Find research datasets worth reusing

opencc-by-4.0Nov 2013View details →

Fig. 3 in A New Genus and Two New Species of the Family Aulopidae (Aulopiformes), Commonly Referred to as Aulopus, Flagfins, Sergeant Bakers or Threadsails, in Australasian Waters

Fig. 3. Hime pyrhistion sp. nov.: A) NMNZ P.054047, holotype, 236 mm SL, male, Poor Knights Islands, New Zealand; B) dorsal fin of CSIRO H 6060–04, 216 mm SL, male, paratype, Norfolk Ridge, Tasman Sea (NORFANZ founding parties, photo by K. Parkinson); C) dorsal fin of NMNZ P.052258, non-type, 227 mm SL, male, Great Barrier Island, New Zealand; D) NMNZ P.047782, paratype, 197 mm SL, female, Poor Knights Islands, New Zealand; E) dorsal fin of NMNZ P.050424, paratype, 171 mm SL, female, Kermadec Islands, New Zealand; F) dorsal fin of NMNZ P.052762, paratype, 234 mm SL, female, Mayor Island, New Zealand.

opencc-by-4.0Nov 2013View details →

Fig. 6 in A New Genus and Two New Species of the Family Aulopidae (Aulopiformes), Commonly Referred to as Aulopus, Flagfins, Sergeant Bakers or Threadsails, in Australasian Waters

Fig. 6. Collection localities for specimens examined of Letaulopus spp. Leptaulopus damasi (triangles): neotype black with blue centre and other material blue. Lepaulopus erythrozonatus sp. nov (squares): holotype black with red centre and paratype red.

Fig. 4 in Microcrustacean (Cladocera, Copepoda) Communities In Artificial Lakes In The Region Of The North Hungarian Mountains, With Special Reference To The Adventive Species

Fig. 4. Occurrences of the non-indigenous microcrustacean species in the investigated artificial lakes of north-eastern Hungary. Abbrev.: Eurytemora velox: +, Daphnia ambigua: ▲, Pleuroxus denticu-

Fig. 1 in Microcrustacean (Cladocera, Copepoda) Communities In Artificial Lakes In The Region Of The North Hungarian Mountains, With Special Reference To The Adventive Species

Fig. 1. Species accumulation curves of the fish ponds for total microcrustaceans (a) and for cladocerans and copepods separately (b)

Fig. 3 in Microcrustacean (Cladocera, Copepoda) Communities In Artificial Lakes In The Region Of The North Hungarian Mountains, With Special Reference To The Adventive Species

Fig. 3. Frequency of occurrence (incl. species occurring in at least 5 fish ponds). Abbrev.: Chy_sph – Chydorus sphaericus, Bos_lon – Bosmina longirostris, Cyc_vic – Cyclops vicinus, Dap_cuc – Daphnia cucullata, Euc_ser – Eucyclops serrulatus, Aca_tra – Acanthocyclops trajani, Eud_gra – Eudiaptomus gracilis, Mac_alb – Macrocyclops albidus, Ple_adu – Pleuroxus aduncus, Sim_vet – Simocephalus vetulus, Mac_lat – Macrothrix laticornis, Euc_spe – Eucyclops speratus, The_oit – Thermocyclops oithonoides, Nit_hib – Nitocra hibernica, Alo_rec – Alona rectangula, Sca_muc – Scaphole-

Fig. 5 in Microcrustacean (Cladocera, Copepoda) Communities In Artificial Lakes In The Region Of The North Hungarian Mountains, With Special Reference To The Adventive Species

Fig. 5. The relative frequencies of Eurytemora velox (white), Eudiaptomus gracilis (black) and all other microcrustacean species (grey) in those fish ponds where the invasive E. velox was present

FIG. 3. — Opisthacanthus milloti n in Scorpions of the Réserve spéciale d'Ankarana, Madagascar, with particular reference to cave-dwelling animals and the description of two new species (Arachnida, Scorpiones)

FIG. 3. — Opisthacanthus milloti n. sp., ventral aspect, showing coxapophysis, sternum,genital operculum, pectines and sternite III: A, ♂ holotype; B, ♀ paratype. Scale bar: 2 mm.

FIG. 7. — Opisthacanthus pauliani n in Scorpions of the Réserve spéciale d'Ankarana, Madagascar, with particular reference to cave-dwelling animals and the description of two new species (Arachnida, Scorpiones)

FIG. 7. — Opisthacanthus pauliani n. sp., ♂ holotype, trichobothrial pattern: A-C, chela dorso-external, external and ventral aspects; D, femur, dorsal aspect; E, fixed finger, internal aspect; F-H, patella, dorsal, external and ventral aspects; I, cutting edge of movable finger with rows of granules. Scale bars: 2 mm.

