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302 results for “Registration”
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
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Data for SciKit-SurgeryFRED publication "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research."
<p>This is data used in the publication;</p> <p><a href="https://www.spiedigitallibrary.org/profile/Steve.Thompson-90188">Stephen Thompson</a>, <a href="https://www.spiedigitallibrary.org/profile/Thomas.Dowrick-4289932">Tom Dowrick</a>, <a href="https://www.spiedigitallibrary.org/profile/Mian.Ahmad-4289934">Mian Ahmad</a>, <a href="https://www.spiedigitallibrary.org/profile/Jeremy.Opie-4314392">Jeremy Opie</a>, and <a href="https://www.spiedigitallibrary.org/profile/notfound?author=Matthew_Clarkson">Matthew J. Clarkson</a> "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980U (15 February 2021); <a href="https://doi.org/10.1117/12.2580159">https://doi.org/10.1117/12.2580159</a></p> <p>Data in summerSchoolGameLogs was collected using scikit-surgeryfred: v0.0.3 summer school 2020 (2020). DOI 10.5281/zenodo.3946090</p> <p>Data in in registration_results was collected using scikit-surgeryfred: v0.0.8 browser based user interface (2020). DOI 10.5281/ zenodo.4314971</p> <p>Each directory contains Python scripts to analyse the data as described in the above paper.</p> <p> </p>
S36 | UBAPMT | Prioritised PMT/vPvM substances in the REACH registration database
<p><strong>Prioritised PMT/vPvM substances in the REACH registration database</strong></p> <p>This is the 2022 update (first update) of the UBA list of prioritised persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) substances in the REACH registration database. All substances are registered under REACH (EC No 1907/2006) and meet the <a href="https://www.umweltbundesamt.de/publikationen/protecting-the-sources-of-our-drinking-water-the">PMT/vPvM criteria as proposed by UBA in 2019</a>. Compared to the first version from 2019, this 2022 update of the UBA list adds new substances and improved the PMT/vPvM assessment. This UBA list is published as UBA TEXTE xxx /2022. It is indicated if a substance would also meet the less stringent PMT/vPvM criteria as published by the European Commission (EC) in September 2021, which are currently under discussion for inclusion in the Classification, Labelling and Packaging (<a href="https://echa.europa.eu/guidance-documents/guidance-on-clp">CLP</a>) regulation (EC No 1272/2008).</p> <p><em>Reference: </em>Hans Peter H Arp, Sarah E Hale, Ivo Schliebner and Michael Neumann (2022). Prioritised PMT/vPvM substances in the REACH registration database, Texte | XXX/2022, edited by Michael Neumann and Ivo Schliebner, IV 2.3 Chemicals, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804 xxx pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0 and Report No. (to be announced)</p> <p><em>Previous version:</em></p> <p>The first version of this UBA list from 2019 was published as a <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">technical note (UBA TEXTE 126/2019)</a>.</p> <p><em>Reference: </em>Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN:1862-4804, 131 pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG.</p> <p>This collection is associated with list S36 UBAPMT on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>).</p>
Colombia. Justicia. SNR Superintendencia de Notariado y Registro. Actividad Registral y Notarial 2011 a 2022,. Mensual
<p>Colombia. Justicia. SNR Superintendencia de Notariado y Registro. Actividad Registral y Notarial 2011 a 2022</p>
FIG. 6 in L'apport du registre paléogène d'Amazonie sur la diversification initiale des Caviomorpha (Hystricognathi, Rodentia): implications phylogénétiques, macroévolutives et paléobiogéographiques
FIG. 6. — Consensus strict de l'AG1 (Fig. 5) et indices de Bremer aux noeuds. Les taxons en gras sont les espèces découvertes dans le Paléogène de Contamana (Éocène et Oligocène) et à Tarapoto/Shapaja. Le code couleur est le même que celui de la Figure 3.
FIG. 3 in L'apport du registre paléogène d'Amazonie sur la diversification initiale des Caviomorpha (Hystricognathi, Rodentia): implications phylogénétiques, macroévolutives et paléobiogéographiques
FIG. 3. — Arbre de contrainte employé pour les AG1-AG9, basé sur des données moléculaires. Modifié d'après Upham & Patterson (2015: 77, fig. 3). Les quatre super-familles de caviomorphes sont différenciées par des couleurs: Cavioidea (rouge), Chinchilloidea (bleu), Erehizontoidea (orange) et Octodontoidea (violet).
