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507 results for “refinement”
A refined method for studying foraging behaviour and body mass in group-housed European starlings.
<p>Datasets and R script corresponding to the following manuscript:</p> <p>A refined method for studying foraging behaviour and body mass in group-housed European starlings.</p> <p>Laboratory experiments on passerine birds have been important for testing hypotheses regarding the effects of environmental variables on the adaptive regulation of body mass. However, previous work in this area has suffered from poor ecological validity and animal welfare due to the requirement to house birds individually in small cages to facilitate behavioural measurement and frequent catching for weighing. Here we describe the social foraging system, a novel technology that permits continuous collection of individual-level data on operant foraging behaviour and body mass from group-housed European starlings (<em>Sturnus vulgaris</em>). We demonstrate rapid acquisition of operant key pecking, followed by foraging and body mass data from two groups of six birds maintained on a fixed-ratio operant schedule under closed economy for 11 consecutive days. Birds gained 6.0 ± 1.2 g (mean ± sd) between dawn and dusk each day and lost an equal amount overnight. Individual daily mass gain trajectories were non-linear, with the rate of gain decelerating between dawn and dusk. Within-bird variation in daily foraging effort (key pecks) positively predicted within-bird variation in dusk mass. However, between-bird variation in mean foraging effort was uncorrelated with between-bird variation in mean mass, potentially indicative of individual differences in daily energy requirements. We conclude that the social foraging system delivers refined data collection and offers potential for improving our understanding of mass regulation in starlings and other species.<strong> </strong></p>
Glass_Procedure_cervix_refining_Mingei
Documentation material from the Mingei project
Electronic Supplementary for: Refining patterns of melt with forward stratigraphic models on stable Pleistocene coastlines
<p>This zipped repository accompanies the paper, "Refining patterns of melt with forward stratigraphic models on stable Pleistocene coastlines". Within the file are two sub-folders. "Model_Input" and "SELEN". "Model_Input" contains the parameters needed to re-run the model runs described in the manuscript in the model environement "OpenFlowSuit" (Beicip Franlab). The "SELEN" sub-folder contains three additional sub-folders: "Background", "Full", and "G2A5". Each of these sub-folders has three .csv files containing the GIA driven sea level curve under three different mantel viscosities, MV1, MV2, and MV3.</p>
Research data for Refined heat pump design and results of final testing
<p>In this dataset, the data of the second experimental test campaign of the CO2-ice heat pump is shared. The report, which analyzes the data and makes the necessary explanations, has already been shared as a "Refined heat pump design and results of final testing (Deliverable: D5.6)". The report has already been published on ZENODO.</p>
Refined Cropland Data Layer (R-CDL)
<p>A decision tree algorithm was employed to refine Cropland Data Layer (CDL) using spatial and temporal information. The Refined Cropland Data Layer (R-CDL) could be used as an alternative to researchers as it provides more accurate cropland information. Annual RCDL maps were produced for the contiguous United States from the year 2017 to 2021.</p> <p>To explore the data online and access the web-based services, please visit the project homepage: https://cloud.csiss.gmu.edu/icrop/</p> <p>To read our full paper on RCDL, please visit: https://www.nature.com/articles/s41597-022-01169-w</p> <p>Cite this article:</p> <p>Lin, L., Di, L., Zhang, C. <em>et al.</em> Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. <em>Sci Data</em> <strong>9, </strong>63 (2022). https://doi.org/10.1038/s41597-022-01169-w</p> <p>Note:</p> <p>2020 RCDL was reproduced with the Re-released CDL on February 1, 2022. More information could be found at: https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php</p> <p>2021 RCDL was released with the official release of 2021 CDL on February 14, 2022</p> <p>2022 RCDL was released on March 24, 2022</p>
The Middle Chalcolithic to Middle Bronze Age Chronology of Cyprus: Refinements and Reconstructions - Supplementary Material
<p>Supplementary material for the article The Middle Chalcolithic to Middle Bronze Age Chronology of Cyprus: Refinements and Reconstructions - Supplementary Material. The material consists of the CQL code for OxCal used to run the models in the article.</p> <p>Article citation: Paraskeva, C. 2019. The Middle Chalcolithic to Middle Bronze Age Chronology of Cyprus: Refinements and Reconstructions, in: Kearns, C., and S. Manning (eds.), New Direction in Cypriot Archaeology, Ithaca-London: Cornell University Press, 45-74.</p>
Refined personal name data from the census book of Vodskaja pjatina
