Find research datasets worth reusing

Compilation of Land Use Data in 21 and 37 Category Classifications - Ipswich and Parker River Watersheds - 1971, 1985, 1991, and 1999 - Vector Shapefile.

The MassGIS Land Use datalayer has 37 land use classifications interpreted from 1:25,000 aerial photography. This layer contains data for 21 and 37 category classifications for the years of 1971, 1985, 1991, and 1999. Coverage is complete for all towns that fall partially or completely within the Ipswich River and/or Parker River watersheds. Data compiled for 1971, 1985, 1991, and 1999.

Boundaries of the designated study area - Ipswich and Parker River Watersheds - Idrisi Vector File.

Individual Towns that are Fully or Partially in the Ipswich and Parker River Watersheds - Idrisi Vector File.

This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole. This layer shows the boundaries for the towns in the Ipswich River Watershed and the Parker River Watershed. This data layer was created so that the town boundaries would correspond to the boundaries of the corresponding land use maps. This datalayer has complete information. Display town boundaries for the study area.

Boundaries of the Ipswich River and Parker River Watersheds - Idrisi Vector File.

This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer was created in July 2006 for Marine Biological Laboratory (MBL) in Woods Hole. This layer shows the boundaries for the Ipswich River and the Parker River Watersheds. This datalayer has complete information. Display watershed boundaries for the study area.

openmit-licenseMar 2020View details →

Vector-LabPics dataset for images of materials in vessels in the chemistry lab

LabPics 2: A newer and larger a version (But harder to use)  can be found here: <a href="../record/4736111"> https://zenodo.org/record/4736111</a> The Vector-LabPics V1 dataset contains 2187 images of chemical experiments with materials within mostly transparent vessels in various laboratory settings and in everyday conditions such as beverage handling. Each image in the dataset has an annotation of the region of each material phase and its type. In addition, the region of each vessel and its labels, parts, and corks are also marked. For more details see: <a href="https://pubs.acs.org/doi/10.1021/acscentsci.0c00460">https://pubs.acs.org/doi/10.1021/acscentsci.0c00460</a>     Acknowledgments We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images of the dataset were taken from images and videos shared on Youtube and Instagram, Twitter and Tumblr channels and other contributors; we do not have copyright for the images. Any commercial or none academic use of the images depends on acquiring permission from the owner of the images. Note that the name of each image contains the image source. For any non-academic use of the images, please contact their sources for permission. We like to thank the following channels for sharing the images used in this dataset. We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images from C&EN's Chemistry in Pictures (<a href="http://cen.chempics.org/">cen.chempics.org</a>) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.

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Data from: Transformation of measurement uncertainties into low-dimensional feature vector space

Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for post-processing, or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose global ocean temperature data. The uses of the method range from supporting decision making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterisation of the El Niño Southern Oscillation.

opencc-zeroJan 2021View details →

opencc-by-4.0Dec 2020View details →

Vector sequences in early WIV SRA sequencing data of SARS-CoV-2 inform on a potential large-scale security breach at the beginning of the COVID-19 pandemic

