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3,172 results for “public”
Data for publication "Measuring the environment of a Cs qubit with dynamical decoupling sequences"
<p>Data sets as plotted in the preprint "Measuring the environment of a Cs qubit with dynamical decoupling sequences" are uploaded.<br> The zip file "data" contains a folder for each figure in the preprint (named after the figure). Each folder contains the data for all graphs in the respective figure.</p>
Data and Code for Publication "Inferring human neutral genetic variation from craniodental phenotypes"
<p>Data and code for publication: H. Rathmann et al., Inferring human neutral genetic variation from craniodental phenotypes. PNAS Nexus.</p> <p>The repository contains:</p> <ul> <li>“<em>R code for DP-DG analysis.txt</em>”: R code for testing levels of neutral evolutionary signals preserved in five craniodental data types: cranial metrics, dental metrics, cranial non-metric traits, dental non-metric traits, and craniodental metrics and non-metric traits combined.</li> </ul> <ul> <li>“<em>Cranial metric data.csv</em>”: Dataset consisting of 37 cranial metric variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by T. Hanihara and originally presented in the publication titled: T. Hanihara, Comparison of craniofacial features of major human groups. <em>Am. J. Phys. Anthropol.</em> 99, 389–412 (1996) (<a href="https://doi.org/10.1002/(SICI)1096-8644(199603)99:3%3c389::AID-AJPA3%3e3.0.CO;2-S">https://doi.org/10.1002/(SICI)1096-8644(199603)99:3<389::AID-AJPA3>3.0.CO;2-S</a>).</li> </ul> <ul> <li>“<em>Dental metric data.csv</em>”: Dataset comprising 28 dental metric variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by T. Hanihara and originally presented in the publication titled: T. Hanihara, H. Ishida, Metric dental variation of major human populations. <em>Am. J. Phys. Anthropol.</em> 128, 287–298 (2005) (<a href="https://doi.org/10.1002/ajpa.20080">https://doi.org/10.1002/ajpa.20080</a>).</li> </ul> <ul> <li>“<em>Cranial non-metric trait data.csv</em>”: Dataset consisting of 24 cranial non-metric trait variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected for the most part by T. Hanihara and presented in the publication titled: T. Hanihara, H. Ishida, Y. Dodo, Characterization of biological diversity through analysis of discrete cranial traits. <em>Am. J. Phys. Anthropol.</em> 121, 241–251 (2003) (<a href="https://doi.org/10.1002/ajpa.10233">https://doi.org/10.1002/ajpa.10233</a>).</li> </ul> <ul> <li>“<em>Dental non-metric trait data.csv</em>”: Dataset comprising 25 dental non-metric trait variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by C. G. Turner II, G. R. Scott, and J. D. Irish. This individual-level dataset was artificially created from population-level trait frequency information presented in the publications: G. R. Scott, J. D. Irish, <em>Human Tooth Crown and Root Morphology </em>(Cambridge University Press, 2017) (<a href="https://doi.org/10.1017/9781316156629">https://doi.org/10.1017/9781316156629</a>); and: J. D. Irish, A. Morez, L. Girdland Flink, E. L. W. Phillips, G. R. Scott, Do dental nonmetric traits actually work as proxies for neutral genomic data? Some answers from continental- and global-level analyses. <em>Am. J. Phys. Anthropol. </em>172, 347–375 (2020) (<a href="https://doi.org/10.1002/ajpa.24052">https://doi.org/10.1002/ajpa.24052</a>).</li> </ul> <ul> <li>“<em>SNP data.txt</em>”: Dataset comprising 8,821 SNP markers for 26 worldwide modern populations, provided in a genepop file format. The data were obtained from various published sources: I. Lazaridis et al., Ancient human genomes suggest three ancestral populations for present-day Europeans. <em>Nature </em>513, 409–413 (2014) (<a href="https://doi.org/10.1038/nature13673">https://doi.org/10.1038/nature13673</a>); P. Qin, M. Stoneking, Denisovan ancestry in east Eurasian and native American populations. <em>Mol. Biol. Evol. </em>32, 2665–2674 (2015) (<a href="https://doi.org/10.1093/molbev/msv141">https://doi.org/10.1093/molbev/msv141</a>); P. Skoglund et al., Genomic insights into the peopling of the Southwest Pacific. <em>Nature </em>538, 510–513 (2016) (<a href="https://doi.org/10.1038/nature19844">https://doi.org/10.1038/nature19844</a>); M. R. Nelson et al., The Population Reference Sample, POPRES: a resource for population, disease, and pharmacological genetics research. <em>Am. J. Hum. Genet. </em>83, 347–358 (2008) (<a href="https://doi.org/10.1016/j.ajhg.2008.08.005">https://doi.org/10.1016/j.ajhg.2008.08.005</a>); J. K. Pickrell, J. K. Pritchard, Inference of population splits and mixtures from genome-wide allele frequency data. <em>PLoS Genet. </em>8, e1002967 (2012) (<a href="https://doi.org/10.1371/journal.pgen.1002967">https://doi.org/10.1371/journal.pgen.1002967</a>); A. Bergström et al., Insights into human genetic variation and population history from 929 diverse genomes. <em>Science </em>367 (2020) (<a href="https://doi.org/10.1126/science.aay5012">https://doi.org/10.1126/science.aay5012</a>); B. M. Henn et al., Genomic ancestry of North Africans supports back-to-Africa migrations. <em>PLoS Genet. </em>8, e1002397 (2012) (<a href="https://doi.org/10.1371/journal.pgen.1002397">https://doi.org/10.1371/journal.pgen.1002397</a>); S. Mallick et al., The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. <em>Nature </em>538, 201–206 (2016) (<a href="https://doi.org/10.1038/nature18964">https://doi.org/10.1038/nature18964</a>); Lao et al., Correlation between genetic and geographic structure in Europe. <em>Curr. Biol. </em>18, 1241–1248 (2008) (<a href="https://doi.org/10.1016/j.cub.2008.07.049">https://doi.org/10.1016/j.cub.2008.07.049</a>); and M. Lipson et al., Population Turnover in Remote Oceania Shortly after Initial Settlement. <em>Curr. Biol. </em>28, 1157-1165.e7 (2018) (<a href="https://doi.org/10.1016/j.cub.2018.02.051">https://doi.org/10.1016/j.cub.2018.02.051</a>).