Find research datasets worth reusing

opencc-by-4.0Aug 2020View details →

FRB 121102 detections from the Lovell Telescope

We provide data for FRB 121102 pulses detected with the Lovell telescope at Jodrell Bank, UK at 1.4 GHz. These detections were used in Rajwade et al. (2020). Each file is a single pulse from FRB 121102 in the standard SIGPROC filterbank format and can be read in by standard radio pulsar data processing software. The tarball contains all the filterbank files. The text file contains the name of each file in the first column and seconds into the file where the pulse is located in the second column. The start time of the pulse is with reference to the highest frequency channel i.e seconds into the file where the pulse was detected in the highest frequency channel.   Citation: If you are planning to use these data in any publication, please do not forget to put in the following acknowledgement: "The data used in this manuscript have been downloaded from the Zenodo open access repository 'FRB 121102 detections from the Lovell Telescope' (10.5281/zenodo.3974768)."

opencc-zeroAug 2020View details →

Data from: Sticky traps as an early detection tool for crawlers of Adelges tsugae (Hemiptera: Adelgidae)

We developed an approach using sticky trap arrays as an early detection tool for populations of first instar nymphs of the hemlock woolly adelgid, Adelges tsugae (Annand), a pest of hemlocks, Tsuga spp., in North America. We considered the detection rate of at least one nymph from trapping arrays consisting of one to six sticky panels, where we varied both the surface area of each trap that we assessed and the length of the trapping duration. We also estimated the time needed to set up, service and assess groups of traps and attempted to relate capture of nymphs on traps to incidence and abundance of A. tsugae in the canopy above the traps. Arrays consisting of two traps provided a detection rate of 75% when 87.5% of the surface area of each trap was assessed, a process that required 38 min per array. The probability of detecting nymphs on traps left in the field for 5 or 6 days was similar to that for traps left for 12 days. The number of nymphs trapped in an array predicted the probability of finding A. tsugae in the canopy but only when all six traps were fully assessed. To reliably detect incipient A. tsugae infestations, we recommend placing arrays of traps at 1 km intervals along the perimeter of a stand during peak activity of first instar sistentes nymphs and servicing these arrays every 5 to 7 days.

opencc-zeroAug 2020View details →

ClinePlotR: Visualizing genomic clines and detecting outliers in R

Patterns of multi-locus differentiation (i.e., genomic clines) often extend broadly across hybrid zones and their quantification can help diagnose how species boundaries are shaped by adaptive processes, both intrinsic and extrinsic. In this sense, the transitioning of loci across admixed individuals can be contrasted as a function of the genome-wide trend, in turn allowing an expansion of clinal theory across a much wider array of biodiversity. However, computational tools that serve to interpret and consequently visualize 'genomic clines' are limited. Here, we introduce the ClinePlotR R-package for visualizing genomic clines and detecting outlier loci using output generated by two popular software packages, bgc and Introgress. ClinePlotR bundles both input generation (i.e, filtering datasets and creating specialized file formats) and output processing (e.g., MCMC thinning and burn-in) with functions that directly facilitate interpretation and hypothesis testing. Tools are also provided for post-hoc analyses that interface with external packages such as ENMeval and RIdeogram Our package increases the reproducibility and accessibility of genomic cline methods, thus allowing an expanded user base and promoting these methods as mechanisms to address diverse evolutionary questions in both model and non-model organisms.

opencc-by-4.0Sep 2020View details →

Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations: data related to publication

Data related to the following publication: "Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations"

opencc-zeroSep 2020View details →

Data from: Improved campephiline detection: an experiment conducted with the Magellanic woodpecker

1. Woodpeckers can be difficult to detect, as they are often cryptic, secretive, occurring in low densities, and wary of humans. Several methods exist to detect woodpeckers (e.g., playback surveys, passive point counts), yet no research has established which technique best detects these elusive picids. Thus, we designed an experiment to determine which of three methods best results in a detection of Magellanic Woodpeckers (Campephilus magellanicus), and if weather variables influence detection probability. 2. Mostly during austral summers 2015-2017, we i) used a drumming device to simulate a double-knock (i.e., territorial acoustical signal), ii) broadcasted a territorial call, and iii) passively listened (control) for Magellanic Woodpeckers. We conducted our experiment on Navarino Island, Chile, where the Magellanic Woodpecker is the sole picid. 3. The drumming device most effectively influenced the likelihood of a woodpecker detection. The odds of a woodpecker responding to a double-knock were 2.1 times more likely than responding to either a call or control. Moreover, the odds of a woodpecker detection decreased by 42% as wind increased by one category and decreased by 40% for every additional month (i.e., Oct.-Mar.), which was expected because woodpeckers become less territorial as the breeding season progresses. 4. As Campephilus woodpeckers communicate via drums or double-knocks, using a drumming device likely will be an effective technique to detect not only Magellanic Woodpeckers, but other woodpeckers within the Campephilus genus in Central and South America.