FIG. 2. — Opisthacanthus milloti n in Scorpions of the Réserve spéciale d'Ankarana, Madagascar, with particular reference to cave-dwelling animals and the description of two new species (Arachnida, Scorpiones)

FIG. 2. — Opisthacanthus milloti n. sp., ♂ holotype and ♀ paratype: A, carapace; B, chelicera; C, hemispermatophore external aspect (♂); D, E, metasomal segment V and telson, lateral aspect (♂ and ♀). Scale bars: 2 mm.

FIG. 6. — Opisthacanthus pauliani n in Scorpions of the Réserve spéciale d'Ankarana, Madagascar, with particular reference to cave-dwelling animals and the description of two new species (Arachnida, Scorpiones)

FIG. 6. — Opisthacanthus pauliani n. sp., ♂ holotype: A, carapace; B, chelicera; C, ventral aspect, showing coxapophysis, sternum, genital operculum, pectines and sternite III; D, hemispermatophore, external aspect. Scale bars: 2 mm.

FIG. 4. — Opisthacanthus milloti n in Scorpions of the Réserve spéciale d'Ankarana, Madagascar, with particular reference to cave-dwelling animals and the description of two new species (Arachnida, Scorpiones)

FIG. 4. — Opisthacanthus milloti n. sp., ♂ holotype, trichobothrial pattern: A-C, chela dorso-external, external and ventral aspects; D, femur, dorsal aspect; E, fixed finger, internal aspect; F-H, patella, dorsal, external and ventral aspects; I, cutting edge of movable finger with rows of granules. Scale bars: 2 mm.

opencc-by-4.0Dec 2021View details →

Significant sQTLs and splicing TWAS reference panels (AMP-AD brain and EADB Belgian LCL cohorts)

This dataset is part of the manuscript "New insights into the genetic etiology of Alzheimer’s disease and related dementias" by Bellenguez, Küçükali, et al. Nature Genetics 2022. Publication link: <a href="https://www.nature.com/articles/s41588-022-01024-z">https://www.nature.com/articles/s41588-022-01024-z</a> GitHub repository of all QTL/TWAS data shared for this study: <a href="https://github.com/SleegersLab-VIBCMN/EADB_GWAS_NatureGenetics_QTL_TWAS">https://github.com/SleegersLab-VIBCMN/EADB_GWAS_NatureGenetics_QTL_TWAS</a> For details, please see the publication. For any questions, please contact Fahri Küçükali (<a href="mailto:fahri.kucukali@uantwerpen.vib.be">fahri.kucukali@uantwerpen.vib.be</a>) and Kristel Sleegers (<a href="mailto:Kristel.Sleegers@uantwerpen.vib.be">Kristel.Sleegers@uantwerpen.vib.be</a>). Significant sQTL catalogues are compressed with gzip and tar achieve of splicing TWAS reference panels are compressed with bzip2. sQTL catalogues The files show significant sQTL - splice junction pairs mapped in AMP-AD brain and EADB Belgian LCL cohorts. The catalogues are in hg38/GRCh38 human genome build. Most of the columns in the files are based on FastQTL output (<a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a>). sQTL file columns: <ol> <li>variant_id - ID of the significant sQTL variant based on the dbSNPv151 rsID annotation or hg38/GRCh38 CHR_POS_REF_ALT ID if rsID not available.</li> <li>junc_id - sJunction ID assigned by Regtools/Leafcutter pipeline. For stranded datasets (ROSMAP and EADB Belgian) strand info is provided with "+" or "-" symbols, and if not stranded, "?" symbol is used.</li> <li>junc_distance - Genomic distance between sQTL variant and splice junction start</li> <li>ma_samples - Number of samples carrying the minor allele</li> <li>ma_count - Total count of minor alleles</li> <li>maf - Minor allele frequency</li> <li>pval_nominal - Nominal P-value of the association</li> <li>slope - Slope of the association with respect to alternative (ALT) allele indicated on column 14</li> <li>slope_se - Standard error of the slope</li> <li>pval_nominal_threshold - Nominal P-value significant threshold for thissJunction</li> <li>min_pval_nominal - Most significant P-value observed for this sJunction</li> <li>pval_beta - permutation P-value obtained via beta approximation and later used to calculate Storey q-values</li> <li>junction - sJunction in chr:start-end splice junction format</li> <li>cluster - The splice cluster of this sJunction</li> <li>genes - Genes overlapping with this sJunction (if any), based on GENCODEv24 (AMP-AD) and GENCODEv32 (EADB Belgian)</li> <li>GRCh38_chr_pos - Genomic position of the variant, separated by underscore</li> <li>ref_alt - Reference (REF) and alternative (ALT) allele of the variant, separated by ">" sign. ALT is the tested (A1) allele</li> </ol> Of note, we also mapped the significant eQTLs in the same datasets (please see the data availability section of the manuscript or the GitHub repository). Splicing TWAS reference panels Custom splicing TWAS reference panels prepared using FUSION pipeline (<a href="http://gusevlab.org/projects/fusion/">http://gusevlab.org/projects/fusion/</a>) in AMP-AD brain and EADB Belgian LCL cohorts. All data in hg38/GRCh38 genome build. In each directory, you will find ".pos", ."profile", and ".profile.err" files. These are explained in the FUSION website as well, but briefly these are: <ol> <li>.pos: This is a position file that describes the 1Mb extended splice junction start and end coordinates for each calculated weight file for splice junction phenotype. Used for scanning the variants in those coordinates for TWAS.</li> <li>.profile: This informs about all prediction weights calculated, in terms of number of variants in the model, heritability information, and R2 info for each prediction model used (top1, blup, enet, bslmm, lasso; bslmm was not used therefore has NA values).</li> <li>.profile.err: This summarizes the reference panel in terms of average hsq (with SD), and which model is the best performing.</li> </ol> Each TWAS weight is provided in a .RDat file under All_Splicing_Weights, and in this data we included all calculated functional weights independent of the fact that they are heritable features or not. In our TWAS analyses, we included the heritable functional weights at a hsq P-value ≤ 0.05 level. Please also see the expression TWAS reference panels we prepared in the same datasets (see the data availability section of the manuscript or the GitHub repository). If you need an LD reference data in hg38/GRCh38 genome build based on 1000 Genomes NFE samples (whose variant ID annotation are matching to these functional weights), suitable for running the TWAS/FUSION pipeline, please contact us.

