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figure 2 in The sawFIsh (Rhinopristiformes, Pristidae) rostrum displayed in the "Basilica Santuario del Carmine Maggiore" in Naples, Italy: A long story of legends and taxonomic errors
figure 2 Morphological characters of the bscm sawfish rostrum and comparison with illustrations from Costa (1853a, b): Pictures of the (A) dorsal and (B) ventral side of the sawfish rostrum showing the measurements carried out: total rostrum length (trl), standard rostrum length (srl), proximal rostrum width (prw), distal rostrum width (drw) proximal tooth gap (ptg) and distal tooth gap (dtg), and (C) illustration of the sawfish rostrum from Costa (1853b), (D) Picture of the proximal end of the rostrum with the three internal canals clearly visible, the median canal from which cartilage samples were taken is indicated by a white arrow and the two lateral canals are indicated with white asterisks, and (E) detail of a rostral tooth posterior margin compared to illustration from Costa (1853b); arrows indicate the groove.
figure 3 in The sawFIsh (Rhinopristiformes, Pristidae) rostrum displayed in the "Basilica Santuario del Carmine Maggiore" in Naples, Italy: A long story of legends and taxonomic errors
figure 3 Principal component analysis (pca) performed on five standardized rostral characters (srl, prw, drw, ptg, dtg) using measures obtained from the rostrum of the bscm (pink square) and data from Seitz & Hoover (2017) for isolated rostra of A. cuspidata (in red), P. pristis (in yellow), P. pectinata (in blue) and P. zijsron (in green). Each rostrum is indicated with a dot, while groups of rostra of the same species are represented by a polygon.
Figure S1. Funnel Plot of Logit Transformation of Proportions Against its Standard Errors for Detecting Publication Bias
<p>Figure S1. Funnel Plot of Logit Transformation of Proportions Against its Standard Errors for Detecting Publication Bias, Provided as Supplementary Material for Assessing Publication Bias in the Study of Worldwide Prevalence of Internet Addiction Among Medical Students by Zhandos Salpynov and Co-Authors.</p> <p> </p>
68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file, revised for clean cnxvalidate error report
<p>This kit includes an additional revised master file, lyso009a_0087.JF07T32V01_master_rev.h5 that provides compliance with the October 2019 NXmx specification as proposed in https://github.com/HDRMX/definitions.git</p> <p>To create a new NeXus master file, assuming DIALS is installed in the folder $DIALS, use this command:</p> <p>libtbx.python $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5</p> <p>Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at dials.github.io.</p> <p>After the DIALS run, for full NXmx compliance you will need the jungfrau portions of the script that was used to generate lyso009a_0087.JF07T32V01_master_rev.h5</p> <p> </p> <pre>#!/bin/bash cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 cp jungfrau/lyso009a_0087.JF07T32V01_master.h5 jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 export curdat=`date +%FT%T.%3` export LD_LIBRARY_PATH=$HOME/lib export HDF5_PLUGIN_PATH=$HOME/lib export PATH=$HOME/bin:$PATH h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/name -d /entry/instrument/name -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/source -d /entry/source -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_rev.nxs -s /entry/instrument/source -d /entry/source -f ref h5copy -i jungfrau/lyso009a_0087.JF07T32V01_master.h5 -o jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 -s /entry/sample/beam -d /entry/instrument/beam -f ref export end_time=`h5dump -d "/entry/end_time" Therm_6_2_master.h5 | grep ":" | sed 's/^.........//'|sed 's/.\$//'` echo "end_time: $end_time" python << 'EOL' import h5py as h5 import numpy as np import os end_time=os.environ['end_time'] curdat=os.environ['curdat'] fvds = h5.File('Therm_6_2_rev.nxs','r+') fmaster = h5.File('Therm_6_2_master_rev.h5','r+') jungfrau= h5.File('jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5','r+') fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() jungfrau_keys=jungfrau.keys() fvds_entry=fvds['entry'] fmaster_entry=fmaster['entry'] jungfrau_entry=jungfrau['entry'] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() jungfrau_entry_keys=jungfrau_entry.keys() fvds_entry_instrument=fvds['entry']['instrument'] fmaster_entry_instrument=fmaster['entry']['instrument'] jungfrau_entry_instrument=jungfrau['entry']['instrument'] fvds_entry_instrument_keys=fvds_entry_instrument.keys() fmaster_entry_instrument_keys=fmaster_entry_instrument.keys() jungfrau_entry_instrument_keys=jungfrau_entry_instrument.keys() fvds_entry_instrument_name=(fvds['entry']['instrument']['name']) fmaster_entry_instrument_name=(fmaster['entry']['instrument']['name']) jungfrau['entry']['instrument'].create_dataset("name", data=np.string_("Paul