Find research datasets worth reusing

opencc-by-4.0Jun 2020View details →

Data relating to Chiedozie et al. How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England.

Data in .csv format relating to the paper Chiedozie et al. 2020 "How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England." Also contains eTables 5-8 in Excel format, and Stata do file for deriving the DU90% indicator.

opencc-zeroApr 2015View details →

Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)

These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing). Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.

opencc-by-4.0Mar 2021View details →

MESINESP2 Corpora: Annotated data for medical semantic indexing in Spanish

Gold Standard annotations of the MESINESP2 corpora (training, development and test sets).  Please cite this paper if you use this dataset: <pre><code class="language-bash">@inproceedings{gasco2021overview, title={Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials}, author={Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia and Estrada-Zavala, Darryl and Murasaki, Renato Toshiyuki and Primo-Pe{\~n}a, Elena and Bojo Canales, Cristina and Paliouras, Georgios and Krallinger, Martin and others}, year={2021}, organization={CEUR Workshop Proceedings} }</code></pre>   Introduction The main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others.  MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams: <ul> <li>[Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish.</li> <li>[Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from <a href="https://reec.aemps.es/reec/public/web.html">Registro Español de Estudios Clínicos (REEC)</a>. REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC <a href="https://github.com/luisgasco/REECapi">API</a>. </li> <li>[Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”.</li> </ul> In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents.   Files structure: Silver_Standard_Mesinesp2.zip contains two separate sections. On the one hand, the union of the labels of the best model of each participating team as long as this model had obtained at least an F-score of 0.2 (folder join). On the other hand, the predictions of the best models of each participant have been included individually and anonymized (folder separated). This silver standard contains a set of 8642 scientific articles, 1537 text sections from Clinical Practice Guidelines, a set of 8458 text segments from Medication Data Sheets, 461 clinical trials from REEC and 5170 patents.  Subtrack1-Scientific_Literature.zip contains the corpora generated for subtrack 1. Content: <ul> <li>Subtrack1: <ul> <li>Train:  <ul> <li>training_set_track1_all.json: Full training set for subtrack 1. </li> <li>training_set_track1_only_articles.json: Articles training set for subtrack 1.</li> </ul> </li> <li>Development <ul> <li>development_set_subtrack1.json: </li> </ul> </li> <li>Test <ul> <li>test_set_subtrack1.json: Test set for subtrack 1. </li> </ul> </li> </ul> </li> </ul> Subtrack2-Clinical_Trials.zip contains the corpora generated for subtrack 2. Content: <ul> </ul> <ul> <li>Subtrack2: <ul> <li>Train <ul> <li>training_set_subtrack2.json: Training set for subtrack 2.</li> </ul> </li> <li>Development <ul> <li>development_set_subtrack2.json: Manually annotated development set for subtrack 2.</li> </ul> </li> <li>Test <ul> <li>test_set_subtrack2.json: Test set for subtrack 2.</li> </ul> </li> </ul> </li> </ul> Subtrack3-Patents.zip contains the corpora generated for subtrack 3. Content: <ul> </ul> <ul> <li>Subtrack3: <ul> <li>Development <ul> <li>development_set_subtrack3.json: Manually annotated development set for subtrack 3.</li> </ul> </li> <li>Test <ul> <li>test_set_subtrack3.json: Test set for subtrack 3.</li> </ul> </li> </ul> </li> </ul> Additional data.zip contains the corpora with additional data for each subtrack of MESINESP2. DeCS2020.tsv contains a DeCS table with the following structure: <ul> <li>DeCS code</li> <li>Preferred descriptor (the preferred label in the Latin Spanish DeCS 2020 set)</li> <li>List of synonyms (the descriptors and synonyms from  Latin Spanish DeCS 2020 set, separated by pipes.</li> </ul> DeCS2020.obo contains the *.obo file with the hierarchical relationships between DeCS descriptors. *Note: The obo and tsv files with DeCS2020 descriptors contain some additional COVID19 descriptors that will be included in future versions of DeCS. These items were provided by the Pan American Health Organization (PAHO), which has kindly shared this content to improve the results of the task by taking these descriptors into account.   