Find research datasets worth reusing

openGEO-OpenApr 2022View details →

Epigenetic changes induced by in utero dietary challenge result in phenotypic variability in successive generations of mice.

GEO Series GSE175538. Mus musculus. 157 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.

openGEO-OpenSep 2013View details →

Expression data from peripheral blood - blood draws at Pre and Post time points of Allergen inhalation challenge (ER and DR)

GEO Series GSE40240. Homo sapiens. 28 samples. Type: Expression profiling by array.

openGEO-OpenFeb 2008View details →

Inflammatory response of trout head kidney to in vivo challenge with IHN virus and LPS

GEO Series GSE10272. Oncorhynchus mykiss. 12 samples. Type: Expression profiling by array.

openGEO-OpenJan 2023View details →

NanoString nCounter Gene Expression of keratinocytes pretreated with HSP90 inhibitor and challenged with 12-O-tetradecanoylphorbol-13-acetate (TPA)

GEO Series GSE221998. Homo sapiens. 24 samples. Type: Expression profiling by array.

openGEO-OpenOct 2012View details →

Peripheral blood gene expression changes during allergen inhalation challenge in atopic asthmatic individuals

GEO Series GSE24745. Homo sapiens. 30 samples. Type: Expression profiling by array.

openGEO-OpenJun 2012View details →

Expression data from mouse lungs, exposed in-utero to second-hand smoke (SHS) and challenged with ovalbumin (OVA) as adults.

GEO Series GSE38409. Mus musculus. 16 samples. Type: Expression profiling by array.

opennotspecifiedJan 2020View details →

FIGS 2–4 in A new subgenus for "Acalathus" advena (LeConte, 1846) and the challenge of defining Calathina based on morphological characters (Coleoptera, Carabidae Sphodrini)

FIGS 2–4. Calathus (Certocalathus) advena (LeConte, 1848). Fig. 2: female genital tract, ventral view; Fig. 3: gonocoxites, ventral view; Fig. 4: aedeagus, dorsal view. Abbreviations: bc: bursa copulatrix; co: common oviduct; ds: dorsal ensiform seta; es: external ensiform setae; gc 1: basal gonocoxite; gc 2: apical gonocoxite; sp: spermatheca; sp-gl: spermathecal gland; spit: sensory pit; pm-l: left aedeagal paramere; pm-r: left paramere; vg: vagina.

opencc-by-4.0Dec 2023View details →

Accountability of Software Intensive Systems: challenges for systems-of-information systems

Open the record for dataset details and reuse information.

opennotspecifiedNov 2023View details →

Figure 30 in What is a 'strong' synapomorphy? Redescriptions of Murray's type species and descriptions of new taxa challenge the systematics of Hypsibiidae (Eutardigrada: Parachela)

Figure 30. Detailed morphology of Platicrista nivea sp. nov. (PCM): A, buccopharyngeal apparatus in toto (paratype); B, claws II (holotype). Scale bars are in micrometres.

opennotspecifiedJan 2011View details →

Figure 8 in The radiation of Satyrini butterflies (Nymphalidae: Satyrinae): a challenge for phylogenetic methods

Figure 8. Optimization of habitat use (forest and non-forest) onto the phylogenetic tree from the BEAST analysis.

opennotspecifiedJan 2011View details →

Figure 1 in The radiation of Satyrini butterflies (Nymphalidae: Satyrinae): a challenge for phylogenetic methods

Figure 1. The eight different biogeographical regions used in this study for the dispersal–vicariance analysis (DIVA). A, Western Palaearctic. B, Eastern Palaearctic. C, Southeast Asia. D, Africa. E, Neotropics. F, Nearctic. G, Central America. H, Australia.

restrictedcc-by-4.0Apr 2022View details →

Training dataset for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022

Training dataset for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022.  Please refer to our website: https://vessel-wall-segmentation-2022.grand-challenge.org/.

openodc-byDec 2018View details →

ASVspoof 2019: The 3rd Automatic Speaker Verification Spoofing and Countermeasures Challenge database