Evaluating registrations of serial sections with distortions of the ground truths. Supplemental data
<p><strong>Evaluating Registrations of Serial Sections With Distortions of the Ground Truths</strong></p> <p>This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. The files are named as follows:</p> <ul> <li>*_challenge.7z: local distortions and global rigid transformations applied, the input for the benchmark we used. Use this to test your rigid and non-rigid methods.</li> <li>*_local-only.7z: only local distortions applied.</li> <li>*_local-DIST.7z: the distortion maps for local distortions.</li> <li>*_SURF-rigid.7z: local distortions and global rigid transformations applied, rigid transformations undone with SURF-based rigid-only method. Local distortions remain. Use this if your method does not cope well with large rigid transformations.</li> <li>_*vis.7z: visualizations of distortions.</li> <li>_rigid_ground.7z: the real rigid transformations used in the global phase.</li> <li>*_ground.7z: the ground truth. All data fit each other, no distortions. Use this to compare your registration result to it.</li> </ul> <p>There are three main modalities and one further, as a reference:</p> <ul> <li>CT_*: µCT data, a rabbit lung, 600 images. (In ground truth, and local distortions, and global transformations we supply more images that went into the benchmark, 50 more from both beginning and end.)</li> <li>EM_*: an EM serial block-face (SBF-SEM) data set of adult mouse lung, 1000 images. (EM ground truth is individually normalized, see paper.)</li> <li>LS_*: a lung from the light sheet microscopy from a male 24 week-old rat, 300 images. (LS ground truth is individually normalized, too.)</li> <li>REAL_*: a region from real serial sections from a rabbit lung, 2 images.</li> </ul> <p>We also supply elastix parameter files.</p> <p>A preprint has been uploaded to <a href="https://arxiv.org/abs/2011.11060">arXiv</a>. The definite version is available from <a href="https://ieeexplore.ieee.org/abstract/document/9594850/media#media">IEEE</a>. The source code of the distorter is available from <a href="https://github.com/olegl/distort">GitHub</a>.</p>
Data from Study: Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration
<p>This dataset contains the data used to arrive at the conclusions in the research article <em>Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration</em>, by Balfour et al [<em>BioMedical Engineering OnLine</em> 2015, <strong>14</strong>:85].</p> <p>This study was based upon motion-affected PET images simulated from real dynamic MR image volumes, simulated and reconstructed using the Software for Tomographic Image Reconstruction ("STIR", see http://stir.sourceforge.net/). This dataset includes data from MR scans of 4 healthy volunteers (male, aged 22-33).</p> <p>Three types of data are provided, which should be sufficient for repeating the findings of the study:</p> <ul> <li>Reconstructed PET image volumes, split into 6 respiratory bins ("gates") for each simulation</li> <li>The dynamic 3D MR volumes used to derive the respiratory motion of each volunteer</li> <li>Text files outline which dynamics have NOT been used for PET simulation - these are the ones used to make the motion model in the study</li> </ul> <p>These MR volumes were registered and combined with the head-foot position of the right hemidiaphragm to form a respiratory motion model, which was subsequently used to constrain PET to PET image registration, attempting to correct for the motion in the PET images.</p> <p>For more detailed information regarding the method, please refer to the article.</p> <p>The PET data is split into several sub-categories:</p> <ul> <li>Volunteer ID (4 possibilities, anonymised)</li> <li>Lesion position (9 possibilities - see article for locations)</li> <li>Lesion diameter, in millimetres (10 or 14 mm)</li> <li>Respiratory gate number, ranging from 1 (most inhaled) to 6 (most exhaled)</li> </ul> <p>Note that there are two types of each simulation: with motion, and without motion. These are included in the respective zip files for each volunteer ID.</p> <p> </p>
Feature-based multi-resolution registration of immunostained serial sections - online material
<p>This is the supplementary online material, including full data, evaluation, and executables, for the paper "Feature-based multi-resolution registration of immunostained serial sections" that appeared in Medical Image Analysis, Volume 35, January 2017, Pages 288–302.</p> <p>Same material was deposited online under https://gdv-server.inf.uni-bayreuth.de/gdvcloud/index.php/s/NnSov0O65n9Gp01 </p> <p>We also include here further supplementary files deposited at the journal page (http://www.sciencedirect.com/science/article/pii/S136184151630127X) under CC-BY licence. See there for the definite version of the paper or http://www.mathematik.uni-marburg.de/~lobachev/papers/lobachev-media16-registration-preprint.pdf for the preprint.</p>
The German Protest Registrations Dataset