Data format description The input text files for the MESINESP track are JSON files with the following structure: <pre><code class="language-json">{ "articles": [ { "id": "ibc-FGT-907", "title": "Metas de control de la presión arterial e impacto sobre desenlaces cardiovasculares en pacientes con diabetes mellitus tipo 2: un análisis crítico de la literatura", "abstractText": "La hipertensión arterial en individuos con diabetes mellitus tipo2 incrementa el riesgo de eventos cardiovasculares. Las guías internacionales de manejo recomiendan iniciar tratamiento farmacológico con valores de presión arterial >140/90mmHg Sin embargo, no existe un punto de corte óptimo a partir del cual se logre reducir los eventos cardiovasculares sin originar eventos adversos; un rango de presión arterial >130/80 y <140/90mmHg parece ser el adecuado. Estos valores pueden alcanzarse mediante intervenciones no farmacológicas (dieta, ejercicio) y farmacológicas (por fármacos que hayan demostrado reducir eventos cardiovasculares). La elección de uno o varios fármacos debe ser individualizada, de acuerdo con factores como etnia, edad, comorbilidades asociadas, entre otros", "journal": "Clín. investig. arterioscler. (Ed. impr.)", "year": 2019, "db": "IBECS", "decsCodes": [ "D006973", "D000959", "D002318", "D003924", "D012307" ] } ] }</code></pre> MESINESP entity mention files contain automatically generated mention annotations of medications, diseases, syntoms and medical procedures with the following JSON format: <pre><code class="language-json">{ "articles": [ { "id": "ibc-FGT-907", "diseases": [ {"span": "hipertensión arterial", "start": "3", "end": "24"}, {"span": "diabetes mellitus tipo2", "start": "43", "end": "66"}, {"span": "eventos cardiovasculares", "start": "91", "end": "115"}], "medications": [], "procedures": [], "symptoms": []}] } ] }</code></pre>   Dataset description: These corpora contain the data for each of the subtracks of MESINESP2 shared-task: <ul> <li>[Subtrack 1] MESINESP-L – Scientific Literature :   <ul> <li>Training set: It contains all spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish. We have filtered out empty abstracts and non-Spanish abstracts.  We have built the training dataset with the data crawled on 01/29/2021. This means that the data is a snapshot of that moment and that may change over time since LILACS and IBECS usually add or modify indexes after the first inclusion in the database. We distribute two different datasets: <ul> <li>Articles training set: This corpus contains the set of 237574 Spanish scientific papers in VHL that have at least one DeCS code assigned to them.</li> <li>Full training set: This corpus contains the whole set of 249474 Spanish documents from VHL that have at leas one DeCS code assigned to them.</li> </ul> </li> <li>Development set: We provided a development set manually indexed by our expert annotators (not VHL ones). This dataset includes 1065 articles annotated with DeCS by three expert indexers in this controlled vocabulary. The articles were initially indexed by 7 annotators, after analyzing the Inter-Annotator Agreement among their annotations we decided to select the 3 best ones, considering their annotations the valid ones to build the test set. From those 1065 records: <ul> <li>213 articles were annotated by more than one annotator. We have selected de union between annotations.</li> <li>852 articles were annotated by only one of the three selected annotators with better performance.</li> </ul> </li> <li>Test set: We provide a test set containing 491 abstracts from LILACS and IBECS. We used this subset to evaluate the participating systems.</li> </ul> </li> <li>[Subtrack 2] MESINESP-T- Clinical Trials:   <ul> <li>Training set: The training dataset contains records from <a href="https://reec.aemps.es/reec/public/web.html">Registro Español de Estudios Clínicos (REEC)</a>. REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC <a href="https://github.com/luisgasco/REECapi">API</a>. Clinical trials are not indexed with DeCS terminology, we have used as training data a set of 3560 clinical trials that were automatically annotated in the first edition of MESINESP and that were published as a <a href="https://zenodo.org/record/3946558#.YFHyhZ1KiUk">Silver Standard outcome</a>. Because the performance of the models used by the participants was variable, we have only selected predictions from runs with a MiF higher than 0.41, which corresponds with the submission of the best team. </li> <li>Development set: We provide a development set manually indexed by expert annotators. This dataset includes 147 clinical trials annotated with DeCS by seven expert indexers in this controlled vocabulary.