Find research datasets worth reusing

Fig. 9 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 9. Composition of barnacle cyprid diversity at different stations and different year of collection.

Fig. 6 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 6. Light and scanning electron micrograph of cyprids of: A–H, Amphibalanus variegatus; and I–L, Euraphia withersi. Details of specific carapace sculpturing patterns in each species are shown at higher magnification. 6I, E. withersi has reddish pigments around the carapace (arrows) and a dark rounded pigmentation spot (circled).

Fig. 5 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 5. Light and scanning electron micrograph of cyprids of: A–D, Fistulobalanus sp.; and E–J, Fistulobalanus patellaris. Details of specific carapace sculpturing patterns in each species are shown at higher magnification.

Fig. 2 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 2. Lateral view of cyris larvae of barnacle showing measurements used for morphometric analysis. CL: carapace length; CH: carapace height; A: posterior carapace angle. Ratio of CL/CH was also calculated.

Fig. 1 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 1. Map of sampling locations at Matang Mangrove Forest Reserve (MMFR) in Perak, Malaysia. Sampling was carried out in April 2011 at sites 1–8 and in June 2012 at sites 9–14.

Fig. 4 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 4. Histogram showing variations of pair-wise genetic distances computed from 12S-rRNA gene fragment sequences using Kimura 2-parameter model. Note the distribution of within-species variations does not overlap with that of inter-species variation.

Fig. 3. Neighbour-joining tree contructed from partial 12S in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 3. Neighbour-joining tree contructed from partial 12S-rRNA gene fragment sequences of cyprids and adults of barnacle. The sequences were clustered into eight clades, and species name were labelled at the clades containing sequence(s) of identified adult of barnacle. Clades with no sequence of identified barnacle adult clustered within were designated as OTU (Operational Taxonomic Unit). Number of sequences in each clade were also shown. Scale bar denotes 0.02 base substituition per site.

openother-ncDec 2021View details →

Podcast annotation dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts "

Dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts". Please refer to the paper for details. Compared to the dataset used in the paper, 20 out of the 417 episodes have been removed due to copyright issues.  The data file contains the following fields: - "episode_intro_start": the time stamp for episode introduction start (in milliseconds) - "episode_intro_end": the time stamp for episode introduction end (in milliseconds) - "program_intro_start": the time stamp for program introduction start (in milliseconds) - "program_intro_end": the time stamp for program introduction end (in milliseconds) - "program_name": name of the podcast program - "episode_name": name of the podcast episode - "transcription": JSON string containing the transcription, including the timestamps. - "annotator": anonymized annotator ID.

opencc-by-4.0Dec 2021View details →

An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes

The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA.  It also includes four images derived from the DEM image.  The image dataset is sourced from publicly available  data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).

opencc-by-4.0Dec 2021View details →

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis 2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis

dryad40/100

Using species distribution models and decision tools to direct surveys and identify potential translocation sites for a critically endangered species

Aim: Occurrence records for cryptic species are typically limited or highly uncertain, leaving their distributions poorly resolved and hampering conservation. This can apply to well‐studied species, and increased survey effort and/or novel methods are required to improve distribution data. Here, we paired species distribution modelling (SDM) with decision tools to direct surveys for the critically endangered Leadbeater's possum (Gymnobelideus leadbeateri) outside its current restricted range. We also assessed survey areas for their suitability to host translocations. Location: Victoria, Australia. Method: We used both recent and historic records (now out of range and spatially uncertain) of Leadbeater's possum to build SDMs using MaxEnt. The SDMs informed an initial multi‐criteria decision analysis (MCDA) that enabled prioritization of 80 survey sites across seven forest patches (13–145 km outside the known range), which we surveyed using camera traps. Site and vegetation data were used in a post‐survey MCDA to rank their potential translocation suitability. Results: The SDM predictions were consistent with the species' ecology, identifying cold areas with high rainfall that had not recently burnt as suitable. The spatial uncertainty of records did not exert a strong influence on either model predictions or the ranking of patches for surveys. Camera trap surveys yielded records of 19 native species, with Leadbeater's possum detected in only one survey patch, 13 km outside of its previously known range. The post‐survey MCDA identified three forest patches as potentially suitable for conservation translocations, and these priorities were not sensitive to the decision criteria used. Main conclusions: The approach outlined here prioritized survey effort over a large area, resulting in detection of Leadbeater's possum in one new patch. The potential translocation sites identified could present an important risk‐spreading measure for the species given the threat posed by bushfire. Combining SDMs and decision tools can help target surveys and guide subsequent conservation strategies.

opencc-zeroJan 2022View details →

opencc-by-4.0Jan 2022View details →

Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).

