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914 results for “filter”
Station catalog in "Real-time earthquake location based on the Kalman filter formulation"
<p>Station catalog used to locate earthquakes in Parkfield, California, in the manuscript entitled "Real-time earthquake location based on the Kalman filter formulation" submitted to Geophysical Research Letters</p>
Waveforms, relocated earthquake and matched filter catalog of seismic swarm preceding the 2017 Mount Agung eruption
<p>Datasets for the manuscript:</p> <p>Sianipar, D., Ulfiana, E., and Sipayung, R. (2020), Seismic swarm preceding the 2017 Mount Agung eruption in Bali (Indonesia) enhanced by the matched filter approach (submitted) (preprint is available at EarthArxiv: <a href="https://eartharxiv.org/a7yx2/">https://eartharxiv.org/a7yx2/</a>)</p> <p>by Dimas Sianipar, Emi Ulfiana, and Renhard Sipayung (STMKG, BMKG, Indonesia).</p> <p>Files including:</p> <p>1) List of continuous waveforms</p> <p>2) HypoDD files: dt.cc, dt.ct, event.dat, hypoDD.reloc, phase.dat</p> <p>3) Processed (filtered) 407 template waveforms</p> <p>4) BMKG catalog</p> <p>5) MFT catalog in ZMAP format</p> <p>6) Table S1: template candidates</p> <p>7) Table S2: MFT catalog</p> <p>The compressed file (*.rar) has been successfully extracted in Ms. Windows OS using WinRAR.</p>
Use of GIMP Retinex filter to enhance the visibility of craquelure: example n. 1
<p>In the image, A represents the original image. It is a detail of the Portrait of a Young Girl, by Petrus Christus, (high resolution), courtesy Wikipedia. B is the grey-tone corresponding image. C is the image that we can obtain using the GIMP thresholding (threshold tone T = 127), applied to B. D is the image that we can have, applying GIMP Retinex on B (level=low, scale=250, scale div.=8, dynamics=4,0). E is the result of the thresholding of D (T = 127). F is the image obtained by means of the use of generic filter Erode on E. G is the result of applying Retinex (the same parameters used to obtain D) on D. H is the result of thresholding (T = 127), and I is the craquelure as we can see it after applying Erode. L is the Retinex filtered G (the same parameters as for G and D), M is the effect of thresholding and N the final result after using Erode. The example shows that the use of a thresholding on a Retinex filtered image gives much better results than the simple thresholding. This approach, that is the use of Retinex filtering, represents a fast manner for enhancing the visibility of craquelure. The iteration of the Retinex filter is also interesting for working on very dark areas.</p>
Use of GIMP Retinex filter to enhance the visibility of craquelure: example n. 6
<p>In the previously proposed figures (*), the GIMP Retinex filter had been applied to greyscale images, to enhance the visibility of dark craquelure in bright backgrounds. After processing, a black and white map had been obtained, suitable for any possible further processing, even for automatic processing. In (**), we have shown an example of the use of GIMP Retinex to observe the craquelure in dark areas. We observed that when craquelure is present in bright and dark scenes, it seems better to use the GIMP Retinex directly on the colour image. Here an example is given on the Lady Seated at a Virginal, a painting created by Dutch artist Johannes Vermeer (highest resolution), courtesy Wikipedia. A is a detail. A0, A1,A2, A3, and A4 are the images we can obtain by applying GIMP Retinex filter on A, level=low, scale=250, scale div.=8, with dynamics equal to 0,0 1,0 2,0 3,0 and 4,0 respectively. On this detail, it seems that the best manner to enhance the visibility of craquelure is that of using dynamics 2,0. In the lower panel, a larger detail of the painting is given, processed by the filter with dynamics 2,0. The example suggests that use of Retinex filtering on colour images represents a fast manner for enhancing in them the visibility of craquelure.</p> <p>(*) Figures at http://doi.org/10.5281/zenodo.3956124 http://doi.org/10.5281/zenodo.3956204 http://doi.org/10.5281/zenodo.3957439 http://doi.org/10.5281/zenodo.3957799<br> (**) http://doi.org/10.5281/zenodo.3959017</p>
Detection of very low frequency earthquakes based on matched-filter technique
<p>List of origin times of detected Very low frequency earthquakes based on Baba et al. (2020, Journal of Geophysical Research).</p> <p>Analysis period: January 2003-June 2019</p> <p>The content of this analysis is published in Geophysical Research Letters: <a href="https://doi.org/10.1029/2020GL088089">https://doi.org/10.1029/2020GL088089</a></p> <p>First column: year, second column: month, third column: day, forth column: hour, fifth column: minute, sixth column: second, seventh column: longitude, eighth column: latitude, ninth column: depth, and tenth column: region name. Times are described in JST (UT+9).</p> <p>Data Set S1 of supporting information of GRL corresponds to version 2. We updated the catalog because the information on Line 19035 in version 2 was incomplete.</p>