</li> <li>Test set: The test dataset contains a collection of 248 items. We used this subset to evaluate the participating systems.</li> </ul> </li> <li>[Subtrack 3] MESINESP-P – Patents:  <ul> <li>Development set: We provide a Development set manually indexed by expert annotators. This dataset includes 115 patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. We have selected these patents based on semantic similarity to the MESINESP-L training set to facilitate model generation and to try to improve model performance.</li> <li>Test set: We provide a test set containing 119 records that correspond to a subset of patents published in Spanish with the IPC codes “A61P” and “A61K31”.Similarly to the development set, we selected these records based on semantic similarity to the MESINESP-L training set. We used this subset to evaluate the participating systems.</li> </ul> </li> <li>Additional data: <ul> <li> We provide this information to the participants as additional data in the “Additional Data” folder. For each training, development, and test set there is an additional JSON file with the structure shown <a href="https://temu.bsc.es/mesinesp2/resources/">here</a>. Each file contains entities related to medications, diseases, symptoms, and medical procedures extrated with the BSC NERs.</li> </ul> </li> </ul>   Summary statistics: <table align="center"> <caption>MESINESP Corpus statistics</caption> <thead> <tr> <th scope="col">MESINESP-L</th> <th scope="col">Docs</th> <th scope="col">DeCS</th> <th scope="col">Unique DeCS</th> <th scope="col">Tokens</th> </tr> </thead> <tbody> <tr> <th scope="row">Training</th> <td>237574</td> <td>1988684</td> <td>22434</td> <td>43106663</td> </tr> <tr> <th scope="row">Development</th> <td>1065</td> <td>11283</td> <td>3750</td> <td>211420</td> </tr> <tr> <th scope="row">Test</th> <td>491</td> <td>5398</td> <td>2124</td> <td>93645</td> </tr> <tr> <th scope="row">Total</th> <td>239130</td> <td>2005365</td> <td>22482</td> <td>43411728</td> </tr> <tr> <th scope="row">MESINESP-T</th> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <th scope="row">Training</th> <td>3560</td> <td>52257</td> <td>3940</td> <td>4133166</td> </tr> <tr> <th scope="row">Development</th> <td>147</td> <td>2038</td> <td>771</td> <td>146791</td> </tr> <tr> <th scope="row">Test</th> <td>248</td> <td>3271</td> <td>905</td> <td>267031</td> </tr> <tr> <th scope="row">Total</th> <td>3955</td> <td>57566</td> <td>4410</td> <td>4546988</td> </tr> <tr> <th scope="row">MESINESP-P</th> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <th scope="row">Development</th> <td>109</td> <td>1092</td> <td>520</td> <td>38564</td> </tr> <tr> <th scope="row">Test</th> <td>119</td> <td>1176</td> <td>629</td> <td>9065</td> </tr> <tr> <th scope="row">Total</th> <td>228</td> <td>2268</td> <td>989</td> <td>47629</td> </tr> </tbody> </table>   <table align="center"> <caption>General MESINESP Corpus statistics</caption> <thead> <tr> <th scope="col">MESINESP</th> <th scope="col">Docs</th> <th scope="col">DeCS</th> <th scope="col">Unique DeCS</th> <th scope="col">Tokens</th> </tr> </thead> <tbody> <tr> <th scope="row">MESINESP-L</th> <td>239130</td> <td>2005365</td> <td>22482</td> <td>43411728</td> </tr> <tr> <th scope="row">MESINESP-T</th> <td>3955</td> <td>57566</td> <td>4410</td> <td>4546988</td> </tr> <tr> <th scope="row">MESINESP-P</th> <td>228</td> <td>2268</td> <td>989</td> <td>47629</td> </tr> <tr> <th scope="row">Total</th> <td>243313</td> <td>2065199</td> <td>22641</td> <td>48006345</td> </tr> </tbody> </table> Related resources: <ul> <li><a href="http://temu.bsc.es/mesinesp2/">MESINESP2 Web</a></li> <li><a href="https://github.com/BioASQ/Evaluation-Measures">Evaluation library</a></li> <li><a href="http://metodologia.lilacs.bvsalud.org/download/E/LILACS-4-ManualIndexacao-es.pdf">Annotation guidelines</a></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhCNKd8zhgD0rLwbhxGqF_wX">Participating teams Youtube Videos</a></li> <li><a href="http://ceur-ws.org/Vol-2936/">Proceedings of BioASQ@CLEF2021</a></li> <li><a href="http://bioasq.org/">BioASQ Web</a></li> </ul>   For further information, please email us at luis.gasco@bsc.es