</li> </ul> <p>For population and variable names and abbreviations, see Supplementary Information in: H. Rathmann et al., Inferring human neutral genetic variation from craniodental phenotypes. PNAS Nexus.</p>
Associated data underlying the publication "Flipped Classroom Real-World Activities for Learning Open Computing Concepts"
<p>Various “open” concepts in Computing, such as open standards, open data, open licenses or system interoperability are becoming more important in the professional lives of software engineers. However, students usually do not receive a systematic education about these concepts ; rather they sporadically learn about a subset of these topics. This paper presents the revised version of the Open Computing course in the University of Zagreb, Faculty of Electrical Engineering and Computing (FER), which teaches a clear set of current topics focused on open data and correlating concepts. The new e-learning course is carried out using the “flipped classroom” educational method ; students construct their knowledge in a set of real-world mini-activities throughout the course, instead of passively learning from the official course resources. In this paper, we discuss our flipped classroom activities, their relation to revised Bloom’s taxonomy of educational objectives, and present the evaluation of the first course instance us- ing this model. Students’ feedback shows they welcome this change in approach, finding the course useful and interesting, and preferring this method to the traditional full lecture setting.</p>
Data from: Business and publication model of surgical journals: A bibliometric analysis
<p>The dataset contains information about surgical journals included in the SCOPUS database used in our research. The following information is present in the data file:</p> <ol> <li>Title</li> <li>Journal sub-specialty</li> <li>Country Origin</li> <li>Continent</li> <li>SJR</li> <li>Publisher</li> <li>Types of Publisher</li> <li>Publication Model</li> <li>Language</li> </ol>
Digitized geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy) collected from public sources.
<p>The database is a supplementary material of:</p><p>Livani, M., Petracchini, L., Benetatos, C., Marzano, F., Billi, A., Carminati, E., Doglioni, C., Petricca, P., Maffucci, R., Codegone, G., Rocca, V., Antoncecchi, I. (2023). Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). Earth System Science Data Discussions, 15, n. 9, 4261-4293, doi: 10.5194/essd-15-4261-2023</p><p> </p><p>This database comprises subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). We realized the database by collecting, revising, and digitizing data, originally in raster format, from public sources. These data have been then used to reconstruct the overall subsurface 3D architecture and to extract the physical properties of the subsurface geological units.</p><p>The data have a common geographical system: WGS 84/UTM zone 32N; EPSG: 32632.</p><p>The database contains borehole information from 160 deep wells (i.e., wellhead coordinates, rotary table elevation, measured depth, true depth, total depth and deviation survey) and digitized Spontaneous Potential, Gamma Ray, and Sonic logs. Five horizons were digitized from 61 geological cross-sections and from 10 isobath maps that roughly correspond to the boundaries of units showing different lithological properties and with different mechanical properties. The horizons are, from the oldest to the youngest: the top of the magnetic basement, the top of the carbonate succession, the base of the Pliocene, the base of the Calabrian and the base of recent continental deposits. In addition, the gridded surfaces of the 3D geological model are available.</p><p>We organized the database into two groups: "primitive data" and "derived data".</p><p>Primitive data are the result of the digitization of public data. The database of the primitive data is formed by the main horizons reported in the geological cross-sections, the isobaths of the main geological surfaces, the well locations, comprised their trajectory along depth, lithological and stratigraphical information, and geophysical logs from composite well logs. Well data include specific sets of well logs aimed to geological/mechanical characterization of the geological units (e.g., Spontaneous Potential log, Resistivity log, Gamma Ray log, and Sonic log).</p><p>Derived data consist of two datasets: i) the primitive isobaths maps and geological cross-sections data that have been filtered and verified after a data accuracy analysis performed to unravel discrepancies in the interpretation of the subsurface geological horizons; ii) a set of regional surfaces of the main geological units of the Po Plain subsurface. These surfaces were generated by interpolating the filtered primitive data and without considering the fault occurrence/displacements.</p><p>Primitive and derived data are provided in delimited text file format organized according to the data type (i.e., well, geological cross-section, map and gridded surface). The format and the organization of data are explained in the related "readme" file presents within each data folder.</p><p>Our database represents a collection of the main published works regarding the Po Plain. Detailed studies related to specific sectors of the Po Plain might not be present in our database.</p><p>Further details about the data processing and organization are given in the related manuscript.</p><p> </p>