A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions
<p><strong>GNSS data:</strong></p> <ul> <li>Instrument: Javad antenna and Septentrio receiver</li> <li>sampling rate: 100 Hz</li> <li>Bandwidth of loop filter: auto adjust</li> <li>Relative positioning </li> <li>Baseline: ultra short with distance of 5 m</li> <li>files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations)</li> </ul> <p><strong>Accelerometer data:</strong></p> <ul> <li>Instrument: EpiSensor and Centaur Digitizer</li> <li>Sampling rate: 250 Hz</li> <li>Unit: counts</li> <li>unfiltered</li> <li>file: XKUK_centaur-6_1233_20200908_114500.seed</li> </ul> <p><strong>Angular rate data:</strong></p> <ul> <li>Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor)</li> <li>Sampling rate: 250 Hz</li> <li>Unit gyro: rad/s</li> <li>Unit accelerometer: g (gravitational acceleration)</li> <li>file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv</li> </ul> <p><strong>Robot Feedback:</strong></p> <ul> <li>Instrument: KUKA model AGILUS KR 6 R900 sixx</li> <li>Sampling rate: 250 Hz</li> <li>Unit translation: m</li> <li>Unit rotation: degree</li> <li>files: kuka_motion_*.txt, 1-4 are consecutive in time.</li> </ul> <p><strong>Experiments:</strong></p> <ul> <li>T: translations, R: rotations, XL, L, S denote the relative amplitudes</li> <li>10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data)</li> <li>9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data</li> </ul>
Automatic plankton image classification - can capsules and filters help coping with data set shift?
<p>This data set is related to the article 'Automatic plankton image classification - can capsules and filters help coping with data set shift?' published in 'Limnology and Oceanography: Methods' by Plonus <em>et al.</em> (2021).</p> <p>The images belong to the trainings set used to train the models in the aforementioned paper (training_) and three different additional data sets which were used to evaluate the performance of the trained models in application mode (fs446_; fs466_; fs534_). The Python-Script 'separate_files.py' can be used to move all the images in different folders for each data set and class respectively.</p>
Training CNNs with Low-Rank Filters for Efficient Image Classification: Trained Models
<p>Models from experiments referenced in the paper "Training CNNs with Low-Rank Filters for Efficient Image Classification", https://arxiv.org/abs/1511.06744</p> <p>Model names differ from those in the paper, but the csv files for each set of experiments relates the paper's name for the model and the real name of the model here:</p> <ul> <li>cifarma.csv: Network-in-Network CIFAR10 Models</li> <li>mitma.csv: MIT Places Models</li> <li>googlenetma.csv: GoogLeNet ILSVRC2012 Models</li> <li>vggma.csv: VGG-11 ILSVRC2012 Models</li> </ul> <p> </p> <p> </p>
A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
<p>To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.</p>
A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
<p>To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.</p>
A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
<p>To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.</p>
Dataset for ICDAR 21 paper "Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction"
<p>This is the dataset of the ICDAR 2021 conference paper "Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction".</p>
Dataset and R code used in "Environmental filtering governs consistent vertical zonation in sedimentary microbial communities across disconnected mountain lakes"
<p>Dataset and R code used for the manuscript:</p> <p>Von Eggers, J. M., Wisnoski, N. I., Calder, J. W., Capo, E., Groff, D. V., Krist, A. C., & Shuman, B. (2024). Environmental filtering governs consistent vertical zonation in sedimentary microbial communities across disconnected mountain lakes. <em>Environmental Microbiology</em>, 26(3), e16607.</p> <p>This dataset and code are also available on GitHub (<a href="https://github.com/jvoneggers/WYLakeSedMicrobes">https://github.com/jvoneggers/WYLakeSedMicrobes</a>).</p>
Fig. 1 in Use of filter papers to determine seroprevalence of Toxoplasma gondii among hunted ungulates in remote Peruvian Amazon
Fig. 1. Animals were hunted in the area surrounding the community of Nueva Esperanza, the only permanent settlement located along the Yavarí-Mirín River. The study site's geographic remoteness reflects extremely limited contact with domestic animals and humans, which eliminates the effect of spillover from domestic animals as a potential source of infection for wildlife. The data from this study are likely to shed light on the maintenance of T. gondii in its natural environment.
Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)
<p>Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)</p>
Reference genome choice and filtering thresholds jointly influence phylogenomic analyses
<p>Molecular phylogenies are a cornerstone of modern comparative biology and are commonly employed to investigate a range of biological phenomena, such as diversification rates, patterns in trait evolution, biogeography, and community assembly. Recent work has demonstrated that significant biases may be introduced into downstream phylogenetic analyses from processing genomic data; however, it remains unclear whether there are interactions among bioinformatic parameters or biases introduced through the choice of reference genome for sequence alignment and variant-calling. We address these knowledge gaps by employing a combination of simulated and empirical data sets to investigate to what extent the choice of reference genome in upstream bioinformatic processing of genomic data influences phylogenetic inference, as well as the way that reference genome choice interacts with bioinformatic filtering choices and phylogenetic inference method. We demonstrate that more stringent minor allele filters bias inferred trees away from the true species tree topology, and that these biased trees tend to be more imbalanced and have a higher center of gravity than the true trees. We find the greatest topological accuracy when filtering sites for minor allele count > 3–4 in our 51-taxa data sets, while tree center of gravity was closest to the true value when filtering for sites with minor allele count > 1-2. In contrast, filtering for missing data increased accuracy in the inferred topologies; however, this effect was small in comparison to the effect of minor allele filters and may be undesirable due to a subsequent mutation spectrum distortion. The bias introduced by these filters differs based on the reference genome used in short read alignment, providing further support that choosing a reference genome for alignment is an important bioinformatic decision with implications for downstream analyses. These results demonstrate that attributes of the study system and dataset (and their interaction) add important nuance for how best to assemble and filter short read genomic data for phylogenetic inference.</p>
High quality protein residues: Top2018 all-atom-filtered residues
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on all atoms in the residue. For the mainchain-only filtered set, see https://doi.org/10.5281/zenodo.4626149</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue‐level filtering, and the Top2018 best‐parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. All protein atoms have been considered in filtering; these files should be usable for any protein question. If your work is strictly limited to mainchain atoms (plus CB), there is a separate version that has been filtered on only mainchain atoms.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_fullfiltered_60pct_complete.txt and found in top2018_pdbs_full_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_full_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_fullll_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_fullfiltered_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_full_filted.csv and in USER records at the end on each .pdb file.</p> <p>All header information from the original structure has been preserved. This includes information about chains and residues no longer present in the file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>All atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p>Filtering documentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned and included residues with USER records. These include self-documenting USER DOC lines as follow:<br> USER DOC Lines marked with USER DEL list residues pruned by<br> USER DOC quality filtering.<br> USER DOC Format is chain:resseq:icode:reason_for_pruning<br> USER DOC Reasons for pruning are abbreviated as 1-letter codes: bcmgoa<br> USER DOC b=bfactor, c=real space correlation, m=2Fo-Fc mapvalue<br> USER DOC g=geometry outlier, o=steric overlap, a=alternate conformations<br> USER DOC Lines marked USER INC list the uninterrupted fragments of structure<br> USER DOC still included after pruning by quality filtering<br> USER DOC Format is chain1:resseq1:icode1:chain2:resseq2:icode2:fragment_length<br> USER DOC where 1 is the first and 2 the last residue of the fragment<br> USER DOC Line marked with USER PCT gives statistics for structure completeness</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 1.0 10.5281/zenodo.5115233 Jul 19, 2021<br> Initial version</p> <p>Version 2.0<br> Removed ~6000 additional residues due to mainchain atom clashes<br> Set case of filenames to unambiguous standard: all lowercase except L</p>