opencc-by-4.0Apr 2022View details →

Perspectives on Medical Education Journal Data and Supplemental Files (2012 - 2019)

This is the supplemental data, figures, and tables for Joining the meta-research movement: A bibliometric case study of Perspectives on Medical Education.  For Figures 2-4 from the manuscript, to open the network maps, use both the network and map file for each figure in VoS viewer - https://www.vosviewer.com/

opencc-by-4.0Aug 2018View details →

Data and materials for Wallace et al (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: comparison of two prospective cohort studies v1.2

This comprises the data and materials for the study: Wallace E, Moriarty F, McGarrigle C, Smith SM, Kenny RA, Fahey T. (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: Comparison of two prospective cohort studies. PLOS ONE 13(10): e0206201. <a href="https://doi.org/10.1371/journal.pone.0206201">https://doi.org/10.1371/journal.pone.0206201</a> The anonymised TILDA dataset is publicly available to researchers who meet the criteria for access, at no monetary cost, from the Irish Social Science Data Archive (ISSDA) at University College Dublin (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.ucd.ie%2Fissda%2Fdata%2Ftilda%2F&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=%2Fcochi1RuRtYSUa5sF9uA%2BjOOoNYIg7DPpk0mZl5D2s%3D&reserved=0">http://www.ucd.ie/issda/data/tilda/</a>) and the Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.icpsr.umich.edu%2Ficpsrweb%2FICPSR%2Fstudies%2F34315&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=7LHSSqU8xotACMsalpAjVrV5m95DlapgQViyr4P%2FsXY%3D&reserved=0">http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315</a>). For the CPCR cohort, no provision for data sharing was included in the original ethical approval and participant consent form. As a minimal data set necessary to replicate the present study could not be deidentified due to the large number of demographic variables considered, a synthetic version of the study dataset was produced using the synthpop package in R: <a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fsynthpop%2Findex.html&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128229884&sdata=j3If%2FNe%2F1eGsGAt9hyg3ICMqmLec4aOrjRVKppaRSFU%3D&reserved=0">https://cran.r-project.org/web/packages/synthpop/index.html</a>. This dataset and the analytical code for the present study are presented here. Code developed on the synthetic data can be sent to frankmoriarty@rcsi.ie or <a href="mailto:enquiries.cpcr@rcsi.ie">enquiries.cpcr@rcsi.ie</a> to be run on the original data. v1.2 includes a more detailed description of how the dataset was synthesised.

opencc-by-4.0Feb 2019View details →

Data for Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets

Data used in MRI experiments in paper 'Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets'. For each volunteer, breath-hold data (folder bhs) and dynamic free-breathing (folder dyn) data is provided in NIFTI format.

opencc-by-4.0Oct 2024View details →

Supporting data for "Raising awareness of potential biases in medical machine learning: Experience from a Datathon"

This archive contains files from a Datathon held virtually in February 2024 to introduce clinicians and data scientists to the challenge of reviewing a clinical dataset for potential biases.

opencc-by-4.0Aug 2023View details →

Processing, Spectroscopic and Laboratory Testing Data from a Medical Grade Hot-Melt Extrusion Process

This dataset contains a collection of raw processing data, spectroscopic data, and laboratory test results of medical-grade polymer extrusion experiments. The data was collected in several experiments conducted in a hot-melt extrusion process.  The process involved extruding PLA through a slit die and drawing the extruded strands onto spools to obtain the desired dimensional and mechanical properties. The strands were later knitted to form the final medical implant. Throughout the experiments, the extrusion process and equipment were upgraded and refined.  Various operational scenarios were simulated under different nozzle configurations. The experiments start using a single-screw extruder and later progress to a double-screw extruder. Medical Grade PURASORB PLA (PLDLA 96/4) material was used when the hardware upgrades were complete. This dataset contains many variations in experimental conditions. However, enough overlap exists to derive working datasets from this compiled raw data.   Two working datasets have been derived from this compiled raw data. Using a double-screw extruder, both working Datasets investigate polymer degradation in the hot-melt extrusion process. Both derived datasets are included in this collection.   Two Jupyter notebooks are included in this data collection. The first notebook gives an example of how an initial dataset can be derived from the raw data using data science techniques. The second notebook gives an example of how a final dataset can be created from the initial dataset.

opencc-by-4.0Feb 2022View details →

Medical Education Journal Data and Supplemental Files (2000 - 2020)