Number of publications in French for the winners of the Nobel Prize for Literature (extraction of the BnF catalog)
<p>Python script and dataset.</p> <p>The dataset contains the number of publications in French one can find in the catalog of the Bibliothèque nationale de France (BnF) for every winner of the Nobel Prize of Literature.</p> <p>Idea from LIBER : <a href="https://twitter.com/kiru/status/1677413039132352512">https://twitter.com/kiru/status/1677413039132352512</a></p>
Related with: Long-short term memory prediction of user's locomotion in Virtual Reality publication (Dataset)
<p>Dataset: Captured motion data from 44 users.</p> <p>Scenes:</p> <p>SL -> Scene Lab.</p> <p>SR -> Escape Room.</p> <p>MF -> Shooter forest.</p> <p>Since it is recorded inside a game engine and all records take place inside their processing, the timestamp is written down for each register (Time_sice_startup field). Additionally, the anonymized identification of the user is recorded (User field).</p> <p>The dataset includes the following characteristics for Oculus Quest 2 HMD and each controller.</p> <ul> <li> DevicePosition (x, y, z): Position recorded.</li> <li> DeviceRotation (w, x, y, z): Rotation expressed with a quaternion.</li> <li> Forward (x, y, z): The unit vector that points to the specific device in the forward direction used in our new model. It can also be obtained by rotating $(0,0,1)$ with the quaternion.</li> <li> DeviceVelocity (x, y, z): Linear velocity of that device in that frame. It represents the rate of change in position.</li> <li> DeviceAcceleration (x, y, z): Linear acceleration of that device in that frame.</li> <li> DeviceAngularVelocity (x, y, z): The angular velocity vector in that frame of the device is measured in radians per second.</li> <li> DeviceAngularAcceleration (x, y, z): The angular acceleration at that frame. </li> </ul> <p>Also for each goal in the scene:</p> <ul> <li> GoalName (x, y, z): Position of that goal. If the element is static, the same position will always be recorded.</li> <li> GoalName_Quat (w, x, y, z): As in the previously defined fields, a rotation is expressed as a quaternion.</li> <li> GoalName_LocalScale (x, y, z): Scale of that element locally related to its parent in the hierarchy. They have no relatives in their hierarchy, so it is the real scale.</li> </ul>
Data, Scripts underlying the publication: Identifying the critical turbidity threshold to maintain estuarine tidal flats worldwide
<p>Data & Scripts featured in the currently unpublished manuscript (as of 21-07-2023) Grandjean et al.: 'Identifying the critical turbidity threshold to maintain estuarine tidal flats worldwide'. This study explores the relationship between tidal range and turbidity concentrations on the morphological trajectory of unvegetated tidal flats at a global scale. These files include the scripts used to produce digital elevation models per estuary.</p> <p> </p>
LiverHccSeg: A Publicly Available Multiphasic MRI Dataset with Liver and HCC Tumor Segmentations and Inter-Rater Agreement Analysis
<p>Please <strong>cite our data paper </strong>published in "Data in Brief": https://www.sciencedirect.com/science/article/pii/S2352340923007473</p><p> </p><p><strong>Background</strong><br>Liver cancer ranks as the third leading cause of cancer-related mortality worldwide [1] and alarmingly, both the incidence and mortality rates of liver cancer are increasing [2; 3]. Among the various types of primary liver cancer, hepatocellular carcinoma (HCC) stands out as the most prevalent, accounting for approximately 70-85% of liver cancer cases [4]. Leveraging the advantages of magnetic resonance (MR) imaging, HCC can be reliably detected and diagnosed without the requirement of an invasive biopsy [5]. MR imaging offers high tissue contrast, which can be further enhanced through contrast-enhanced multiphasic magnetic resonance imaging (mpMRI) techniques. This enables accurate identification and non-invasive diagnosis of HCC [6].</p><p> </p><p><strong>Objective</strong><br>Precise segmentation of the liver plays a crucial role in volumetry assessment and serves as a vital pre-processing step for subsequent tumor detection algorithms [7]. However, accurate liver segmentation can be particularly challenging in patients with cancer-related tissue alterations and deformations in shape [8]. Accurate HCC tumor segmentation is essential for the extraction of quantitative imaging biomarkers such as radiomics and can be used for studies on treatment response assessment and prognosis evaluation and provides critical information about the tumor biology. In order to enhance the reproducibility of liver and tumor segmentation, automated methods utilizing image analysis techniques and machine learning have been developed. These methods have demonstrated promising results [7; 8]; however, most algorithms were tested only on small internal test sets and therefore do not guarantee generalizable and consistent performance on external data.