Scherrer Institute SwissFEL Aramis 1 (Alvra)")) jungfrau_entry_instrument_name=(jungfrau['entry']['instrument']['name']) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs['short_name'] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs['short_name'] jungfrau_entry_instrument_name.attrs.modify('short_name',np.string_("Alvra")) jungfrau_entry_instrument_short_name=jungfrau_entry_instrument_name.attrs['short_name'] zero_offset=fmaster_entry_instrument['detector']['module']['fast_pixel_direction'].attrs['offset'] fmaster_det_z=fmaster_entry_instrument['transformations']['det_z'] fvds_det_z=fvds_entry_instrument['transformations']['det_z'] print('fvds_keys: ',fvds_keys) print('fmaster_keys: ',fmaster_keys) print('jungfrau_keys: ',jungfrau_keys) print('fvds_entry_keys: ',fvds_entry_keys) print('fmaster_entry_keys: ',fmaster_entry_keys) print('jungfrau_entry_keys: ',jungfrau_entry_keys) print('fvds_entry_instrument_keys: ',fvds_entry_instrument_keys) print('fmaster_entry_instrument_keys: ',fmaster_entry_instrument_keys) print('jungfrau_entry_instrument_keys: ',jungfrau_entry_instrument_keys) print('fvds_entry_instrument_name: ',fvds_entry_instrument_name) print('fmaster_entry_instrument_name: ',fmaster_entry_instrument_name) print('jungfrau_entry_instrument_name: ',jungfrau_entry_instrument_name) print('fvds_entry_instrument_short_name: ',fvds_entry_instrument_short_name) print('fmaster_entry_instrument_short_name: ',fmaster_entry_instrument_short_name) print('jungfrau_entry_instrument_short_name: ',jungfrau_entry_instrument_short_name) print('fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: ',zero_offset) print('fmaster_entry_instrument_detector_detector_z_det_z: ',fmaster_det_z) print('fmaster_entry_end_time: ',end_time) fmaster.attrs.modify('file_time',np.string_(end_time)) fmaster.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fmaster.attrs.modify('HDF5_Version',np.string_('hdf5-1.8.18')) fvds.attrs.modify('file_time',np.string_(end_time)) fvds.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fvds.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) jungfrau.attrs.modify('file_time',np.string_(curdat)) jungfrau.attrs.modify('file_name',np.string_('lyso009a_0087.JF07T32V01_master.h5')) jungfrau.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fmaster_entry_instrument_name.attrs.modify('short_name',np.string_(fmaster_entry_instrument.attrs['short_name'])) fmaster_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fmaster_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fvds_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fvds_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fmaster_det_z.attrs.modify('offset',zero_offset) fvds_det_z.attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) print(fmaster['entry']['instrument']['name'].attrs['short_name']) print(fmaster['entry']['instrument']['name'].attrs['short_name'].shape) print(fmaster['entry']['instrument']['name'].attrs['short_name'].dtype) print("/entry/instrument/ELE_D0/pixel_mask_applied :",jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied']) del jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied'] jungfrau_entry_instrument['ELE_D0'].create_dataset("pixel_mask_applied",dtype='int8', data=1) print("/entry/instrument/ELE_D0/pixel_mask_applied :",jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied']) jungfrau_entry_source=jungfrau_entry.create_group('source') jungfrau_entry_source=jungfrau_entry['source'] jungfrau_entry_source.attrs.modify('NX_class',np.string_("NXsource")) jungfrau_entry_source.create_dataset("name",data=np.string_("Paul Scherrer Institute SwissFEL")) jungfrau_entry_source['name'].attrs.modify('short_name',np.string_("SwissFEL")) #jungfrau_entry_instrument.create_group['beam'] #jungfrau_entry_instrument['beam']=jungfrau_entry['sample']['beam'] jungfrau_entry_instrument['beam'].create_dataset('total_flux',dtype='float64',data=1000000000000.) jungfrau_entry_instrument['beam']['total_flux'].attrs.modify('units',np.string_('/pulse')) del jungfrau_entry['sample']['beam'] del fvds_entry_instrument.attrs['short_name'] del fmaster_entry_instrument.attrs['short_name'] del fmaster_entry_instrument['source'] fvds.close() fmaster.close() jungfrau.close() quit() EOL $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_master_rev.h5 $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_rev.nxs $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 </pre> <p> </p>
Dataset of "A large-scale empirical study of compiler errors in continuous integration"
<p>Data obtained from the authors, uploaded with permission.</p> <p>Warning: Uncompressed is >3TB</p>
The experimental data of three-axis fluxgate instrument installation orientation error correction
Open the record for dataset details and reuse information.