This is the supplemental data, figures, and tables for The Voices of Medical Education Science: Describing the Landscape. This also includes the author thesaurus and institution thesaurus with supporting read me files.  Abstract  Introduction Medical education has been described as a dynamic and growing field, driven in part by its unique body of scholarship. The voices of authors who publish medical education literature have a powerful impact on the discourses of the community. While there have been numerous studies looking at aspects of this literature, there has been no comprehensive view of recent publications. Method The authors conducted a bibliometric analysis of all articles published in 24 medical education journals published between 2000-2020 to identify article characteristics, with an emphasis on author gender, geographic location, and institutional affiliation. This study replicates and greatly expands on two previous investigations by examining all articles published in these core medical education journals.   Results  The journals published 37,263 articles with the most articles published in 2020 (n=3,957, 10.7%) and least in 2000 (n=711, 1.9%) representing a 456.5% increase. The articles were authored by 139,325 authors of which 62,708 were unique. Men were more prevalent across all authorship positions (n=62,828; 55.7%) than women (n=49,975; 44.3%). Authors listed 154 country affiliations with the United States (n=42,236, 40.4%), United Kingdom (n=12,967, 12.4%), and Canada (n=10,481, 10.0%) most represented. Ninety-three countries (60.4%) were low- or middle-income countries accounting for 9,684 (9.3%) author positions. Few articles were written by multinational teams (n=3,765; 16.2%). Authors listed affiliations with 4,372 unique institutions. Across all author positions, 48,189 authors (46.1%) were affiliated with institutions ranked globally as Top 200 institutions by the Times Higher Education ranking.   Discussion  There is a relative imbalance of author voices in medical education. If the field values a diversity of perspectives, there is considerable opportunity for improvement.

opencc-by-4.0Jun 2022View details →

Fake Medical Data representing examinations from different hospital deprtments

This data set includes examinations for a number of hypothetical patients in different hospital departments. They are organized in folders, one for each department. In each of these folders, a number of subfolders can be found, one for each patient. The files for the actual examinations can be found in the corresponding folder of each patient.

opencc-by-4.0Jul 2022View details →

Imaging data of mechanically loaded, micro-patterned, silk-reinforced cellulose films with gold coating for flexible electrodes in medical implants

Neurodegenerative diseases can be treated using a functional interface between the physically soft tissue such as brain and the man-made electrodes. The orders of magnitude harder neural probes cause local injuries, due to periodic micromovements owing to breathing and pulsatile blood flow leading to encapsulation and related collapsing signals. An alternative to the currently used neural implant films including polyimide, poly(p-xylylene), SU-8 - epoxy-based negative photoresist, liquid crystal polymer, and benzocyclobutene is the natural polymer cellulose with an elastic modulus between 100 and 200 MPa. This article elucidates the measurement of the mechanical properties of bare as well as mono- and double-layer silk-reinforced cellulose in phosphate-buffered saline using a universal testing machine. In addition, the article contains electron microscopy data of these micro-structured, gold-coated films subsequent to peel-off tests to access the impact of micro-structures on gold adhesion on cellulose. These imaging data were completed by electron micrographs of mechanically loaded gold-coated cellulose films to demonstrate the impact of micro-structures on crack formation. Finally, the phosphate-buffered saline-induced swelling of the micro-structure was visualized by electron micrographs obtained before and after two-month storage in air and phosphate-buffered saline, respectively.

dryad40/100

Data from: A novel laboratory method to simulate climatic stress with successful application to experiments with medically relevant ticks

Ticks are the most important vectors of zoonotic disease-causing pathogens in North America and Europe. Many tick species are expanding their geographic range. Although correlational evidence suggests that climate change is driving the range expansion of ticks, experimental evidence is necessary to develop a mechanistic understanding of ticks' response to a range of climatic conditions. Previous experiments used simulated microclimates, but these protocols require hazardous salts or expensive laboratory equipment to manipulate humidity. We developed a novel, safe, stable, convenient, and economical method to isolate individual ticks and manipulate their microclimates. The protocol involves placing individual ticks in plastic tubes, and placing six tubes along with a commercial two-way humidity control pack in an airtight container. We successfully used this method to investigate how humidity affects survival and host-seeking (questing) behavior of three tick species: the lone star tick (Amblyomma americanum), American dog tick (Dermacentor variabilis), and black-legged tick (Ixodes scapularis). We placed 72 adult females of each species individually into plastic tubes and separated them into three experimental relative humidity (RH) treatments representing distinct climates: 32% RH, 58% RH, and 84% RH. We assessed the survival and questing behavior of each tick for 30 days. In all three species, survivorship significantly declined in drier conditions. Questing height was negatively associated with RH in Amblyomma, positively associated with RH in Dermacentor, and not associated with RH in Ixodes. The frequency of questing behavior increased significantly with drier conditions for Dermacentor but not for Amblyomma or Ixodes. This report demonstrates an effective method for assessing the viability and host-seeking behavior of tick vectors of zoonotic diseases under different climatic conditions.