</p><p>Publicly available datasets allow for fair and objective comparisons between different algorithms, techniques, or approaches. Researchers can evaluate the strengths and weaknesses of their methods in relation to existing solutions and establish benchmarks for performance evaluation. In addition to providing a benchmark with this dataset, we also assess the inter-rater variability between two different sets of tumor segmentations. This analysis serves as a measure of reproducibility for human segmentations, highlighting the consistency or variability that may exist among different human raters. Understanding the reproducibility of human segmentations is essential in assessing the reliability of manual annotations and establishing a baseline for algorithm performance comparison. By introducing LiverHccSeg, we aim to fill the gap of lacking publicly available mpMRI HCC datasets and offer researchers and developers a valuable resource for algorithmic evaluation on external data and imaging biomarker analyzes.</p><p> </p><p><strong>Materials and Methods</strong></p><p><i><strong>Inclusion of Patients</strong></i><br>All available scans from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) (<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436">https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436</a>) were downloaded [9]. One multiphasic MRI scan (pre and triphasic post contrast) per patient was included. Patients who did not exhibit a tumor or residual tumor were excluded from the tumor segmentation dataset; however, they were included in the liver segmentation dataset.</p><p> </p><p><i><strong>MR Imaging Data</strong></i><br>Subsequently, all imaging data was converted to the Neuroimaging Informatics Technology Initiative (NIfTI) format with the dcm2nii (v2.1.53) package [10] and available header information was extracted using the pydicom (v.2.1.2) package [11]. Multiparametric MR sequences were labeled with a consistent syntax ('pre', 'art', 'pv', 'del', for the pre-contrast, arterial, portal-venous and delayed contrast phases, respectively). All images were already de-identified by the TCIA website. Images were acquired between the years 1993 and 2007 on Philips and Siemens scanners with field strengths of 1.5 and 3 Tesla. Full details of the imaging parameters can be found in Table 5. Briefly, the median repetition time (TR) and median echo time (TE) were 365.8 ms and 26.4 ms, respectively. The median slice thickness was 9.5 mm, the median bandwidth 536.9 Hz.</p><p> </p><p><i><strong>Scientific Reading</strong></i><br>After conversion, all images were read in a scientific reading by two board-certified abdominal radiologists (S.A. and S.H with 9 and 10 years of experience, respectively). Any disagreement between the two raters was discussed in a consensus meeting. All HCC lesions were classified according to LI-RADS criteria [6].</p><p> </p><p><i><strong>Image Registration</strong></i><br>The co-registration of pre-contrast, portal-venous, and delayed-phase images with arterial phase images was performed using the software BioImage Suite (v3.5) [12]. A non-rigid intensity-based registration approach was applied, employing a parameterized free-form deformation (FFD) with 3D B-splines [13]. The optimal FFD transformation was estimated by maximizing the normalized mutual information similarity metric [14] through gradient descent optimization. To enhance the optimization process, a multi-resolution image pyramid with three levels was utilized. The final B-spline control point spacing was set to 80 mm. The estimated transformation was then employed to warp the moving images (pre-contrast, portal-venous, and delayed-phase) into the reference image space, specifically the arterial phase image.</p><p> </p><p><i><strong>Liver and Tumor Segmentation and Statistical Analysis</strong></i><br>All livers and tumors were manually segmented under the supervision of two board-certified abdominal radiologists using the software 3D Slicer (v4.10.2) [15]. To compare the segmentation agreement between the two sets of liver and tumor segmentations, we calculated segmentation metrics using the Python package seg-metrics (v1.0.0) [16]. All segmentation metrics and statistics were calculated in Python (v3.7).</p><p> </p><p><strong>Data description</strong><br>The data that appears in this article include:</p><ol><li>dicoms.zip: This zip file contains all the raw MR images from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) [1] in the Digital Imaging and Communications in Medicine (DICOM) format used for the curation of this dataset. The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique unidentified patient ID, DATE is the date of the image acquisition, and SEQUENCE is the name of the MR sequence.