DESCRIPTION Sequences identified as Influenza A virus, Spodoptera frugiperda rhabdovirus and Nipah henipavirus have been previously identified within the early HiSeq 1000 and HiSeq 3000 sequencing data of SARS-CoV-2, SRR11092059,SRR11092060,SRR11092061 and SRR11092062, and were being used to support the hypothesis that a "simultaneous outbreak of multiple zoonotic viruses" have happened in the Huanan Seafood market. https://doi.org/10.31219/osf.io/s4td6 However, a closer examination of these sequences revealed that they were not sequences of actual wild viruses, but were in stead fragments left behind from PCR products and cloning vectors harboring both cDNA clones and infectious clones of such viruses, with evidence of viral sequences being joined directly to DNA sequences of vector and non-human origin within the same short reads. Here are the vector sequences and PCR product-like sequences recovered from the earliest WIV SRA sequencing data of Human SARS-CoV-2 from dataset SRR11092059,SRR11092060,SRR11092061,SRR11092062. Sequences associated with Vectors and PCR products from 3 distinct viral species have been obtained: The 3'-end of a Nipah Henipahvirus with fusion to a Hepatitis D virus Ribozyme, a T7 terminator and a Tetracycline resistance gene, The 5'-end of the same Nipah Henipahvirus with fusion to sequences found in diverse vectors, A complete vector genome encoding the HA gene of Influenza A virus subtype H7N9 under a CMV promoter and a bgH polyA terminator, and 221 Contiguous sequences corresponding to the Spodoptera frugiperda rhabdovirus reference genome fused to sequences that were homologous to multiple Plastid sequences and Notably Mitochondrial sequences of Rodents. As sequences corresponding to a rescued infectious clone of a BSL-4 organism (Nipah Henipahvirus) were found in sample sequences that supposedy represents patient samples that were obtained from Hospital ICU and sequenced in a pathogen diagnosis laboratory (which is separate from the Virology Research laboratory which is implied by the context of an Infectious Clone of such an organism, evident by the 3'-HDV ribozyme and T7 terminator fused directly to the 3'-terminus of the Nipah Henipahvirus reads), The discovery of artifact-containing sequences of at least 3 different pathogen species that are phylogenetically and methodologically distinct from each other in samples that were supposedly submitted by a laboratory that is Separate from the virological research laboratories that could have hosted such clone sequences imply extensive crosstalk and cross-contamination between the various laboratories within the Wuhan Institute of Virology, which includes at least one BSL-4 laboratory with evidence of containment breach of a BSL-4 organism and it's subsequent introduction into RNA-seq samples that were processed by a laboratory of distinct and separate purposes than the basic virological research evidenced by the Infectious Clone of the Hipah Henipahvirus. Such a discovery therefore likely imply a major security breach happening within the Wuhan institute of Virology at the time when the first sequences of SARS-CoV-2 was sampled and sequenced, which have important implications on the origins of the SARS-CoV-2 virus itself. METHODS The metagenomic sequencing datasets, SRR11092059,SRR11092060,SRR11092061 and SRR11092062 were first analyzed using the NCBI phylogenetic analysis tool, which identified viral sequences that is not related to SARS-CoV-2 itself. These include Influenza A virus (IAV, subtype H7N9), Spodoptera frugiperda rhabdovirus and Nipah Henipahvirus. The datasets were then subjected to BLAST search using MEGABLAST against the reference sequences of such viruses to verify the existence of the viral sequences and determine the exact sybtype of such viruses and the closest sequences on GenBank that corresponds to the reads. There seuqences are MH926031.1 for the  Spodoptera frugiperda rhabdovirus,  KY199425.1 for the Influenza A virus and AY988601.1 for the Nipah Henipahvirus. A second round BLAST analysis with these identified sequences were then performed, which unexpectedly revealed numerous reads corresponding to Cloning vectors and non-human Mitochondrial and Plastid sequences being fused directly to the sequences of the identified viral species. Reads were then downloaded and subjected to assembly using the CAP3 sequence assembly program and the EGASSEMBLER tool. Contig sequences were then queried against the NCBI nr/nt database which unanimously identified the original sample sequences as viral sequences inserted into cloning vectors. The complete sequence of the Influenza A virus Haemagluttinin (HA) gene clone was obtained from SRR11092061,SRR11092062 using multiple rounds of BLAST search and sequence assembly expansion on the existing vector-virus junction contigs, and a partial sequence corresponding the 3'-end of Nipah Henipahvirus AY988601.1 fused to a 3'-HDV ribozyme, T7 terminator and a Tet resistance gene was obtained from SRR11092059. In addition, 221 Contig sequences corresponding to the Rhabdovirus MH926031.1 fused to Chloroplast sequence MN524635.1 and Rodent Mitochondrial sequence MT241668.1 have been recovered from SRR11092061. We then performed a BLAST search using the identified vector sequences on SRR11092059,SRR11092060,SRR11092061 and SRR11092062, which confirms the existence of these two vetor sequences in all 4 datasets.

opencc-by-4.0Dec 2017View details →

Figure 2. Box-plot head centroid size. A. Rhodnius prolixus instars. B in Head geometric morphometrics of two Chagas disease vectors from Venezuela

Figure 2. Box-plot head centroid size. A. Rhodnius prolixus instars. B. Triatoma maculata instars. Abbreviation: I—First instar; II— Second instar; III—Third instar; IV—Fourth instar; V—Fifth instar; F—Adult female; M—Adult male.

opencc-by-4.0Dec 2017View details →

Figure 4. Canonical Variates Analysis head conformation diagram for 136 in Head geometric morphometrics of two Chagas disease vectors from Venezuela

Figure 4. Canonical Variates Analysis head conformation diagram for 136 Triatoma maculata specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.

opencc-by-4.0Dec 2017View details →

Figure 3. Canonical Variates Analysis head conformation diagram for 140 in Head geometric morphometrics of two Chagas disease vectors from Venezuela

Figure 3. Canonical Variates Analysis head conformation diagram for 140 Rhodnius prolixus specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.

opencc-by-4.0Nov 2023View details →

Reproduction Package (VirtualBox Image) for the POPL 2024 Article `Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations`

This is the artifact for the ACM PACMPL article Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations. We provide our artifact as an easy-to-use VirtualBox image, which contains the benchmarks, our tools for bit-vector synthesis, and the scripts for generating the results showcased in the paper.

opencc-by-4.0Apr 2021View details →

Dataset for ICDAR 21 paper "Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction"

This is the dataset of the ICDAR 2021 conference paper "Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction".