opencc-zeroSep 2022View details →

dryad40/100

Medical interview score data from PostCC-OSCE and programs for an extended many-facet IRT model

Objective structured clinical examinations (OSCEs) are widely used performance assessments for medical and dental students. A common limitation of OSCEs is that the evaluation results depend on the characteristics of raters and the scoring rubric. To overcome this limitation, item response theory (IRT) models such as the many-facet models have been proposed to estimate examinee abilities while accounting for the characteristics of raters and evaluation items in a rubric. However, conventional IRT models have two impractical assumptions: constant rater severity across all evaluation items in a rubric and an equal interval rating scale among evaluation items, which can decrease model fitting and ability measurement accuracy. To resolve this problem, we propose a new IRT model that relaxes these assumptions. We demonstrate the effectiveness of the proposed model by applying it to actual data collected from a medical interview test conducted at Tokyo Medical and Dental University as part of a post-clinical clerkship (PostCC) OSCE. The experimental results showed that the proposed model fit our OSCE data well and measured ability accurately. Furthermore, it provided abundant information on rater and item characteristics that conventional models cannot, helping us to better understand rater and item properties. This dataset includes the actual score data collected from the above-mentioned medical interview test in a PostCC OSCE, as well as the program for estimating the parameters of the proposed IRT model.

opencc-zeroJun 2024View details →

FIGURE 8 in Description of contents of unopened bamboo corsets and crates from Quarry Ig/WJ of the Tendaguru locality (Late Jurassic, Tanzania, East Africa) as revealed by medical CT data and the potential of this data under paleontological and historical aspects

FIGURE 8. Examples of bone elements from sauropod dinosaurs found in the bamboo corsets, link to corresponding movies. A) "Ig 230", cervical rib of Giraffatitan brancai, and cluster of vertebrae and bone fragments of Dysalotosaurus; B) "Ig 281", presumable dorsal vertebral fragments and pneumatic transverse process of a sauropod (this bamboo corset contains also a dentary remain of Dysalotosaurus); C) "Ig 310, 311, 312, 313, 314", pneumatic transverse process of dorsal vertebra; D) "Ig 498", neural arch with clearly visible hyposphene and broken neural spine of titanosauriform sauropod. Close-up of visualization of isolated MtII of Giraffatitan, E) in dorsal view F) in plantar view. Scale bar in A-D is 100 mm, in E-F it is 50 mm. Abbreviations: bf, bone fragment; ccost, corpus of cervical rib (of Giraffatitan); df, dentary fragment (of Dysalotosaurus); hypo, hyposphene; mt, metatarsal; na, neural arch; nsp, neural spine; prcost, caudal process of cervical rib (of Giraffatitan); postzyg, postzygapophysis; prtrans, transverse process of vertebra; tib,tibia; vc, vertebral centrum; vf, vertebral fragment. Videos of A) to D) available at the PE You Tube channel (https://www.youtube.com/channel/UCF6IBDiGbut- DrVada60Izyg)

FIGURE 6 in Description of contents of unopened bamboo corsets and crates from Quarry Ig/WJ of the Tendaguru locality (Late Jurassic, Tanzania, East Africa) as revealed by medical CT data and the potential of this data under paleontological and historical aspects