<br> </li><li>LiverHccSeg_MetaData.xlsx: This spreadsheet contains all the metadata from the DICOM headers along with the data from the scientific image readings.<br> </li><li>nifti_and_segms.zip: This zip file contains all MR images along with the liver and tumor segmentations in the Neuroimaging Informatics Technology Initiative (NIfTI) format.<br>The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique anonymized patient identifier, DATE is the date of the image acquisition, and SEQUENCE is the name of the MRI sequence or segmentation image.<br><br>The NIfTI files are named as follows:<br><strong>pre.nii.gz</strong> : Pre-contrast T1-weighted MRI<br><strong>art.nii.gz</strong>: Arterial-phase T1-weighted MRI<br><strong>pv.nii.gz</strong>: Portal-venous-phase T1-weighted MRI<br><strong>del.nii.gz</strong>: Delayed-phase T1-weighted MRI<br><strong>art_pre.nii.gz</strong>: Pre-contrast T1-weighted MRI registered to the corresponding arterial-phase T1-weighted image<br><strong>art_pv.nii.gz</strong>: Portal-venous-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRI<br><strong>art_del.nii.gz</strong>: Delayed-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRI<br><br>The corresponding manual segmentations are named after the rater and the type of segmentation and follow the format 'RATER_ROI.nii.gz' where RATER denotes the human rater and ROI denotes the region of interest that was segmented, for example, '<strong>rater1_liver.nii.gz</strong>', '<strong>rater2_liver.nii.gz</strong>', '<strong>rater1_tumor1.nii.gz</strong>', and '<strong>rater2_tumor1.nii.gz</strong>'. For tumor segmentations, an integer indicates the tumor identification number for different tumor ROIs, for example, 'rater1_tumor1.nii.gz' and 'rater2_tumor1.nii.gz'. The segmentations can be used for the arterial phase NIfTI file as well as the corresponding co-registered pre-contrast (art_pre.nii.gz), portal-venous (art_pv.nii.gz), and delayed-phase (art_del.nii.gz) images.<br> </li><li>segm_metrics.xlsx: This spreadsheet summarizes the segmentation agreement between the two sets of liver and tumor segmentations by the two board-certified abdominal radiologists.</li></ol><p> </p><p><strong>References</strong></p><p>1 Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209-249</p><p>2 Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7-34</p><p>3 White DL, Thrift AP, Kanwal F, Davila J, El-Serag HB (2017) Incidence of Hepatocellular Carcinoma in All 50 United States, From 2000 Through 2012. Gastroenterology 152:812-820.e815</p><p>4 Perz JF, Armstrong GL, Farrington LA, Hutin YJ, Bell BP (2006) The contributions of hepatitis B virus and hepatitis C virus infections to cirrhosis and primary liver cancer worldwide. J Hepatol 45:529-538</p><p>5 Hamer OW, Schlottmann K, Sirlin CB, Feuerbach S (2007) Technology insight: advances in liver imaging. Nat Clin Pract Gastroenterol Hepatol 4:215-228</p><p>6 Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology 289:816-830</p><p>7 Bousabarah K, Letzen B, Tefera J et al (2020) Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol. 10.1007/s00261-020-02604-5</p><p>8 Gross M, Spektor M, Jaffe A et al (2021) Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLoS One 16:e0260630</p><p>9 Erickson BJ, Kirk S, Lee Y et al (2016) Radiology Data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma [TCGA-LIHC] collection. The Cancer Imaging Archive. 10.7937/K9/TCIA.2016.IMMQW8UQ</p><p>10 dcm2nii DICOM to NIfTI converter. <a href="https://github.com/rordenlab/dcm2niix">https://github.com/rordenlab/dcm2niix</a> Accessed: 2021-12-07.</p><p>11 Mason D, scaramallion;, rhaxton; et al (2020) pydicom/pydicom: pydicom 2.1.2, v2.1.2. Zenodo</p><p>12 X. Papademetris MJ, N. Rajeevan, H. Okuda, R.T. Constable, L.H Staib BioImage Suite: An integrated medical image analysis suite, Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine. <a href="http://www.bioimagesuite.org">http://www.bioimagesuite.org</a>.</p><p>13 Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans Med Imaging 18:712–721</p><p>14 Studholme C, Hill DL, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32:71-86</p><p>15 Fedorov A., Beichel R., Kalpathy-Cramer J. et al (2012) 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323-1341</p><p>16 Ordgod (2020) Ordgod/segmentation_metrics: seg-metrics, v1.0.0. Zenodo</p><p> </p><p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</p><p><strong>ChangeLog</strong></p><p><strong>Version 1.1:</strong> Fixed incorrect liver segmentation mask.</p><p> </p><p> </p><p> </p><p> </p>
Estonian National Bibliography - works in public domain
A subset of public domain works from the Estonian national bibliography. The Estonian national bibliography (Eesti rahvusbibliograafia - ERB) covers publications in all languages in Estonia and abroad, regardless of the medium and date of publication. The database of the national bibliography is constantly updated with newly published works. A separate part of the national bibliography is the Estonica collection, which includes both publications in foreign languages by Estonian authors and publications about Estonia and Estonians published abroad. The database is managed by the National Library of Estonia, which manages the Estonian national bibliography in accordance with the relevant laws. For more info, see <a href="https://digilab.rara.ee">https://digilab.rara.ee</a>.
Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications
<p>This repository contains the dataset for the study of <a href="https://doi.org/10.1093/gigascience/giad113">computational reproducibility of Jupyter notebooks from biomedical publications</a>. Our focus lies in evaluating the extent of reproducibility of Jupyter notebooks derived from GitHub repositories linked to publications present in the biomedical literature repository, PubMed Central. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.</p> <p><strong>Data Collection and Analysis</strong></p> <p>We use the code for reproducibility of Jupyter notebooks from the study done by <a href="../record/2592524">Pimentel et al., 2019</a> and adapted the code from <a href="https://github.com/fusion-jena/ReproduceMeGit">ReproduceMeGit</a>. We provide code for collecting the publication metadata from PubMed Central using <a href="https://biopython.org/docs/1.76/api/Bio.Entrez.html">NCBI Entrez utilities via Biopython</a>.</p> <p>Our approach involves searching PMC using the esearch function for Jupyter notebooks using the query: ``(ipynb OR jupyter OR ipython) AND github''. We meticulously retrieve data in XML format, capturing essential details about journals and articles. By systematically scanning the entire article, encompassing the abstract, body, data availability statement, and supplementary materials, we extract GitHub links. Additionally, we mine repositories for key information such as dependency declarations found in files like requirements.txt, setup.py, and pipfile. Leveraging the GitHub API, we enrich our data by incorporating repository creation dates, update histories, pushes, and programming languages.</p> <p>All the extracted information is stored in a SQLite database. After collecting and creating the database tables, we ran a pipeline to collect the Jupyter notebooks contained in the GitHub repositories based on the code from Pimentel et al., 2019.</p> <p>Our reproducibility pipeline was started on 27 March 2023.</p> <p><strong>Repository Structure</strong></p> <p>Our repository is organized into two main folders:</p> <ul> <li><strong>archaeology</strong>: This directory hosts scripts designed to download, parse, and extract metadata from PubMed Central publications and associated repositories. There are 24 database tables created which store the information on articles, journals, authors, repositories, notebooks, cells, modules, executions, etc. in the db.sqlite database file.</li> <li><strong>analyses</strong>: Here, you will find notebooks instrumental in the in-depth analysis of data related to our study. The db.sqlite file generated by running the archaelogy folder is stored in the analyses folder for further analysis. The path can however be configured in the config.py file. There are two sets of notebooks: one set (naming pattern N[0-9]*.ipynb) is focused on examining data pertaining to repositories and notebooks, while the other set (PMC[0-9]*.ipynb) is for analyzing data associated with publications in PubMed Central, i.e.\ for plots involving data about articles, journals, publication dates or research fields. The resultant figures from the these notebooks are stored in the 'outputs' folder.</li> <li><strong>MethodsWorkflow</strong>: The MethodsWorkflow file provides a conceptual overview of the workflow used in this study.</li> </ul> <p><strong>Accessing Data and Resources:</strong></p> <ul> <li>All the data generated during the initial study can be accessed at https://doi.org/10.5281/zenodo.6802158</li> <li>For the latest results and re-run data, refer to this link.</li> <li>The comprehensive SQLite database that encapsulates all the study's extracted data is stored in the db.sqlite file.</li> <li>The metadata in xml format extracted from PubMed Central which contains the information about the articles and journal can be accessed in pmc.xml file.</li> </ul> <p><strong>System Requirements:</strong></p> <ul> <li>Centos 7 (Documentation: https://www.centos.org/)</li> <li>Conda 4.9.4 (Installation Guide: https://docs.anaconda.com/anaconda/install/linux/)</li> <li>Python 3.7.6 (Download Link: https://www.python.org/downloads/)</li> <li>GitHub account (Get Started: https://github.com/, Requires GitHub Username and Token)</li> <li>gcc 7.3.0 (Installation Guide: https://gcc.gnu.org/install/)</li> <li>lbzip2 (Command: `conda install -c conda-forge lbzip2')</li> </ul> <p><strong>Running the pipeline:</strong></p> <ul> <li>Clone the computational-reproducibility-pmc repository using Git:<br>git clone https://github.com/fusion-jena/computational-reproducibility-pmc.git<br> </li> <li>Navigate to the computational-reproducibility-pmc directory:<br>cd computational-reproducibility-pmc/computational-reproducibility-pmc</li> <li>Configure environment variables in the config.py file:<br>GITHUB_USERNAME = os.environ.get("JUP_GITHUB_USERNAME", "add your github username here")<br>GITHUB_TOKEN = os.environ.get("JUP_GITHUB_PASSWORD", "add your github token here")</li> <li>Other environment variables can also be set in the config.py file.