Datasets and Codes - Toward improved representation of reservoir operation at the larger scales: The role of calibration, the trade-off between reservoir release and storage, and the issue of error accumulation in long-term simulations
Open the record for dataset details and reuse information.
FIGURE 3 in Correction of a typographical error in Bignonia 'ghorta' (Bignoniaceae)
FIGURE 3. Lectotype of Bignonia ghonta (G00133401 image!).
FIGURE 2. Page 149 in Correction of a typographical error in Bignonia 'ghorta' (Bignoniaceae)
FIGURE 2. Page 149 of Buchanan-Hamilton's manuscript.
FIGURE 1 in Correction of a typographical error in Bignonia 'ghorta' (Bignoniaceae)
FIGURE 1. Part of Wallich's Numeric List showing 'Bignonia ghonta B. Ham.' (No. 6510).
SIP-field porbe development: Lessons learned & measurement error estimations
Open the record for dataset details and reuse information.
ERROR ANALYSIS AND CORRECTION IN EFL LINGUISTICS DEVELOPMENT IN FORMAL WRITING
Open the record for dataset details and reuse information.
The Tale of Errors in Microservices (Artifact part 2)
<p>Part 2 of trace release. Please refer to part 1 <a href="https://doi.org/10.5281/zenodo.13947828">10.5281/zenodo.13947828</a> for details.</p>
Data from: Colour patch size and measurement error using reflectance spectrometry
1. Over the past twenty years, portable and relatively affordable spectrophotometers have greatly advanced the study of animal coloration. However, the small size of many colour patches poses methodological challenges that have not, to date, been assessed in the literature. Here, we tackle this issue for a reflectance spectrophotometry set-up widely used in ecology and evolution (the beam method). 2. We reviewed the literature on animal coloration reporting the use of reflectance spectrophotometry to explore how the minimum measurable size of a colour patch is determined. We then used coloured plastic sheets to create artificial colour patches, and quantify the relationship between colour patch size and distortions induced by resulting chimeric spectra (spectra contaminated by an adjacent colour patch). Finally, we assessed the generality of our findings using natural colour spots in the lizard Podarcis muralis, as a biologically realistic model. 3. We found a lack of consensus in the literature, frequently resulting in the rejection of valid data or the potential inclusion of unreliable data. As expected, we show that decreasing colour patch size reduces the reliability of reflectance measurements, but also that spectral distortions resulting from chimeric spectra depend on patch/background colour combinations. We found similar results using natural colour spots in P. muralis. 4. We propose a series of steps to avoid the pitfalls described above. First, we provide guidelines on how to identify chimeric spectra and estimate the minimum size of a measurable colour patch in order to avoid them. Second, we show that reducing the probe-to-surface distance allows for more accurate measurements and therefore improves the spectrophotometric assessment of small colour patches. Third, we suggest that, as a general rule of thumb, very small (< 2 mm) colour patches should be avoided when using traditional spectrophotometry methods.