<p>The data contains approximately 36,000 personal names derived from medieval Russian documentation. More preciously, names are collected from an edited version of the census book of Vodskaja pjatina, which was one of the five administrative areas in the late 15<sup>th</sup> century Novgorod.</p> <p>Editions were compiled in parts and the first two, which cover the northernmost region, are called <em>Переписная окладная книга по новугороду вотской пятины</em> (1851, 1852)(POKV I‒II). The third part of the book series <em>Новгородские пистсовые книги</em> (1868)(NPK III) covers the southern and western parts of the study area.</p> <p>The process of obtaining the personal from the inscription has been following: First, editions of the census book were obtained as scanned PDF files. These were transformed as editable copies by using OCR (=Optical Character Recognition) software Abbyy. The program read the original mid-19<sup>th</sup> century Russian text adequately with its old Russian alphabet package.</p> <p>After the initial corrections, a Python script was written to harvest the personal names. This was based on exploiting the systematic formalities in how most of the names were presented in the census book. The script looked for abbreviations “дв.” and “д.” and extracted all following capitalized words until section end markers “.”, “;” or “:”. As an output, a name to pogost matrix was produced, which held the raw frequencies of each word in each pogost.</p> <p>The process of cleaning the name data, in turn, has been done mostly by data wrangling program OpenRefine in following manner: For starters, all name forms shorter than four characters were removed as there were no personal names consisting of three or less letters. Furthermore, nouns that were not names were removed. This meant discarding expressions that described person’s special feature or profession, like such as being a widow (“вдова”) or working as a deacon (“діакъ”). For some reason, editors followed inconsistent conventions in capitalizing these non-name nouns.</p> <p>In addition, some orthographical and morphological harmonization was done on the data. The letter <em>ы </em>was cut from the end of bynames, where it denotes plurality. Similarity of so called soft and hard signs, <em>ь </em>and <em>ъ</em> caused some problems. As the latter one is not used in contemporary Russian and was not used in the original documents either (Неволин 1853 : 4 (in Appendix 1)) it was removed. The soft sign <em>ь </em>was also removed because it was absent in the original documents and it had been used inconsistently by the editors. The letter <em>ѣ</em> (yat) is rarely used in personal names but nevertheless, it was changed to <em>е </em>(like as it is in contemporary Russian) as since it was often confused with soft and hard signs (<em>ь </em>and <em>ъ</em>). Furthermore, the letter <em>ѳ </em>(fita) was often erroneously recognized as <em>о </em>or <em>е. </em>As it is only found in NPK III and only in the beginning of certain names, which all are also written with “Ф” (e.g. “Ѳедко” vs. “Федко”), it was replaced with <em>Ф</em>.</p> <p>In the second phase most of the erroneous orthographies were corrected. We do not detail herescribe all the OCR-errors here that were found, but in the following a short description is given of the most significant corrections. There were, for example, many letters whose similarity caused problems for the OCR-program (e.g. <em>и </em>/ <em>й </em>and <em>б </em>/ <em>в</em>). In these cases, the correct orthography was sought in the census book editions and accordingly, Openrefine was used to change erroneous forms to right correct ones.</p> <p>After the corrections were made, the number of name types (= name variants) was reduced from 4942 to 2748. The Overall overall number of name tokens was dropped as well: from 36,405 to 35,726. Of the name types, more than half (1484) have only one occurrence.</p> <p>The refined and harmonized data is published as pogost-by-name frequency tabulations (<em>pogost,</em> equivalent of English <em>parish</em>). The file is in tab-delimited file (.tsv) format.</p> <p>References:</p> <p>Неволин, К. А. 1853, О пятинах и погостах новгородских в XVI веке, с приложением карты, Санкт-Петербург (Из Записок Императорского русского географического общества, Кн. VIII).</p> <p>NPK III = Новгородские писцовые книги, Т. 3 : Переписная оброчная книга Вотской пятины, 1500 года, 1868, 1868, Санкт Петербург.</p> <p>POKV I, II = Переписная окладная книга по Новугороду Вотьской пятины, 1851, 1852, Имп. Моск. о-во истории и древностей рос., Москва.</p> <p> </p>
Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
Underlying and Extended Data for Refined and benchmarked homemade media for cost-effective, weekend-free human pluripotent stem cell culture
<p>Extended Data and raw data for the manuscript "Refined home-brew media for cost-effective, weekend-free hiPSC culture and genetic engineering"</p> <p> </p> <p dir="ltr">Extended Data 1.zip - protocol for preparation of the supplement for hE8 and B8+ media</p> <p dir="ltr">Extended Data 2.zip - Gene counts, supporting files, and output results of bulk analyses</p> <p dir="ltr">Extended Data 3.zip - Images of iPS cells adapted to cE8, hE8 and B8+ taken 24, 48 and 72 hours after passage. Contains raw .tiff files for each image and a .pdf with a compiled figure</p> <p dir="ltr">Extended Data 4.zip - Results of miloR analysis on the differences in the distribution of cells adapted to cE8, hE8 and B8+ to assigned monocle clusters </p> <p dir="ltr">Manuscript Data.zip – Raw data underlying the Figures 1, 2, 4, 5, 6 and 7.</p> <p>Bulk_RNA_seq_archive – archived source code used for generating results in Figure 3</p>