<br>BASE_DIR = Path(os.environ.get("JUP_BASE_DIR", "./")).expanduser() # Add the path of directory where the GitHub repositories will be saved<br>DB_CONNECTION = os.environ.get("JUP_DB_CONNECTION", "sqlite:///db.sqlite") # Add the path where the database is stored.</li> <li>To set up conda environments for each python versions, upgrade pip, install pipenv, and install the archaeology package in each environment, execute:<br>source conda-setup.sh</li> <li>Change to the archaeology directory<br>cd archaeology</li> <li>Activate conda environment. We used py36 to run the pipeline.<br>conda activate py36</li> <li>Execute the main pipeline script (r0_main.py):<br>python r0_main.py</li> </ul> <p><strong>Running the analysis:</strong></p> <ul> <li>Navigate to the analysis directory.<br>cd analyses</li> <li>Activate conda environment. We use raw38 for the analysis of the metadata collected in the study.<br>conda activate raw38</li> <li>Install the required packages using the requirements.txt file.<br>pip install -r requirements.txt</li> <li>Launch Jupyterlab<br>jupyter lab</li> <li>Refer to the Index.ipynb notebook for the execution order and guidance.</li> </ul> <p><strong>References:</strong></p> <ul> <li>Sheeba Samuel, Daniel Mietchen. (2024). Computational reproducibility of Jupyter notebooks from biomedical publications, https://doi.org/10.1093/gigascience/giad113, GigaScience</li> <li>Sheeba Samuel, Daniel Mietchen. (2022). Computational reproducibility of Jupyter notebooks from biomedical publications, https://arxiv.org/pdf/2209.04308.pdf, CoRR abs/2209.04308</li> <li>Sheeba Samuel, & Daniel Mietchen. (2022). Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6802158</li> </ul> <p> </p>
Data and code for the publication "Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil"
<p><strong>Background</strong></p> <p>The dataset contains data on soil properties and microplastic abundance in soil samples taken in Langel-Merkenich (Cologne, Germany). It was analysed in the paper by M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” <a href="https://doi.org/10.1016/j.scitotenv.2022.155141">https://doi.org/10.1016/j.scitotenv.2022.155141</a>) published in Science of the Total Environment.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The zipped folder</p> <ul> <li><strong>data.zip</strong> contains subfolders <strong>imagelab_data</strong> (contains microplastics data by depth and the blanks), <strong>additional_data</strong> (weights of aliquots and polymer densities) and <strong>soil_data</strong> (data on soil properties and images of soil profiles).</li> <li><strong>images.zip</strong> contains the workflow diagram, which is plotted in the R notebook Data_read_in.Rmd.</li> <li><strong>results.zip</strong> contains the results of reading and wrangling of microplastics data.</li> </ul> <p> </p> <p><strong>Description of the code</strong></p> <p>The R notebooks *.Rmd contain the code to read and wrangle the microplastics data (Data_read_in_corrected.Rmd), analyse and plot it (Analysis_plots_MP.Rmd) and plot data on soil properties (Plots_soil_data.Rmd).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p> </p> <p><strong>Funding</strong></p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p> <p> </p> <p><strong>References</strong></p> <p>M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” <a href="https://doi.org/10.1016/j.scitotenv.2022.155141">https://doi.org/10.1016/j.scitotenv.2022.155141</a></p> <p>R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/</p>
Dataset for the publication 'Enset landrace diversity in major enset growing regions of Southern Ethiopia'
<p>Enset (<em>Ensete ventricosum</em> (Welw.) Cheesman) is a major food security crop of the Southern Ethiopian highlands, where it is cultivated on small-holder subsistence farms. Data of survey results through interviews with 375 households is presented, covering 20 communities (kebeles) and eight ethnic groups, along an altitudinal range of 1,500 to 3,000 masl across the main enset-producing belt in Southern Ethiopia. A total of 296 locally named enset landraces were recorded. For each landrace, a number of descriptive traits (as reported by farmers) are available, as is the enset landrace composition of each farm.</p> <p>This data accompanies the publication ‘Blomme G, Kearsley E, Buta S, Chala A, Kebede R, Addis T, Yemataw Z. (2023). Enset landrace diversity in major enset growing regions of Southern Ethiopia. African Crop Science Journal, 31, 279-299’.</p>
Raw data of publication: Towards a greener synthesis of dianhydrohexitol esters (Green Chemistry, 2023)
<p>These are the raw data of all graphs shown in the publication:</p> <p>Katrin Städtke, Andreas W. Göpfert, Alexandra Inayat, Towards a greener synthesis of dianhydrohexitol esters, Green Chemistry, 2023</p> <p>DOI: 10.1039/d3gc01795h</p> <p>All raw data file names refer to the respective figures in this publication and its supplementary information file.</p>
Data associated with the following publication: "A fully automated measurement system for the characterization of micro thermoelectric devices near room temperature"