opencc-by-4.0Dec 2020View details →

Reference Corpus of Early New High German (1350–1650)

Das "Referenzkorpus Frühneuhochdeutsch" (ReF) ist ein Korpus diplomatisch transkribierter und annotierter Texte des Frühneuhochdeutschen (1350–1650). Es wurde an den drei Projektstandorten Bochum, Halle und Potsdam erstellt und enthält morphologische (Bochum, Halle) und syntaktische Annotationen (Potsdam). The "Reference Corpus of Early New High German" (ReF) is a corpus of diplomatically transcribed and annotated texts from Early New High German (1350–1650). It was created at the three project sites Bochum, Halle, and Potsdam and contains morphological (Bochum, Halle) and syntactic annotations (Potsdam).

opencc-by-sa-4.0Oct 2021View details →

Dataset of first appearances of the scholarly bibliographic references on English Wikipedia articles as of 1 March 2017 and as of 1 October 2021

Abstract We developed a methodology to detect the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable as the "first appearance of the scholarly reference." We identified the first appearances of 923,894 scholarly references (611,119 unique DOIs) in180,795 unique pages on English Wikipedia as of March 1, 2017, and stored them in the dataset. Moreover, we assessed the precision of the dataset, which was and it was a high precision regardless of the research field. In this version, it is available not only the dataset of English Wikipedia as of March 1, 2017, but also English Wikipedia as of October 1, 2021, generated by using the same methodology.   Data Records The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we detected the first appearance of each scholarly reference added to Wikipedia articles. If there are multiple references corresponding to the same paper on the same page, only the oldest one is collected. Sample of the record is the following. <ul> <li>doi -- DOI corresponding to the paper (String), e.g., "10.1006/anbe.1996.0497"</li> <li>paper_type -- Document type of the paper (String), e.g., "journal-article"</li> <li>paper_container_title -- Journal title, book title, or proceedings title (Array of String), e.g., ["Animal Behaviour"]</li> <li>paper_publisher -- Publisher name (String), e.g., "Elsevier BV"</li> <li>paper_title -- Paper title (Array of String), e.g., ["Push or pull: an experimental study on imitation in marmosets"]</li> <li>paper_published_year -- Published year (String), e.g., "1997"</li> <li>paper_issue -- Issue number (String), e.g., "4"</li> <li>paper_volume -- Volume number (String), e.g., "54"</li> <li>paper_page -- Page numbers (String), e.g., "817-831"</li> <li>paper_author -- Authors information consisted of given and family names, sequences (order in author names), and affiliations (Array of JSON), e.g., [{"given":"THOMAS", "family":"BUGNYAR", "sequence":"first", "affiliation":[]}, {"given":"LUDWIG", "family":"HUBER", "sequence":"additional", "affiliation":[]}]</li> <li>issn -- ISSN related to the paper (Array of String), e.g., ["0003-3472"]</li> <li>research_field -- Research fields from ESI categories (Array of String), e.g., ["PLANT & ANIMAL SCIENCE"]</li> <li>page_id -- Page id (String), e.g., "577858"</li> <li>page_title -- Page title (String), e.g., "Imitation"</li> <li>revision_id -- Revision id (String), e.g., "203309031"</li> <li>revision_timestamp -- Revision timestamp (String), e.g., "2008-04-04 15:54:09 UTC"</li> <li>revision_comment -- Revision comment (edit summary) (String), e.g., "/* Animal Behaviour */"</li> <li>editor_name -- Wikipedia editor's name (String), e.g., "Nicemr"</li> <li>editor_type -- Type of the editor (String), e.g., "User"</li> </ul> References <ul> <li>Kikkawa, J., Takaku, M. & Yoshikane, F. "Dataset of first appearances of the scholarly bibliographic references on Wikipedia articles", Scientific Data, Vol. 9, Article number 85, pp. 1-11, 2022. <a href="https://doi.org/10.1038/s41597-022-01190-z">https://doi.org/10.1038/s41597-022-01190-z</a>.