and aligned to the reference genome and transcriptome of Mus Musculus (GENCODE, GRCm38,p5) with STAR v2.7.3. Distribution of the reads across genomic isoform expression was quantified using the R package GenomicRanges from Bioconductor Version 3.10. Minimum mapping quality, as well as minimum feature overlaps, was set to 10. Multi-overlaps were allowed. Differentially expressed genes (DEGs) were identified using the R package edgeR from Bioconductor Version 3.10, using a generalized linear model (glm) regression, a quasi-likelihood (QL) differential expression test and the trimmed means of M-values (TMM) normalization.