opencc-zeroSep 2020View details →

Supplementary material 1 from: Abram PK, McPherson AE, Kula R, Hueppelsheuser T, Thiessen J, Perlman SJ, Curtis CI, Fraser JL, Tam J, Carrillo J, Gates M, Scheffer S, Lewis M, Buffington M (2020) New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. Journal of Hymenoptera Research 78: 1-17. https://doi.org/10.3897/jhr.78.55026

Collection information for specimens reported in: New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis

opencc-by-4.0Aug 2020View details →

A Dataset for Evaluating Blood Detection in Hyperspectral Images

The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.

opencc-by-4.0Sep 2020View details →

ScaR — a tool for sensitive detection of known fusion transcripts

Use scaffold re-aligning approach to detect the prevalence and recurrence of known fusion transcripts across samples

opencc-by-4.0Sep 2020View details →

Foundation Detection Model

Foundation Detection Model

opencc-zeroSep 2020View details →

Data from: Spotting the pests of tomorrow - Sampling designs for detection of species associations with woody plants

Aim: Early warning against potentially harmful organisms of woody plant species can be achieved by sampling sentinel plants in exporting countries. However, it is unclear where sentinel plants can best be located, and how many samples are required and when and how often sampling optimally should take place for the adequate assessment of the biodiversity associated with the target plant species. We aimed to review spatial and temporal factors affecting associate biodiversity of single woody plant species and to develop guidance for the design of global biodiversity sampling studies. Location: Worldwide. Taxon: Insects and Fungi. Methods: Literature about factors affecting the diversity of insects and fungi in association with single plant species on global, regional, local and different temporal scales was reviewed. Case studies of insect and fungal diversity, primarily collected on single plant species, and the cost of collecting and analysing samples from locations around the world were analysed. Results: The review of the literature illustrated various factors affecting diversity, and the case studies allowed quantification of the relative impact of some spatial, temporal and financial aspects on captured biodiversity and, thus, illustrate the need to consider all possible factors that may affect the result of the sampling when deciding on a sampling design. Main conclusions: Our study illustrates the factors that should be considered when deciding on the location and timing of sampling for sentinel plants, which is important because of the trade-off between the number of samples and sampling locations needed to detect many of the species which may be potential pests, and the cost of (repeated) sampling in many locations. Decisions about the sampling design must be based on the objective of the sampling, but our recommendations apply irrespective of the targeted plant species or country.

opencc-zeroOct 2020View details →

Using genomic prediction to detect microevolutionary change of a quantitative trait

Detecting microevolutionary responses to natural selection by observing temporal changes in individual breeding values is challenging. The collection of suitable datasets can take many years and disentangling the contributions of the environment and genetics to phenotypic change is not trivial. Furthermore, pedigree-based methods of obtaining individual breeding values have known biases. Here, we apply a genomic prediction approach to estimate breeding values of adult weight in a 35-year dataset of Soay sheep (Ovis aries). Comparisons are made with a traditional pedigree-based approach. During the study period adult body weight decreased, but the underlying genetic component of body weight increased, at a rate that is unlikely to be attributable to genetic drift. Thus cryptic microevolution of greater adult body weight has probably occurred. Genomic and pedigree-based approaches gave largely consistent results. Thus, using genomic prediction to study microevolution in wild populations can remove the requirement for pedigree data, potentially opening up new study systems for similar research.

Recalibrating classifiers for interpretable abusive content detection

Dataset and code for the paper, 'Recalibrating classifiers for interpretable abusive content detection' by Vidgen et al. (2020) -- to appear at the <a href="https://sites.google.com/site/nlpandcss/">NLP + CSS workshop</a> at <a href="https://2020.emnlp.org">EMNLP 2020</a>. We provide: <ol> <li>1,000 annotated tweets, sampled using the Davidson classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>1,000 annotated tweets, sampled using the Perspective classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>Code for recalibration in R and STAN.</li> <li>Annotation guidelines for both datasets.</li> </ol>   Paper abstract We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the 'raw' scores are used) align poorly with human evaluations. This limits their use to understand the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election. A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.