BioMates - Coding of identifiers for WP1-samples and -blends

Ablative Fast Pyrolysis (AFP) is the first step in the BioMates-concept to convert herbaceous biomass into co-feed with reliable properties for conventional refineries (www.biomates.eu). The document provides the coding behind the identifiers used for samples and sample blends produced by Fraunhofer UMSICHT via AFP within the H2020-project BioMates. Thus, it supplements the data-set-document “BioMates - WP1: Novel pyrolysis oil from non-food/feed biomass - Data sheet: Identifiers for samples and blends”, DOI:10.24406/fordatis/156

Towards identifying industrial crop types and associated agronomies to improve biomass production from marginal lands in Europe

Background: Growing industrial crops on marginal lands has been proposed as a strategy to minimize competition for arable land and food production. In the present study, eight experimental sites in three different climatic zones in Europe (Mediterranean, Atlantic and Continental), seven advanced industrial crop species [giant reed (two clones), miscanthus (M. × giganteus and two new seed-based hybrids), saccharum (one clones), switchgrass (one variety), tall wheatgrass (one variety), industrial hemp (three varieties) and willow (eleven clones)], and six marginality factors alone or in combination (dryness, unfavorable texture, stoniness, shallow soil, topsoil acidity, heavy metal and metalloid contamination) were investigated. At each site, biophysical constraints and low-input management practices were combined with prevailing climatic conditions. Results: The relative yield of a site-specific low-input system compared with the site-specific control was from small to large (i.e., from -99% in industrial hemp in the Mediterranean to +210% in willow in the Continental zone), due to the genotype-by-management interaction along with climatic variation between growing seasons. Genotype selection and improved knowledge on crop response to changing environmental, site-specific biophysical constraint and input application has been detected as key to profitably grow industrial crops on marginal areas. Conclusions: This study may act to provide hints on how to scale-up investigated cropping systems, through low-input practices, under similar environmental and soil conditions tested at each site. However, further attention to detail on the agronomy of early plant development and management in larger multi-year and multi-location field studies with commercially scalable agronomies are needed in order to validate yield performances, and thereby to inform on the best industrial crop options.

Understanding Digits in Identifier Names: An Exploratory Study

This is the dataset that accompanies the study: "Understanding Digits in Identifier Names: An Exploratory Study." This study has been accepted for publication at: The 1st International Workshop on Natural Language-based Software Engineering (NLBSE 2022).   Following is the abstract of the study: Before any software maintenance can occur, developers must read the identifier names found in the code to be maintained. Thus, high-quality identifier names are essential for productive program comprehension and maintenance activities. With developers free to construct identifier names to their liking, it can be difficult to automatically reason about the quality and semantics behind an identifier name. Studying the structure of identifier names can help alleviate this problem. Existing research focuses on studying words within identifiers, but there are other symbols that appear in identifier names-- such as digits. This paper explores the presence and purpose of digits in identifier names through an empirical study of 800 open-source Java systems. We study how digits contribute to the semantics of identifier names and how identifier names that contain digits evolve over time through renaming. We envision our findings improving the efficiency of name appraisal and recommendation tools and techniques.

Fig. 2 in A new lineage of Galapagos giant tortoises identified from museum samples

Fig. 2 Statistical parsimony haplotype network of the mitochondrial control region (668 bp) for 129 contemporary individuals from San Cristóbal and six historical specimens collected in 1906, as well as 28 representative haplotypes from the other species of Galapagos giant tortoise. The name of the island where each species occurs is labeled with capital letters, with current taxonomy in italics. Haplotypes are represented as black circles on the network, the size of the circle is proportional to the frequency of the haplotype in the analysis. Open circles represent unsampled, hypothesized haplotypes, and hash marks indicate a single mutational change. Reticulations reflect uncertainty in relationships, or homoplasy.

Fig. 4 Barplots depicting K in A new lineage of Galapagos giant tortoises identified from museum samples

Fig. 4 Barplots depicting K = 2 for the STRUCTURE analysis using the SNP (12 192 loci) and microsatellite (21 loci) genotypes for the contemporary San Cristóbal population (n = 64). Each bar represents an individual and the proportion of the bar that is each color represents the membership of that individual to the two clusters. The order of individuals is the same in both plots, black boxes around bars highlight individuals with a greater than 0.4 discrepancy in assignment proportions between the analyses.

Fig. 3 in A new lineage of Galapagos giant tortoises identified from museum samples

Fig. 3 Bayesian Inference maximum clade credibility cladogram showing relationships among the San Cristóbal historical samples from the cave and collected alive in 1906, and a reference dataset of 93 Galapagos giant tortoise haplotypes and three outgroups based on the mitochondrial control region (alignment length 718 bp), estimated using BEAST with a strict clock and Birth Death tree. The numbers on the branches are the posterior probability support values. § indicates the C. chathamensis type specimen. The name of the island where each clade is found is in capitals, with current taxonomy in italics.

Fig. 1 in A new lineage of Galapagos giant tortoises identified from museum samples

Fig. 1 Map of the Galapagos Archipelago, indicating the locations of each Chelonoidis species, with San Cristóbal Island enlarged in the inset map. Island names are in capital letters. The approximate location of the cave where the bones were found in 1906 is marked, as is the approximate location where CAS 8133 was collected alive and the region of Punta Pitt.