</li> </ul> FUNDING <ul> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12543">JP20K12543</a></li> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K21303">JP21K21303</a></li> </ul>

opencc-by-4.0Oct 2021View details →

Labeled points for four land cover classes referring to the year 2019

This dataset provides hand-labeled points for four different land cover classes in coastal areas (sand, seawater, grass, trees). It was created based on photointerpretation of high-resolution imagery in Google Earth Pro and QGIS, referring to the year 2019. This dataset was used for the random forest classification of satellite imagery in the following manuscript:  "Satellite image processing for the coarse-scale investigation of sandy coastal areas". If you use any part of this dataset, please cite as follows:

opencc-by-4.0Feb 2022View details →

Data Steward Professional: Reference dataset of Data Steward related job vacancies for competences assessment

Data Stewardship vacancies collection to support FAIRsFAIR Data Stewardship Professional Competence Framework This dataset is provided to validate and support the analysis of Data Stewardship competences. The dataset includes a collection of vacancies from the popular job search website <a href="http://indeed.com/">indeed.com</a> that responded to the search term "Data Steward". Acknowledgment The research leading to these results has received funding from the Horizon2020 projects FAIRsFAIR (grant number 831558) References FAIRsFAIR Project Deliverable D7.3 Data Stewardship Professional Competence Framework, Work in Progress. To be published Feb 2021 Yuri Demchenko, Lennart Stoy, Research Data Management and Data Stewardship Competences in University Curriculum, In Proc. Data Science Education (DSE), Special Session, EDUCON2021 – IEEE Global Engineering Education Conference, 21-23 April 2021, Vienna, Austria

opencc-by-4.0Feb 2022View details →

Data for: Hematology and biochemistry of critically endangered radiated tortoises (Astrochelys radiata): reference intervals in previously confiscated subadults and variability based on common techniques

Dataset for: Brenn-White M, Raphael BL, Rakotoarisoa NAT, Deem SL. 2022. Hematology and biochemistry of critically endangered radiated tortoises (Astrochelys radiata): reference intervals in previously confiscated subadults and variability based on common techniques. PLOS One. Dataset is contained in a.radiata_bloodwork.csv. Metadata and data definitions are contained in a.radiate_bloodwork_readme.txt. Dataset contains hematology and biochemistry values of 120 previously confiscated, clinically healthy subadult radiated tortoises living under human care within their native habitat at the Tortoise Conservation Center (TCC), Madagascar. To evaluate the effects of different commonly used techniques on these parameters, we compared results between two venipuncture sites (subcarapacial sinus and brachial vein) and three different WBC quantification methods (Natt and Herrick, LeukopetTM, and slide estimate). Data from tortoises removed from the final analyses due to sample quality issues are also included as frequency of sample quality issues varied by venipuncture site. See associated publication for detailed methods.