opencc-by-nc-4.0Oct 2020View details →

PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media

A Russian-language sentiment lexicon for social media discussions on political and social issues. The file contains raw markings collected with LINIS coding service <a href="https://linis-crowd.org">https://linis-crowd.org</a> [in Russian]. Learn more about PolSentiLex in our papers: <ul> <li> Koltsova, O., & Alexeeva, S. (2015). Linis-crowd.org: A lexical resource for Russian sentiment analysis of social media [Linis-crowd.org: Lexichesk resurs dl’a analiza tonal’nosti sotsial’no-politicheskix tekstov]. Computational Linguis- Tics and Computantional Ontologies: Proceedings of the XVIII Joint Conference “Internet and Modern Society (IMS-2015)” [Kompyuternaya Lingvistika i Vyichis- Litelnyie Ontologii: Sbornik Nauchnyih Statey. Trudyi XVIII Ob’edinennoy Konferen- Tsii «Internet i Sovremennoe Obschestvo» (IMS-2015)], 25–34. [in Russian] URL: <a href="https://scila.hse.ru/data/2020/06/02/1603986481/koltsovaoyuetal.pdf">https://scila.hse.ru/data/2020/06/02/1603986481/koltsovaoyuetal.pdf</a> </li> <li> Koltsova, O., Alexeeva, S., & Koltsov, S. (2016). An Opinion Word Lexicon and a Training Dataset for Russian Sentiment Analysis of Social Media. Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2016”, 277–287. URL: <a href="http://www.dialog-21.ru/media/3400/koltsovaoyuetal.pdf">http://www.dialog-21.ru/media/3400/koltsovaoyuetal.pdf</a> </li> <li> Koltsova O., Alexeeva S., Pashakhin S., Koltsov S. (2020) PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media. In: Filchenkov A., Kauttonen J., Pivovarova L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science, vol 1292. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-59082-6_1">https://doi.org/10.1007/978-3-030-59082-6_1</a> </li> </ul>

RefDiff4Go: Detecting Refactorings in Go

RefDiff4Go: Detecting Refactorings in Go.  Presentation for 14th Brazilian Symposium on Software Components, Architectures, and Reuse (SBCARS ’20), October 19–20, 2020, Natal, Brazil

100 NPM Repositories used for Anomalicious Commit Detection Experiment

100 NPM Repositories used to evaluate positive rates of the Anomalicious Commit Detector

Microbiome dataset used in SIMON analysis for early-stage detection of cancer

The Zeller dataset contains information on the microbiome species abundance in healthy individuals and colorectal cancer patients (doi: 10.15252/msb.20145645) and was used for the SIMON analysis as described (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). The data was access through MetagenomicData package as described by Pasolli and colleagues (doi: 10.1038/nmeth.4468). In total 184 individuals were included of which 93 were healthy controls and 91 colorectal cancer patients.

SDT Dataset | Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification

The Synthetic Depth & Thermal (SDT) dataset consists of 40k synthetic and 8k real depth and thermal stereo images, depicting human behavior in indoor environments. Included samples show uniquely posed lying, sitting, and standing persons within four different room types (living room, bedroom, bathroom, and kitchen), recorded from an elevated position. Furthermore, a fourth control class with empty rooms is provided as well. Both parts of SDT are balanced sets of these four classes and room types. The synthetic part of the dataset is intended to be used as training (and validation) data for uni-/multi-modal pose classification or person detection models, while the real part can be used to assess the generalization performance. To facilitate supervised training, pose labels and person bounding boxes are provided for all images. The real images in the dataset were captured by a multi-modal stereo camera system, consisting of an Orbbec Astra depth camera and a FLIR Lepton 3.5 thermal camera, while synthetic images, which share the image characteristics of these cameras, were acquired through 3D rendering of virtual scenes within Blender and subsequent introduction of camera-specific noise. Download and Use This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1]. [1] C. Pramerdorfer, J. Strohmayer and M. Kampel, "Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification," 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 1611-1615, doi: 10.1109/ICIP40778.2020.9191284. BibTeX citation: <pre>@INPROCEEDINGS{9191284, author={Pramerdorfer, C. and Strohmayer, J. and Kampel, M.}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, title={Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification}, year={2020}, volume={}, number={}, pages={1611-1615}, doi={10.1109/ICIP40778.2020.9191284}}</pre>

Detection of fusion transcripts and their genomic breakpoints from RNA sequencing data - Table S03 - All detected SVs.xlsx

Large concatenated results table on all samples of the Dr. Disco study.

opencc-by-4.0Nov 2020View details →

Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels

Dataset accompanying "Ryan Brate, Paul Groth and Marieke van Erp (2020) Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels. LaTeCH-CLfL 2020. Barcelona, December 2020." Abstract: Environmental factors determine the smells we perceive, but societal factors factors shape the importance, sentiment and biases we give to them. Descriptions of smells in text, or as we call them `smell experiences', offer a window into these factors, but they must first be identified. To the best of our knowledge, no tool exists to extract references to smell experiences from text. In this paper, we present two variations on a semi-supervised approach to identify smell experiences in English literature. The combined set of patterns from both implementations offer significantly better performance than a keyword-based baseline.