This is a database used for the Third Automatic Speaker Verification Spoofing and Countermeasures Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org) organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman, and Andreas Nautsch in 2019. The ASVspoof challenge aims to encourage further progress through (i) the collection and distribution of a standard dataset with varying spoofing attacks implemented with multiple, diverse algorithms and (ii) a series of competitive evaluations for automatic speaker verification. The ASVspoof 2019 challenge follows on from three special sessions on spoofing and countermeasures for automatic speaker verification held during INTERSPEECH 2013, 2015, and 2017. While the first edition in 2013 was targeted mainly at increasing awareness of the spoofing problem, the 2015 edition included the first challenge on the topic, accompanied by commonly defined evaluation data, metrics and protocols. The task in ASVspoof 2015 was to design countermeasure solutions capable of discriminating between bona fide (genuine) speech and spoofed speech produced using either text-to-speech (TTS) or voice conversion (VC) systems. The ASVspoof 2017 challenge focused on the design of countermeasures aimed at detecting replay spoofing attacks that could, in principle, be implemented by anyone using common consumer-grade devices. The ASVspoof 2019 challenge extends the previous challenge in several directions. The 2019 edition is the first to focus on countermeasures for all three major attack types, namely those stemming from TTS, VC and replay spoofing attacks. Advances with regards to the 2015 edition include the addition of up-to-date TTS and VC systems that draw upon substantial progress made in both fields during the last four years. ASVspoof 2019 thus aims to determine whether the advances in TTS and VC technology post a greater threat to automatic speaker verification and the reliability of spoofing countermeasures. Advances with regards to the 2017 edition concern the use of a far more controlled evaluation setup for the assessment of replay spoofing countermeasures. Whereas the 2017 challenge was created from the recordings of real replayed spoofing attacks, the use of an uncontrolled setup made results somewhat difficult to analyse. A controlled setup, in the form of replay attacks simulated using a range of real replay devices and carefully controlled acoustic conditions is adopted in ASVspoof 2019 with the aim of bringing new insights into the replay spoofing problem. Last but not least, the 2019 edition aligns ASVspoof more closely with the field of automatic speaker verification. Whereas the 2015 and 2017 editions focused on the development and assessment of stand-alone countermeasures, ASVspoof 2019 adopts for the first time a new ASV-centric metric in the form of the tandem decision cost function (t-DCF). The ASVspoof 2019 database encompasses two partitions for the assessment of logical access (LA) and physical access (PA) scenarios. Both are derived from the VCTK base corpus which includes speech data captured from 107 speakers (46 males, 61 females). Both LA and PA databases are themselves partitioned into three datasets, namely training, development and evaluation which comprise the speech from 20 (8 male, 12 female), 10 (4 male, 6 female) and 48 (21 male, 27 female) speakers respectively. The three partitions are disjoint in terms of speakers, and the recording conditions for all source data are identical. While the training and development sets contain spoofing attacks generated with the same algorithms/conditions (designated as known attacks), the evaluation set also contains attacks generated with different algorithms/conditions (designated as unknown attacks). Reliable spoofing detection performance therefore calls for systems that generalise well to previously-unseen spoofing attacks. Full descriptions are available in the ASVspoof 2019 evaluation plan. ## Codec ## The original waveform format is PCM and compressed using Flac. No telephone or mobile codec was used.

opennotspecifiedMar 2021View details →

Fig. 1 in Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)

Fig. 1. Phylogeny of Molurina imposed with new generic classification. Maximum likelihood topology from IQ-Tree obtained in the analysis of the concatenated CAD, wg, COI JP, COI BC, COII, and 28S matrix. Posterior probabilities are displayed above branches, while the ultrafast IQ-Tree bootstrap values are shown below (black circles indicate a full support of either 1.00 or 100). Representatives of Molurina: (A) Psammodes pinguis—holotype; (B) Dichtha cubica (voucher TB14720); (C) Brachyphrynus abyssinicus—syntype; (D) Physophrynus burdoi; (E) Ocnodes cf. scrobricollis (voucher TB19987); (F) Ocnodes sericicollis— paratype; (G) Psammoryssus titanus; (H) Huilamus welwitschi—mt: metatibia; (I) Stridulomus sulcicollis—ep: epipleuron.