<p>Data associated with the following publication: "A fully automated measurement system for the characterization of micro thermoelectric devices near room temperature" (DOI: <a href="https://doi.org/10.1016/j.applthermaleng.2023.120111">10.1016/j.applthermaleng.2023.120111</a>).</p>
Data from Commission for the Prevention of Corruption of the Republic of Slovenia: public-to-private transactions
<p>Public spending is often a contentious subject because different political parties have different agendas as to what should be the current national priorities. Of course, the same is true for the public in general. It is thus of interest to determine whether public spending is indeed as biased and capricious as it is often perceived, or whether there nevertheless exist some fundamental principles that guide it. We use data from the Commission for the Prevention of Corruption of the Republic of Slovenia, detailing every transfer of public money to the private sector from January 2003 to May 2020. During this time Slovenia has done business with no less than 248,989 companies. We find that the cumulative distribution of money received per company can be reasonably well explained by means of a power-law or a log-normal fit. We also show evidence for the first-mover advantage, and determine that the attachment rate of public spending to companies over time is roughly linear. These results indicate that Slovenian public spending is to a large extent guided by self organizing principles that, against all odds, go beyond nefarious interests and lobbying.</p>
Supplementary Material for publication "Bifidobacteria Define Gut Microbiome Profiles of Golden Lion Tamarin (Leontopithecus rosalia} and Marmoset Callithrix sp. Metagenomic Shotgun Pools
<p>Supplementary Tables and Figure for the publication "Bifidobacteria Define Gut Microbiome Profiles of Golden Lion Tamarin <em>Leontopithecus rosalia</em> and Marmoset <em>Callithrix</em> sp. Metagenomic Shotgun Pools"</p>
List of multinational publishers of serial publications established by the ISSN Network
<p>The ISSN International Centre and the ISSN Network of nearly 100 national centres around the world are authorized by the International Organization for Standardization (ISO) to implement serial identification according to the terms of ISO3297:2022 standard.<br> The ISSN Network serves publishers who wish to uniquely identify their serial publications. These publishers contact the National Centres of the countries where they are located. When there is no national centre, they contact the ISSN International Centre.<br> The place of publication determines the ISSN Centre responsible for identification. However, when a publisher operates in several countries, an agreement may be reached between this multinational publisher and the involved ISSN Centres so that all requests for identification are handled by one centre.<br> This list identifies more than 300 multinational publishers. These publishers have been identified through the ISSN Portal (https://portal.issn.org) data. The ISSN International Centre works with the National Centres on this list of publishers to determine their responsibility for identifying the publications of these publishers.<br> The list also contains information on the names by which these publishers are known and a mapping of links between publishers. It also contains a link to the ISNI and VIAF databases in which these publishers are also recorded. This data is managed by the ISSN Network in an internal wiki that will be updated regularly.<br> The ISSN Network makes this list available to the publishing community and to librarians. They can contact the ISSN International Centre to share comments (additions, modifications, deletions) on this list which will be published quarterly.</p>
Map of public and private policies and governance mechanisms
<p>This Excel database maps key public and private policies and governance mechanisms regulating legal and sustainable forest and ecosystem-risk biomass value chains across demand and supply-side countries and regions.</p>
OIE4PA: Open Information Extraction for the Public Administration
<p>Tenders are powerful means of investment of public funds and represent a strategic development resource.<br> Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. <br> With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration.<br> Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. <br> For evaluation purposes, we built a dataset composed of 2,000 triples extracted from Italian tenders, which have been manually annotated by two human experts.</p> <p>The dataset, compressed in a single zip file, is composed of:</p> <ul> <li>The corpus of 6,262 texts extracted from Italian public tenders (corpus_tenders)</li> <li>The training set of 1,600 annotated triples (training_set)</li> <li>The test set of 400 annotated triples (test_set)</li> <li>The set U of 14,096 triples used for the self-training (u_triples_dd)</li> <li>a compressed archive that contains both the extracted triples and the index for each supervised approach (extraction) <br> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.