<p>The <strong>German Protest Registrations Dataset</strong> covers protests that have been registered with demonstration authorities in 16 German cities. The data has been compiled from <i>Freedom of Information</i> requests and covers dates, organizers, topics, the number of registered participants, and for some cities the number of observed participants. Covered date ranges vary, with all cities covered in 2022, and 5 cities covered consistently from 2018 to 2022. In comparison to previous datasets that are largely based on newspaper reports, this dataset gives an unprecented level of detail, and is the largest dataset on protest events in Germany to date. <a href="https://github.com/davidpomerenke/german-protest-registrations/releases/download/v1.0.0/report.pdf">The report</a> gives an overview over existing datasets, explains the data retrieval and processing, displays the properties of the dataset, and discusses its limitations. Code and data are available <a href="https://github.com/davidpomerenke/german-protest-registrations">on Github</a>.</p><p>The dataset is provided in three subsets:</p><ol><li>The <strong>2018-2022 dataset</strong> contains only cities that have coverage throughout 2018-2022, and that also have the number of registered participants available. This results in a large dataset (about 40.000 entries) that strikes a balance between regional diversity (5 cities) and time coverage (5 years).</li><li>The <strong>2022 dataset</strong> contains all cities that have data available in 2022, and also have the number of registered participants available. With 13 cities covered but only 12.500 overall entries, it is useful especially for the study of geographic variations.</li><li>The <strong>unfiltered dataset</strong> contains all data without restrictions on included cities and time ranges, overall 57.000 entries. It also contains cities where the number of registered participants is not available. 16 cities are included, with varying time ranges between 2012 and 2022. This dataset should <i>not</i> be used for direct analysis; but it may be used for the creation of alternative consistent sub-datasets similar to the two ones above.</li></ol>
Bradipho Registration Utils
<p>Registration Utils for visualization in the specimen space (https://bradipho.eu/). This is the early version used for data generation during the pilot phase of the project around OHBM 2023.<br><br>Requires Scilpy (https://github.com/scilus/scilpy), Bradiphopy (https://github.com/minilabus/bradiphopy) and AntsRegistration (https://github.com/ANTsX/ANTs)</p> <p>From a folder with .trk or .tck, and a .nii or .nii.gz (optional) launch using :</p> <p>The folders in the ZIP file should be merged with this <a href="https://github.com/minilabus/bdp_registration_utils" target="_blank" rel="noopener">GitHub repository</a><br><em>(specimen's folders have the same name so there are no conflicts)</em><br>We recommend using the Dockerfile on the <a href="https://github.com/minilabus/bdp_registration_utils" target="_blank" rel="noopener">GitHub repository</a>.</p> <p><em>bash ../bdp_registration_utils/launch_registration.sh $PWD "sub-01"</em></p>
Registration of brands from Germany's most active companies
<p>This bar-plot shows the amount of brand registrations of Germany's most active companies in 2017 and 2019.</p>
Brand registrations from Germany's most active companies
<p>This dataset shows the amount of brand registrations from Germany's most active companies in 2017 and 2019.</p>
PID Registration Service Demo Application
<p>The demonstration will cover the four functions: verification, registration, identified user taskId and taskId status. </p> <ul> <li> <p>The API Reference Overview demonstrates four functions of the pid registration service for variables: the verification (validation) of the variables and the metadata to be registered. It returns a validation report.</p> </li> <li> <p>The variable registration prototype (not implemented yet) returns a taskId to follow up on the registration status. </p> </li> <li> <p>The Variable status tasks check the status of the tasks according to the identified user</p> </li> <li> <p>The variable/status/task/{taskId} that returns either the verification status or register process identified by the taskId.</p> </li> </ul> <p>The demonstration also cover the status pages. The first status page organizes the tasks by Username, amount of tasks, and their respective status (Finished, Pending/running or failed). The user task overview demonstrates the JSON file validation. Some tasks can register a bunch of variables, taking longer, so it is important to follow up on the running tasks from the user's perspective. The status page breakdowns the follow-up for each variable registration requested at a given taskId.</p>
Data associated with Versatile Multiple Object Tracking in Sparse 2D/3D Videos via Deformable Image Registration (2024)
<p>This includes a volumetric whole-brain calcium recording of a freely behaving worm (<em>C. elegans</em>) captured at 4 Hz with tracked fluorescent neuronal nuclei, used to demonstrate the performance of a multi-object tracking algorithm (ZephIR) described in the associated publication.</p>
Whole slide images of mouse liver serial sections - Test registration dataset
<p>15 H&E serial section of mouse liver and small intestine.</p> <p>Sampled prepared in the <a href="https://www.epfl.ch/research/facilities/histology-core-facility/">EPFL histology core facility</a> by Nathalie Müller, Gian-Filippo Mancini, and Agnès Hautier.</p> <p>All slides where imaged with a VS200 Evident slide scanner from the<a href="http://biop.epfl.ch/"> EPFL BIOP imaging facility</a>.</p>
Figures 2-3. 2 in Paf+SU: Deposit Limit in Registration
Figures 2-3. 2 Loricophrya bosporica on nematode Metachromadoroides remanei. 3 Group of Loricophrya bosporica individuals on nematode Metachromadoroides remanei. Scale bar 10 μm.