The name of the game: Palaeoproteomics and radiocarbon dates further refine the presence and dispersal of caprines in Eastern and Southern Africa
<p><span>We report the first large-scale palaeoproteomics research on eastern and southern African zooarchaeological samples, thereby refining our understanding of early caprine (sheep and goat) pastoralism in Africa. Assessing caprine introductions is a complicated task because of their skeletal similarity to endemic wild bovid species and the sparse and fragmentary state of relevant archaeological remains. Palaeoproteomics has previously proved effective in clarifying species attributions in African zooarchaeological materials, but few comparative protein sequences of wild bovid species have been available. Using newly generated collagen type I sequences for wild species, as well as previously published sequences, we assess species attributions for elements originally identified as caprine or "unidentifiable bovid" from seventeen eastern and southern African sites that span seven millennia. We identified over 70% of the archaeological remains and the direct radiocarbon dating of domesticate specimens allows refinement of the chronology of caprine presence in both African regions. These results thus confirm earlier occurrences in eastern Africa and the systematic association of domesticated caprines with wild bovids at all archaeological sites. The combined biomolecular approach highlights repeatability and accuracy of the methods for conclusive contribution in species attribution of archaeological remains in dry African environments.</span></p>
IRES-KIEA Exploration I: Local Sensitivity-driven Refinement
<p>This repository contains the data for the automated reaction network exploration of the Eschenmoser-Claisen rearrangements of allyl alcohol and furfuryl alcohol using local sensitivity analysis as a measure for the targeted network refinement, as presented in</p> <p>Bensberg, M.; Reiher, M., Uncertainty-aware First-principles Exploration of Chemical Reaction Networks, **2023** arXiv, DOI: 10.48550/ARXIV.2312.15477.</p> <p>Furthermore, it contains the data for the expoloration of the network of the Eschenmoser--Claisen rearrangement of allyl alcohol explored without refinement and the electronic structure model combinations PBE0-D3//GFN2 as well as DLPNO-CCSD(T)//PBE-D3.</p> <p> </p>
NASA's Voyager 1 Refined Magnetic Field Data (1-10 au)
<p>This dataset contains NASA's Voyager 1 spacecraft in situ measurements of the solar wind for the Cartesian RTN magnetic field (nT), heliocentric distance (au), proton density (cm^-3), and bulk flow speed (km/s), with time resolution 1.92 seconds.<br> The original source for this refined dataset can be found via the Goddard Space Flight Center Space Physics Data Facility (SPDF), for 2 second MAG data and hourly averaged plasma data.<br> The range of heliocentric distance covered in this dataset is from 1-10 au and each file contains a time series of magnetic field measurements over two days.<br> Each file is labeled with the start and end day covered by the magnetic field time series.<br> Information regarding heliocentric distance, proton density, and bulk flow speed in each file are represented by averages over the corresponding two-day interval.<br> These averages were taken from the hourly averaged data, provided by SPDF, over the appropriate time covered by the magnetic field time series.<br> The CDF files have global attributes: 'CREATOR', 'AFFILIATION', 'S/C NAME', and 'DATE CREATED'.<br> Variable keys are 'SSEPOCH' (time), 'BR' (radial magnetic field), 'BT' (tangential magnetic field), 'BN' (normal magnetic field), 'R' (heliocentric distance), 'N' (proton density), and 'VSW' (bulk flow speed), with variable attribute 'UNITS'.<br> The 'SSEPOCH' variable are timed measurements in units of seconds since Epoch (January 1, 1970).<br> The CDF files were generated in Python using the spacepy.pycdf library.<br> For more specifics regarding the magnetic field cleaning methods utilized to produce this dataset, please refer to the Master's Thesis "Voyager 1: Cleaning Magnetic Field Data for Turbulence Studies" and contact the author for any questions related to the thesis or access.<br> Please send any questions/concerns to Manuel Enrique Cuesta at mecuesta@udel.edu for any problems related to downloading/accessing the data from the CDF files.</p>
Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'.
<p>Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'. doi: https://doi.org/10.1016/j.matchar.2020.110673</p>
Benchmark Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three benchmark datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
Full Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Refinement dataset SPIRA
<p>This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger.</p> <p>The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness.</p> <p>The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.</p>
Training data for 'Refining Manual Genome Annotations with Apollo (eukaryotes)' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for manual curation of eukaryotic genome annotation using Apollo.</p>
Cervix_Refining
This is a part of Bontemps carafe making process (from the Mingei project).
"Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data". Raw data and JANA refinement files.