High quality protein residues: Top2018 mainchain-filtered residues
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on mainchain atoms. For the full-residue filtered set, see https://doi.org/10.5281/zenodo.5115232</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue‐level filtering, and the Top2018 best‐parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. As long as the question concerns mainchain protein atoms, these files should be usable as is. There is a separate version that has been filtered on all atoms that is suitable for sidechains.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_mcfilter_60pct_complete.txt and found in top2018_pdbs_mc_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_mc_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_mc_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_mcfilter_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_mc_filted.csv and in USER records at the end on each .pdb file.</p> <p>All header information from the original structure has been preserved. This includes information about chains and residues no longer present in the file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p>Sidechain atoms beyond CB have not been considered in the filtering. However, all sidechains have been included for residues that passed the mainchain filters. DO NOT use this set of files for serious questions involving sidechains. See our all-atom filtered dataset instead.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>Mainchain atoms are defined as N, CA, C, O, CB.<br> Note that CB is included, since its ideal position is defined by the other mainchan atoms.</p> <p>All mainchain atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p><br> Filtering doumentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned and included residues with USER records. These include self-documenting USER DOC lines as follow:<br> USER DOC Lines marked with USER DEL list residues pruned by<br> USER DOC quality filtering.<br> USER DOC Format is chain:resseq:icode:reason_for_pruning<br> USER DOC Reasons for pruning are abbreviated as 1-letter codes: bcmgoa<br> USER DOC b=bfactor, c=real space correlation, m=2Fo-Fc mapvalue<br> USER DOC g=geometry outlier, o=steric overlap, a=alternate conformations<br> USER DOC Lines marked USER INC list the uninterrupted fragments of structure<br> USER DOC still included after pruning by quality filtering<br> USER DOC Format is chain1:resseq1:icode1:chain2:resseq2:icode2:fragment_length<br> USER DOC where 1 is the first and 2 the last residue of the fragment<br> USER DOC Line marked with USER PCT gives statistics for structure completeness</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 0.9 10.5281/zenodo.4626150 Mar 21, 2021<br> Initial version</p> <p>Version 1.0 10.5281/zenodo.5115075 Jul 19, 2021<br> Split into 30, 50, 70, and 90% homology sets</p> <p>Version 2.0<br> Set case of filenames to unambiguous standard: all lowercase except L</p> <p>Version 2.01</p> <p>Added missing chain list for recommended hom70 set</p>
Particulate methylsulfonic acid (MSA), sodium and chloride concentrations from high-volume air filter samples over the Southern Ocean during austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Aerosol particles come from a variety of sources: a look at the chemical composition gives insights on the particle origin. Ion chromatography was performed for aerosol particles smaller than 10 micrometers (PM10 inlet), giving concentrations of sodium and chloride, as well as particulate methylsulfonic acid (MSA). For this, aerosol particles where sampled on quartz fibre filters for 24 hours each. The sampled filters were stored at -20 degrees C on the research vessel, transported frozen back to the chemistry lab of TROPOS and analysed for main ions. Temporal coverage is from December 20, 2016 to March 20, 2017. We give 24-hour quality controlled particulate MSA, sodium and chloride concentrations in microgram per cubic meter for the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ particulate_MSA_Sodium_Chloride_PM10, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul>
Inner filter effect correction for fluorescence measurements in microplates (ZINFE and NINFE) - experimental data
<p>Experimental data for the paper entitled <em>Inner Filter Effect Correction for Fluorescence Measurements in Microplates Using Variable Vertical Axis Focus</em> (https://doi.org/10.1021/acs.analchem.2c01031).</p> <p>Separate datasets are provided for data with background correction, without background correction, and absorbance-corrected data.</p> <p>All results were obtained using the online calculator service written in Javascript: https://ninfe.science (version 15.9.2021.).</p> <p>For additional details please visit: https://glymech.pharma.hr//GlyMech.html.</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.