embargoedcc-by-4.0Jan 2024View details →

SynthRAD2023 Grand Challenge test dataset: synthetizing computed tomography for radiotherapy

<div> DATASET STRUCTURE The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/10.5281/zenodo.10514185</a> and a detailed description is offered at "synthRAD2023_dataset_description.pdf". The test datasets for Task1 is in Task1.tar.zst, while for Task2 in Task2.tar.zst. After unzipping, each Task is organized according to the following folder structure: Task1 └── mr │   ├── brain │   │   ├── 1BA003 │   │   │   ├── mask.nii.gz │   │   │   └── mr.nii.gz │   │   ├── 1BA029 │   │   │   ├── mask.nii.gz │   │   │   └── mr.nii.gz │   │   ├── ... │   └── pelvis │       ├── 1PA003 │       │   ├── mask.nii.gz │       │   └── mr.nii.gz │       ├── 1PA006 │       │   ├── mask.nii.gz │       │   └── mr.nii.gz │       ├── ... ├── ct │   ├── 1BA003.nii.gz │   ├── 1BA029.nii.gz │   ├── 1BA063.nii.gz │   ├── ... ├── doseplanning │   ├── 1BA003 │   │   ├── 1BA003_RTplan_photons.mat │   │   └── 1BA003_RTplan_protons.mat │   ├── 1BA029 │   │   ├── 1BA029_RTplan_photons.mat │   │   ├── 1BA029_RTplan_protons.mat │   ├── ... Task2 ├── cbct │   ├── brain │   │   ├── 2BA019 │   │   │   ├── cbct.nii.gz │   │   │   └── mask.nii.gz │   │   ├── 2BA021 │   │   │   ├── cbct.nii.gz │   │   │   └── mask.nii.gz │   │   ├── ... │   └── pelvis │       ├── 2PA022 │       │   ├── cbct.nii.gz │       │   └── mask.nii.gz │       ├── 2PA023 │       │   ├── cbct.nii.gz │       │   └── mask.nii.gz │       ├── ... ├── ct │   ├── 2BA019.nii.gz │   ├── 2BA021.nii.gz │   ├── 2BA022.nii.gz │   ├── .... ├── doseplanning │   ├── 2BA019 │   │   ├── 2BA019_RTplan_photons.mat │   │   └── 2BA019_RTplan_protons.mat │   ├── 2BA021 │   │   ├── 2BA021_RTplan_photons.mat │   │   └── 2BA021_RTplan_protons.mat │   ├── ... Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below: <table> <tbody> <tr> <td>[Task]</td> <td>[Anatomy]</td> <td>[Center]</td> <td>[PatientID]</td> </tr> <tr> <td>1</td> <td>B</td> <td>A</td> <td>001</td> </tr> </tbody> </table> The zip contains the following structure:  <ul> <li> ct/<patient code>.nii.gz: The gold-standard CT image. </li> <li> doseplanning/<patient code>/<patient code>_RTplan_[photons/protons].mat: The matRad photon and proton doseplans for this particular patient.  </li> <li> [mr/cbct]/[brain/pelvis]/<patient code>/[mr/cbct].nii.gz: The corresponding CBCT or MR image. </li> <li> [mr/cbct]/[brain/pelvis]/<patient code>/mask.nii.gz: image containing a binary mask of the dilated patient outline. </li> </ul> DATASET DESCRIPTION This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks. Data was collected at 3 Dutch university medical centers: <ul> <li> Radboud University Medical Center </li> <li> University Medical Center Utrecht </li> <li> University Medical Center Groningen </li> </ul> For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to. The following number of patients is available in the training set. Training <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> <td> 120 </td> <td> 0 </td> <td> 60 </td> <td> 180 </td> </tr> <tr> <td> Task 2 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> </tr> </tbody> </table> Each subset generally contains equal amounts of patients from each center, except for task 1 brain, where center B had no MR scans available. To compensate for this, center A provided twice the number of patients than in other subsets. Validation <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 20 </td> <td> 0 </td> <td> 10 </td> <td> 30 </td> </tr> <tr> <td> Task 2 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> </tr> </tbody> </table> Testing <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> <td> 40 </td> <td> 0 </td> <td> 20 </td> <td> 60 </td> </tr> <tr> <td> Task 2 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> </tr> </tbody> </table> In total, for all tasks and anatomies combined, 1080 image pairs (720 training, 120 validation, 240 testing) are available in this dataset. This repository only contains the test data. All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure). Data was acquired with the following scanners: <ul> <li> Center A: <ul> <li> MRI: Philips Ingenia 1.5T/3.0T </li> <li> CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT </li> <li> CBCT: Elekta XVI </li> </ul> </li> <li> Center B: <ul> <li> MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T </li> <li> CT: Siemens SOMATOM Definition AS </li> <li> CBCT: IBA Proteus+ or Elekta XVI </li> </ul> </li> <li> Center C: <ul> <li> MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T </li> <li> CT: Philips Brilliance Big Bore </li> <li> CBCT: Elekta XVI </li> </ul> </li> </ul> For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast. For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT. The following pre-processing steps were performed on the data: <ul> <li> Conversion from dicom to compressed nifti (nii.gz) </li> <li> Rigid registration between CT and MR/CBCT </li> <li> Anonymization (face removal, only for brain patients) </li> <li> Patient outline segmentation (provided as a binary mask) </li> <li> Crop MR/CBCT, CT and mask to remove background and reduce file sizes </li> </ul> The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics. ETHICAL APPROVAL Each institution received ethical approval from their internal review board/Medical Ethical committee: <ul> <li> UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”. </li> <li> UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”. </li> <li> Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”. </li> </ul> CHALLENGE DESIGN The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746020">https://doi.org/10.5281/zenodo.7746020</a>.  <h2>Notes</h2> <div>FUNDING BODIES: The challenge has been funded thanks to the support of the Seed Fund provided by the " EWUU Alliance TU/e, WUR, UU, UMCU" https://ewuu.nl/en/collaboration/seed-fund/.</div> </div>