Figure 1. Sampling stations A and B in Paf+SU: Deposit Limit in Registration
Figure 1. Sampling stations A and B located at Western part of the Ria Formosa lagoon (Southern Portugal).
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2) in Structure and dynamics of the taxocenes of shrews in different habitats of the Norsky nature reserve
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2)
Рис. 4. Карта-схема мест встреч пятнистого оΛеня в Нижнем Приамурье в 1979–2021 гг. КваΑраты — места фоторегистрации: 1 — верховья рр. Обор и Àурмин; 2, 3 — Анюйский национаΛьный парк; круги — места встреч по Λитературным и опросным Αанным: 1 — окрестности с. Кутузовка (место первой регистрации в 1979 г.); 2 — верховья р. СиΑима; 3 — устье р. Нижняя Буге; 4 — бассейн р. Мухен; 5–8 — Анюйский национаΛьный парк (соответственно, р. Пихца, урочище Сира, окрестности с. Арсеньево, устье р. СоΛоми); 9 — среΑнее течение р. СоΛоми; 10 — 76 км трассы ΔиΑога — Ванино; 11 — бассейн р. Кия; 12 — бассейн р. ХойΑур; 13 — бассейн р. Нюра Fig. 4. A schematic map of sika deer sightings in the Lower Amur Region in 1979-2021. Squares designate sites of photo recording: 1 — upper reaches of the rivers Obor and Durmin; 2, 3 — Anyui National Park; circles designate sightings sites according to the literature and the survey data: 1 — vicinity of the village Kutuzovka (the place of the first registration in 1979); 2 — upper reaches of the river Sidima; 3 — the mouth of the river Lower Buge; 4 — the Mukhen River basin; 5-8 —Anyui National Park (respectively, the Pikhtsa River, the Sira tract, the vicinity of the village Arsenyevo, the mouth of the Solomi River); 9 — the middle course of the Solomi River; 10 — 76 km of the Lidoga-Vanino Highway; 11 — the Kiya River basin; 12 — the Khoydur River basin; 13 — the Nyura River basin in New data on the distribution of sika deer Cervus nippon Temminck, 1838 in the Lower Amur Region
Рис. 4. Карта-схема мест встреч пятнистого оΛеня в Нижнем Приамурье в 1979–2021 гг. КваΑраты — места фоторегистрации: 1 — верховья рр. Обор и Àурмин; 2, 3 — Анюйский национаΛьный парк; круги — места встреч по Λитературным и опросным Αанным: 1 — окрестности с. Кутузовка (место первой регистрации в 1979 г.); 2 — верховья р. СиΑима; 3 — устье р. Нижняя Буге; 4 — бассейн р. Мухен; 5–8 — Анюйский национаΛьный парк (соответственно, р. Пихца, урочище Сира, окрестности с. Арсеньево, устье р. СоΛоми); 9 — среΑнее течение р. СоΛоми; 10 — 76 км трассы ΔиΑога — Ванино; 11 — бассейн р. Кия; 12 — бассейн р. ХойΑур; 13 — бассейн р. Нюра Fig. 4. A schematic map of sika deer sightings in the Lower Amur Region in 1979-2021. Squares designate sites of photo recording: 1 — upper reaches of the rivers Obor and Durmin; 2, 3 — Anyui National Park; circles designate sightings sites according to the literature and the survey data: 1 — vicinity of the village Kutuzovka (the place of the first registration in 1979); 2 — upper reaches of the river Sidima; 3 — the mouth of the river Lower Buge; 4 — the Mukhen River basin; 5-8 —Anyui National Park (respectively, the Pikhtsa River, the Sira tract, the vicinity of the village Arsenyevo, the mouth of the Solomi River); 9 — the middle course of the Solomi River; 10 — 76 km of the Lidoga-Vanino Highway; 11 — the Kiya River basin; 12 — the Khoydur River basin; 13 — the Nyura River basin
ScienceDex guides
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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.