FIGURE 6. Examples of bone elements from Dysalotosaurus lettowvorbecki as found in the bamboo corsets, link to corresponding movies. A) "Ig 189, 211, 213", bamboo corset with dentary fragments, dorsal rib shaft, tibia remains, a proximal fibula and some other bone fragments; B) "Ig 401, 415, 416, 417, 472, 475", bamboo corset with well-preserved ilium, proximal fibula, at least two metatarsals and other long bone fragments, as well as a presumed MtII of Giraffatitan brancai in 2 parts; C) "Ig 343, 346, 354, 359, 366, 383", bamboo corset with femur in 3 parts, fragments of ilium and some bone fragments; D) "Ig 335, 336, 338, 368", bamboo corset with 1 humerus, 1 femur, tibia and fibula as proximal parts and other bone fragments; E) "Ig 537, 540, 547, 549, 562", bamboo corset with vertebral centra, 2 isolated neural arches, a coracoid, a distal femur, a metatarsal and other bone fragments; F) "Ig 522, 527, 528, 539, 543, 544", bamboo corset with tibia and fibula, ulna and other bone fragments. Scale bar is 50 mm. Abbreviations: cor, coracoid; df, dentary fragment; dori, dorsal rib; fe, femur; fib, fibula; hu, humerus; ili, ilium; isc, ischium; MtII, second metatarsal of Giraffatitan; mt, metatarsal (of Dysalotosaurus); nar, neural arch; tib, tibia; uln, ulna; vc, vertebral centrum. Videos of A) to F) available at the PE You Tube channel (https://www.youtube.com/channel/UCF6IBDiGbut- DrVada60Izyg)

FIGURE 5. Diagrams showing A in Description of contents of unopened bamboo corsets and crates from Quarry Ig/WJ of the Tendaguru locality (Late Jurassic, Tanzania, East Africa) as revealed by medical CT data and the potential of this data under paleontological and historical aspects

FIGURE 5. Diagrams showing A) the distribution of clay jackets, unprepared bones and tin cans in the bamboo corsets and crates, B) Frequency of different bone elements of Dysalotosaurus lettowvorbecki in the bamboo corsets and crates. The unidentified bone fragments of Dysalotosaurus are not incorporated in this count. Isolated vertebral centra have been counted without determination of their corresponding body region (i.e., cervical, dorsal, sacral, or caudal).

FIGURE 4 in Description of contents of unopened bamboo corsets and crates from Quarry Ig/WJ of the Tendaguru locality (Late Jurassic, Tanzania, East Africa) as revealed by medical CT data and the potential of this data under paleontological and historical aspects

FIGURE 4. Packing types of fossil specimens as visible in the CT images A) "Ig 420, 439, 453, 512", longitudinal section (MIP mode and CT-pulmonary filters set) showing wrapping of specimens in savanna grass, B) "Ig88" cross-section showing wrapping of fossils in savanna grass and tight stuffing of bamboo corsets, C) "Ig291, 291", longitudinal section of the typical clay jackets, showing a femur of Dysalotosaurus in pieces with protection cover of clay, D) "Ig281", partial longitudinal section showing clay jackets and single bone fragments with and without sediment, E) "Ig 122, 124, 266, 267, 269, 270, 272, 276", longitudinal section showing clusters of vertebrae and bone fragments in the bamboo corset, F) "Ig_2011_4", crate with two large sediment slabs with fossil bones, G) "Ig_2011_1", crate with bamboo stalks filled with fossil bones and bones wrapped in savanna grass, H) "Ig_NN6", bamboo corset in longitudinal section (MIP mode) showing tin cans and bone cluster in between (see also Figure 4C-D), I) "Ig 330, 339, 347, 349 350, 351, 352, 356, 360, 346, 366", bamboo corset in longitudinal section (MIP mode) showing several tin cans and some additional loose bones. Scale bar is 50 mm. Abbreviations: bb, bamboo stalk; bf, bone fragment; cl, clay cover of bone; clj, clay jacket; df, dentary fragment; dife, distal femur; sed, sediment; sgb, savanna grass bundle; vc, vertebral centrum.

FIGURE 3. 3D in Description of contents of unopened bamboo corsets and crates from Quarry Ig/WJ of the Tendaguru locality (Late Jurassic, Tanzania, East Africa) as revealed by medical CT data and the potential of this data under paleontological and historical aspects

FIGURE 3. 3D visualization images of containers, showing different content types, link to corresponding movies. A) "Ig88", clay jackets and bones; B) "Ig_2011_5", crate with an accumulation of vertebrae and some other bones. Scale bars in A) and B) are 50 mm. "Ig_NN6", tin cans and small bones in bamboo corset, 3D MIP videos, C) with bamboo corset and surface visualized, D) with content of tin cans visualized. Scale bars in C) and D) are 100 mm. Abbreviations: bc, 3D visualized bamboo corset; bf, bone fragment; clvc, cluster of vertebral centra; clj, clay jacket; tc, tin can; vc, vertebral corpus. Videos of A), B), C) and D) available at the PE You Tube channel (https://www.youtube.com/channel/UCF6IBDiGbut- DrVada60Izyg).