restrictedcc-by-4.0Jun 2024View details →

Roles and Current Challenges of Peer Educators in delivering HIV/AIDS-related services in Vietnam: a dataset

The file contains raw data from a survey on the roles of peer educators (PE) in delivering HIV/AIDS services in Vietnam. The study was conducted in seven large provinces in Vietnam from December 2021 to March 2022. An online cross-sectional survey was administered to 63 health staff and 147 PEs to collect quantitative data on PEs’ roles.

Fig. 1 in Two Pione species (Hadromerida, Clionaidae) from the Red Sea: a taxonomical challenge

Fig. 1 Study area on the South Sinai peninsula; sampling sites: Ras Nosrani (27°58′3.4″N, 34° 25′15.7″E), Camping Site (27° 47′19.4″N, 34°13′29.3″E), Marsa Bareka (27°46′1.2″N, 34° 13′11.4″E), Mangrove Island (27°43′26.0″N, 34°15′0.5″E)

Fig. 6 Bayesian 50 in Two Pione species (Hadromerida, Clionaidae) from the Red Sea: a taxonomical challenge

Fig. 6 Bayesian 50% majority rule phylogram based on CO1 sequences, reconstructed by MrBayes. Bayesian posterior probabilities shown above branches indicate, maximum-likelihood bootstrap values below branches. Haplotypes 1–3 from the Red Sea, other sequences from the Caribbean or Western Australia. For each Red Sea sequence, sample number and GenBank accession number are given. Tree has been submitted to TreeBASE (www.treebase.org) under accession number SN4780

Fig. 4 in Two Pione species (Hadromerida, Clionaidae) from the Red Sea: a taxonomical challenge

Fig. 4 Results of PCA analysis based on mean spicule lengths and widths of the two supposed species. Each triangle represents one specimen (only specimens having all three spicule types considered; see Table 1). First axis (PC1) explains 78.7% of variation among individuals

Fig. 5 in Two Pione species (Hadromerida, Clionaidae) from the Red Sea: a taxonomical challenge

Fig. 5 Discriminant analysis based on tylostyle dimensions. On horizontal axis, "0" is midpoint between means of discriminant scores of the two groups