opencc-by-4.0Mar 2022View details →

Dataset for Reference-free Isotropic Super-resolution For Volumetric Fluorescence Microscopy

Dataset for a research paper titled "Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy". The images were acquired using two modalities: confocal fluorescence microscopy (CFM) and open-top light-sheet microscopy (OT-LSM). For details about the imaging, please refer to the paper (Link to be uploaded later).  A. CFM <ul> <li>CFM image of a cortical region of a Thy 1-eYFP mouse brain. </li> <li>Lateral resolution estimated as 1.24 micron and Z-depth interval of 3 micron</li> </ul> <ol> <li>Input image ["CFM_input_xy-view.tif] [Figure 2] </li> <li>Reference image acquired by rotating the sample by 90 degrees ["CFM_rotated-and-registered_xz-view.tif'] [Figure 2] </li> </ol> B. OT-LSM  <ul> <li>OT-LSM image of a cortical region of a Thy 1-eYFP mouse brain.</li> <li>Lateral resolution estimated as 0.5 micron and axial resolution estimated as 4.6 micron. </li> <li>For testing of artifact correction, the microscope was poorly calibrated on purpose. </li> </ul> <ol> <li>Input image for artifact correction ["OT-LSM_artifact-correction_input_volume_xy-view.tif"] [Figure 4]</li> <li>Ground-truth image for artificial blurring ["OT-LSM_artificial-blurring_GT.tif"][Supplementary Figure 14]</li> <li>Input image for artificial blurring ["OT-LSM_artificial-blurring_gau-z-blurred-std-10.tif"][Supplementary Figure 14]</li> <li>Input image for PSF deconvolution ["input_volume_PSF-deconvolution.tif"][Figure 3]</li> </ol> C. Simulation  <ul> <li>Jupyter notebook to generate a 3D image volume for simulation ["Data Generator for Simulation.ipynb"] [Figure 1] </li> </ul>

opencc-by-4.0Dec 2020View details →

InpactorDB: A Plant classified lineage-level LTR retrotransposon reference library for free-alignment methods based on Machine Learning

LTR retrotransposons are mobile elements that make up the major part of most plant genomes. Their identification and annotation via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in the variation in genome size, these elements are also associated in the function and structure of different chromosomal regions and in the alteration of the function of coding regions, among others. Several plant retrotransposon sequence databases of LTR retrotransposons are available with public access such as PGSB, RepetDB or restricted access such as Repbase. Although they are useful for approaches to identify LTR-RTs in new genomes by similarity, the elements of these databases are not classified down to the lineage/family level. with great depth.  Here, we present InpactorDB a semi-curated dataset composed of 130,511 elements from 195 plant genomes (belonging to 108 plant species), classified down to the lineage level. This data set has been used to train two deep neural networks (one fully connected and one convolutional) for fast classification of elements. Used in lineage-level classification approaches, we obtain a score above 98% of F1-score, precision and recall.  In order to classify elements of the ‘LTR_STRUC’ and ‘EDTA’ datasets, we used the methodology proposed by Inpactor, which uses homology-based strategy with known coding domains belonging to LTR-RTs. We utilized the RexDB  domain library as reference. LTR-RTs were classified into superfamilies, Gypsy (RLG) or Copia (RLC) and sub-classified into lineages according to the similarities of five different amino acid reference domains (GAG, AP, RT, RNAseH, and INT domains). In addition, we applied filters to remove keep only intact elements: 1) to remove predicted elements with domains from two different superfamilies (i.e. Gypsy and Copia), 2) or elements with domains belonging to two or more different lineages, 3) to remove elements with lengths different than those reported by Gypsy Database (GyDB) with a tolerance of 20%, 4) to delete incomplete elements which has less than three identified domains, and 5) to remove elements with insertions of TE class II (reported in Repbase).  The final non-redundant version of InpactorDB consists of 67,305 LTR retrotransposons. Both redundant and non-redundant versions of InpactorDB are available in Fasta format in which sequences have identifiers with the following general Identification code: >Superfamily-Lineage-plant_family-specie-source-length-ID, Where Superfamily can is either RLC (for Copia) or RLG (for Gypsy), Lineage/family follows following the RexDB nomenclature, source (can be Repbase, RepetDB, PGSB, LTR_STRUC or EDTA datasets), length, and ID, is a unique number which identify each element inside the InpactorDB.