A Streamlined and High-Throughput Error-Corrected Next-Generation Sequencing Method for Low Variant Allele Frequency Quantitation
<p></p><p>Quantifying mutant or variable allele frequencies (VAFs) of ≤10−3 using next-generation sequencing (NGS) has utility in both clinical and nonclinical settings. Two common approaches for quantifying VAFs using NGS are tagged single-strand sequencing and duplex sequencing. While duplex sequencing is reported to have sensitivity up to 10−8 VAF, it is not a quick, easy, or inexpensive method. We report a method for quantifying VAFs that are ≥10−4 that is as easy and quick for processing samples as standard sequencing kits, yet less expensive than the kits. The method was developed using PCR fragment-based VAFs of Kras codon 12 in log10 increments from 10−5 to 10−1, then applied and tested on native genomic DNA. For both sources of DNA, there is a proportional increase in the observed VAF to input VAF from 10−4 to 100% mutant samples. Variability of quantitation was evaluated within experimental replicates and shown to be consistent across sample preparations. The error at each successive base read was evaluated to determine if there is a limit of read length for quantitation of ≥10−4, and it was determined that read lengths up to 70 bases are reliable for quantitation. The method described here is adaptable to various oncogene or tumor suppressor gene targets, with the potential to implement multiplexing at the initial tagging step. While easy to perform manually, it is also suited for robotic handling and batch processing of samples, facilitating detection and quantitation of genetic carcinogenic biomarkers before tumor formation or in normal-appearing tissue.</p><p></p>
Benchmarking datasets used in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs"
<p>This is the raw long-read sequencing data used for benchmarking experiments in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs". The name of the dataset is labeled as : simulated/real; ecoli/metagenome; ploidy/name; pacbio/ont; sequencing error rate; average sequencing coverage per haplotype.</p>
Figure 3 from: Welter-Schultes F, Görlich A, Lutze A (2016) Sherborn's Index Animalium: New names, systematic errors and availability of names in the light of modern nomenclature. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 173–187. https://doi.org/10.3897/zookeys.550.10041
Figure 3 - Example of a new name established in a chaotically arranged early zoological work. Detail of Hartmann (1821: p. 231), with the original description of Helix ruderata Hartmann, 1821 (currently Discus ruderatus). For the non-insider it is very difficult to see that a new name was established here. It is necessary to understand precisely the content of the German text: "s. meine Tab. II. f. 11. Der vorigen ähnlich, aber aufgeblasener, rauher, weniger Umgänge" (= see my plate II, figure 11. Similar to the previous one, but more inflated, rougher, fewer whorls).
Figure 1 from: Welter-Schultes F, Görlich A, Lutze A (2016) Sherborn's Index Animalium: New names, systematic errors and availability of names in the light of modern nomenclature. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 173–187. https://doi.org/10.3897/zookeys.550.10041
Figure 1 - Number of names. Total number of taxonomic names listed in Sherborn's Index Animalium and in AnimalBase (2011) plotted in 5-year intervals. It must be taken into account that Sherborn's numbers included a proportion of 30% of names that were not new, while in AnimalBase this proportion was much lower (less than 5 %).
Figure 2 from: Welter-Schultes F, Görlich A, Lutze A (2016) Sherborn's Index Animalium: New names, systematic errors and availability of names in the light of modern nomenclature. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 173–187. https://doi.org/10.3897/zookeys.550.10041
Figure 2 - Languages used in early zoological literature. Language analysis of 2100 arbitrarily selected binominal zoological works published between 1758 and 1850.
Code and data for manuscript: Does ankle push-off correct for errors in anterior-posterior foot placement relative to center-of-mass states?
<p>Data and code documentation for paper: “Does ankle push-off correct for errors in anterior-posterior foot placement relative to center-of-mass states?” All code in the current study can be accessed via the link (https://drive.google.com/drive/folders/1AnL9ajMk_Q3Gwzv_OrOp7A8DNwS8lniC?usp=sharing). The code was written by Jian Jin, Sjoerd Bruijn and Moira van Leeuwen. The data was from (van Leeuwen et al., 2020)and can be accessed through the link (https://doi.org/https://doi.org/10.5281/zenodo.4229851).</p>
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.