<p><strong>Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data</strong><br> 3D ED data sets of 5 compounds and JANA refinement files of 12 compounds</p> <p><strong>Relevant tools</strong><strong>:</strong></p> <ul> <li>PETS2: data reduction and analysis of electron diffraction patterns <ul> <li>Download program and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Palatinus, L. <em>et al.</em> Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. <em>Acta Cryst. B</em><strong>75</strong>, 512–522 (2019). <a href="https://doi.org/10.1107/S2052520619007534">DOI: 10.1107/S2052520619007534</a></li> </ul> </li> <li>JANA2006: crystal structure model refinement program <ul> <li>Download program from <a href="http://jana.fzu.cz/">http://jana.fzu.cz/</a> and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Results here were obtained with JANA2006. We recommend using JANA2020.</li> <li>Petricek, V., Dusek, M. & Palatinus, L. Crystallographic Computing System JANA2006: General features. <em>Z. Kristallogr.</em> <strong>229</strong>, 345–352 (2014). <a href="https://doi.org/10.1515/zkri-2014-1737">DOI: 10.1515/zkri-2014-1737</a></li> </ul> </li> <li>DYNGO: Bloch wave program, calculates dynamical diffraction intensities and derivatives <ul> <li>Program automatically included in JANA2006/JANA2020</li> <li>Palatinus, L., Petříček, V. & Corrêa, C. A. Structure refinement using precession electron diffraction tomography and dynamical diffraction: theory and implementation. <em>Acta Cryst. A</em><strong>71</strong>, 235–244 (2015). <a href="https://doi.org/10.1107/S2053273315001266">DOI: 10.1107/S2053273315001266</a></li> </ul> </li> </ul> <p><strong>3D ED data sets:</strong></p> <p>STW_HPM-1 (RT) was measured on a JEOL JEM-2100-LaB6 and diffraction patterns were recorded with an ASI Timepix detector. Another sample of STW_HPM-1 was measured at a temperature of 100 K after cryotransfer with a Titan Krios (CETA-D detector). The other data sets were measured on an FEI Tecnai G2 20 (Olympus SIS Veleta, CCD). Each data set contains the raw diffraction patterns (*.tif) and the basic input files needed to reproduce the data reduction with PETS2 as used in the associated publication (*.pts2, *.celllist, *.cenloc). Step-by-step tutorials are provided for quartz and glycine (and selected steps for abiraterone acetate) at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a>.</p> <ul> <li>α-quartz, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>natrolite, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>cobalt aluminophosphate (CAP), static ED patterns recorded in 0.1° steps (3 data sets from 2 crystals)</li> <li>abiraterone acetate, stepwise continous-rotation (5 data sets from 5 crystals)</li> <li>STW_HPM-1, continuous-rotation (1 data set, room temperature)</li> <li>STW_HPM-1, continuous-rotation (1 data set, <em>T</em> = 100 K, cryotransfer)</li> </ul> <p><strong>JANA refinement and CIF files:</strong></p> <p>CIF (Crystallographic Information Framework) files include two data items. The first is related to the dynamical and the second to the kinematical refinement. Relevant parameters and statistics specific for dynamical refinement are found in the field _refine_special_details.</p> <p>JANA files are provided for the dynamical and kinematical refinement at the stage after the final refinement cycle together with the original input files generated by PETS2. For quartz and natrolite, relevant files for the refinements against precession-assisted 3D ED data are included. For abiraterone acetate and limaspermidine, relevant files for the absolute structure determination are included.</p> <ul> <li>α-quartz</li> <li>albite</li> <li>mordenite</li> <li>natrolite</li> <li>STW_HPM-1</li> <li>cobalt aluminophosphate (CAP)</li> <li>CAU-36</li> <li>α-glycine</li> <li>carbamazepine</li> <li>(+)-limaspermidine</li> <li>abiraterone acetate</li> <li>MBBF4</li> </ul> <p>For the kinematical refinements based on more than one data set, the self-written tool "CompInt" (unpublished) was used. The tool can be found in the file "tool_scalehkl_compint.zip". Input (*.hkl, *.compint) and output files (*.scalehkl) are provided in the respective folder with the JANA files.</p> <p>Raw data sources of other data sets relevant for the associated publication are given in the SI of the associated publication.</p>
KIEA-IRES Exploration II: Morris Sensitivity-analysis-driven Refinement
<p>This repository contains the data for the automated reaction network exploration of the Eschemoser-Claisen rearrangements of allyl alcohol and furfuryl alcohol using Morris sensitivity analysis as a measure for the targeted network refinement, as presented in</p> <p>Bensberg, M.; Reiher, M., Uncertainty-aware First-principles Exploration of Chemical Reaction Networks, **2023** arXiv, DOI: 10.48550/ARXIV.2312.15477.</p> <p> </p>
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.