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Bloodmeal metabarcoding of the argasid tick (Ornithodoros turicata Dugès) reveals extensive vector-host associations

Molecular methods to understand host feeding patterns of arthropod vectors are critical to assess exposure risk to vector-borne disease and unveil complex ecological interactions. We build on our prior work discovering the utility of PCR-Sanger sequencing bloodmeal analysis that work well for soft ticks (Acari: Argasidae), unlike for hard ticks (Acari: Ixodidae), thanks to their unique physiology that retains prior bloodmeals for years. Here, we apply bloodmeal metabarcoding using amplicon deep sequencing to identify multiple host species in individual Ornithodoros turicata soft ticks collected from two natural areas in Texas, United States. Of 788 collected O. turicata, 394 were evaluated for bloodmeal source via metabarcoding, revealing 27 different vertebrate hosts (17 mammals, 5 birds, 1 reptile, and 4 amphibians) fed upon by 274 soft ticks. Information on multiple hosts was derived from 167 individual O. turicata (61%). Metabarcoding revealed mixed vertebrate bloodmeals in O. turicata while same specimens yielded only one vertebrate species using Sanger sequencing. These data reveal wide host range of O. turicata and demonstrate the value of bloodmeal metabarcoding for understanding the ecology for known and potential tick-borne pathogens circulating among humans, domestic animals and wildlife such as relapsing fever caused by Borrelia turicatae. Our results also document evidence of prior feeding on wild pig from an off-host soft tick for the first time in North America; a critical observation in the context of enzootic transmission of African swine fever virus if it were introduced to the US. This research enhances our understanding of vector-host associations and offers a promising perspective for biodiversity monitoring and disease control strategies.

opencc-zeroJan 2024View details →

opencc-by-4.0Mar 2024View details →

AeroVmag: A new aero-towed vector magnetometer system, Geophysics, 2025

This dataset contains: <ol> <li> Vector magnetic anomaly maps for the northern part of the Sea of Galilee 1.1 Mag_grids_xyz - grids in XYZ format (WGS 84) 1.2 Mag_grids.nc - grids in netCDF4 format (WGS84 / UTM 36N) </li> <li>Aerovmag.py - source code for processing vectorial magnetic data collected by AeroVmag tow-bird system</li> <li> Theoretical_model.ipynb - source code for the theoretical accuracy estimation of the vectorial magnetic survey system. </li> </ol>

opencc-by-4.0Nov 2016View details →

Fig. 2. A in Genetic differentiation in populations of Aedes aegypti (Diptera, Culicidae) dengue vector from the Brazilian state of Maranhão

Fig. 2. A priori estimate of the probable groups of populations produced by the BAPS (Bayesian Analysis of Population Structure v 6.0) program, indicating a total of two groups.

Fig. 3 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 3. Predicted re-encounter rates (95% confidence intervals [CIs]) over a single trapping interval (2007–2008) for adult female and male black-tailed prairie dogs inoculated at Conata Basin, South Dakota in 2007 with F1–V fusion protein vaccine or placebo on the no dust and dusted plots (the latter with flea control). Sample sizes are depicted above the 95% CIs.

Fig. 1 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 1. Categories of flea vector control (deltamethrin dust) and F1–V fusion protein plague vaccination (V = vaccine, P = placebo, N = no inoculation) used for analyses of black-tailed prairie dog annual re-encounter rates (2007–2008 and 2008–2009) at Conata Basin, South Dakota. Annual re-encounter rates were compared for subsets of animals, here each enclosed by unique rectangles. Sample sizes are depicted in subsequent figures with results from multivariate analyses.

Fig. 5 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 5. Predicted re-encounter rates (95% confidence intervals [CIs]) over a single trapping interval 2007–2008 for non-inoculated adult and juvenile blacktailed prairie dogs on the no dust and dusted plots (the latter with flea control) at Conata Basin, South Dakota. Sample sizes are depicted above the 95% CIs.

Fig. 2 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 2. Predicted flea parasitism (95% confidence intervals [CIs]) on blacktailed prairie dogs at the no dust and dusted plots, 2007–2008 at Conata Basin, South Dakota (prevalence on the left, intensity on the right). Prairie dog burrows on the dusted plots were treated annually with deltamethrin dust at ~4–6 g per burrow. Model predictions adjust (i.e., control) for year and Julian day (adjusted here as year 2008, and Julian day 212 for prevalence and 200 for intensity). Flea intensity data were log-transformed (log10) for analysis; hence, predicted flea intensity and 95% CIs could extend below 0. Sample sizes are depicted above the 95% CIs.

Fig. 4 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 4. Predicted re-encounter rates (95% confidence intervals [CIs]) over two trapping intervals (2007–2008 and 2008–2009) for adult female and male black-tailed prairie dogs in Conata Basin, South Dakota inoculated in 2007 or 2008 with F1–V fusion protein vaccine or placebo on the dusted plots (with flea control). Sample sizes are depicted above or below the 95% CIs.