100+ datasets found
  1. Z

    Data from: Ionisation of Atoms Determined by Kappa Refinement against 3D...

    • data.niaid.nih.gov
    • zenodo.org
    • +1more
    Updated Oct 11, 2024
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    Výborný, Karel (2024). Ionisation of Atoms Determined by Kappa Refinement against 3D Electron Diffraction Data [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10809375
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    Dataset updated
    Oct 11, 2024
    Dataset provided by
    Výborný, Karel
    Cabaj, Małgorzata
    Chintakindi, Hrushikesh
    Brázda, Petr
    Yörük, Emre
    Palatinus, Lukáš
    Suresh, Ashwin
    Sedláček, Ondřej
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The following submission contains the data reduction and processing files, dynamical refinement files, refinement files for theoretical structure factors, and CIF files of five inorganic compounds: quartz, natrolite, borane, caesium lead bromide, and lutetium aluminium garnet collected by 3D electron diffraction (3D ED) for studying ionisation of atoms by kappa refinement against 3D ED data.

    The data set for quartz was collected using the precession-assisted 3D ED method and for borane, caesium lead bromide, and lutetium aluminium garnet was collected using the continuous-rotation 3D ED method. Two data sets were collected from the same crystal for natrolite using continuous-rotation and precession-assisted 3D ED method. The data reduction and processing were done using PETS2 (1) software and the dynamical refinements were performed using the JANA2020 (2) software. The refinements were performed in two primary stages: IAM refinements (without taking into consideration the effects of charge transfer between the atoms) and kappa refinements (by taking into consideration the effects of charge transfer between the atoms).

    The submission also contains JANA2020 files of refinements against theoretical structure factors obtained using periodic DFT calculations and on the structure model obtained after IAM refinements of each of the experimental data sets.

    The folders are divided according to the compounds. Each folder contains the relevant data reduction and processing files (PETS2 files), dynamical refinement files (JANA2020 files for IAM and kappa refinements), refinement files for theoretical structure factors (JANA2020 files for IAM and kappa refinements) and final CIF files (for IAM and kappa refinements).

    References

    1. L. Palatinus, P. Brázda, M. Jelínek, J. Hrdá, G. Steciuk, M. Klementová, Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. Acta Cryst B 75, 512–522 (2019).
      
    2. V. Petříček, L. Palatinus, J. Plášil, M. Dušek, Jana2020 – a new version of the crystallographic computing system Jana. Zeitschrift für Kristallographie - Crystalline Materials 238, 271–282 (2023).
      

    The following table summarises the crystallographic information and data collection parameters for the data sets.

    Crystal data

    Sample

    Quartz

    Natrolite

    Natrolite

    Borane

    Caesium lead bromide

    Lutetium Aluminium Garnet

    Chemical formula

    SiO2

    Na2Al2Si3O12H4

    Na2Al2Si3O12H4

    B18H22

    CsPbBr3

    Lu3Al5O12

    Mr

    60.1

    380.2

    380.2

    108.4

    579.8

    851.8

    Crystal system, space group

    Trigonal, P3221

    Orthorhombic, Fdd2

    Orthorhombic, Fdd2

    Orthorhombic, Pccn

    Orthorhombic, Pbnm

    Cubic, Ia3 ̅d

    a, b, c (Å)

    4.9012(24), 4.9012, 5.4068(26)

    18.3885(1), 18.7183(32), 6.6569(11)

    18.4125(9), 18.7073(7), 6.6306(2)

    10.7789(17), 11.9869(16), 10.7338(17)

    8.1189(4), 8.359(4), 11.7593(5)

    11.9105(4), 11.9105(4), 11.9105(4)

    α, β, γ (°)

    90, 90, 120

    90, 90, 90

    90, 90, 90

    90, 90, 90

    90, 90, 90

    90, 90, 90

    V (Å3)

    112.48(8)

    2291.31(54)

    2283.90(16)

    1386.87(36)

    798.1(1)

    1689.6(1)

    Z

    3

    8

    8

    4

    4

    8

    Crystal size (mm)

    0.0004

    0.0005

    0.0005

    0.0015

    0.0004

    0.0003

    Data collection

    Diffractometer

    TEM FEI Technei G2 20

    TEM FEI Technei G2 20

    TEM FEI Technei G2 20

    TEM FEI Technei G2 20

    TEM FEI Technei G2 20

    TEM FEI Technei G2 20

    3D ED method

    Precession

    Precession

    Continuous Rotation

    Continuous Rotation

    Continuous Rotation

    Continuous Rotation

    Detector

    Medipix 3 ASI Cheetah

    Medipix 3 ASI Cheetah

    Medipix 3 ASI Cheetah

    Medipix 3 ASI Cheetah

    Medipix 3 ASI Cheetah

    Medipix 3 ASI Cheetah

    Radiation source

    LaB6

    LaB6

    LaB6

    LaB6

    LaB6

    LaB6

    Radiation type

    Electron, λ = 0.0251 Å

    Electron, λ = 0.0251 Å

    Electron, λ = 0.0251 Å

    Electron, λ = 0.0251 Å

    Electron, λ = 0.0251 Å

    Electron, λ = 0.0251 Å

    Temperature (K)

    293

    95

    95

    100

    153

    153

    (sin θ/λ)max (Å−1)

    1.25

    1.1

    1.00

    0.85

    1.00

    1.4

    No. of measured, independent andobserved [I > 3σ(I)] reflections

    3631, 1076, 1004

    15767, 6018, 4419

    12368, 4546, 4422

    30304, 13809, 4779

    16736, 422, 363

    23256, 1562, 1363

    Software used

    Data collection

    RATS software

    RATS software

    RATS software

    RATS software

    RATS software

    RATS software

    Data reduction and processing

    PETS2

    PETS2

    PETS2

    PETS2

    PETS2

    PETS2

    Refinement

    JANA2020

    JANA2020

    JANA2020

    JANA2020

    JANA2020

    JANA2020

    DFT calculation

    WIEN2k and Crystal23

    WIEN2k

    Crystal23

    Crystal23

    WIEN2k

    WIEN2k

  2. h

    the-pile-nih-refined-by-data-juicer

    • huggingface.co
    Updated Sep 15, 2015
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    Data-Juicer (2015). the-pile-nih-refined-by-data-juicer [Dataset]. https://huggingface.co/datasets/datajuicer/the-pile-nih-refined-by-data-juicer
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 15, 2015
    Dataset authored and provided by
    Data-Juicer
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    The Pile -- NIHExPorter (refined by Data-Juicer)

    A refined version of NIHExPorter dataset in The Pile by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 2.0G).

      Dataset Information
    

    Number of samples: 858,492 (Keep ~91.36% from the original dataset)

      Refining… See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/the-pile-nih-refined-by-data-juicer.
    
  3. f

    Crystallographic data and refinement statistics.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Veerendra Kumar; J. Sivaraman (2023). Crystallographic data and refinement statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0027543.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Veerendra Kumar; J. Sivaraman
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    aRsym = ∑|Ii–|/|Ii| where Ii is the intensity of the ith measurement, and is the mean intensity for that reflection.bReflections with I>σ was used in the refinement.cRwork = |Fobs–Fcalc|/|Fobs| where Fcalc and Fobs are the calculated and observed structure factor amplitudes, respectively.dRfree = as for Rwork, but for 5% of the total reflections chosen at random and omitted from refinement.eIndividual B-factor refinements were calculated.*The high resolution bin details are in the parenthesis.

  4. f

    Crystal parameters, data collection and structure refinement statistics.

    • plos.figshare.com
    • figshare.com
    xls
    Updated May 31, 2023
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    Yinnan Mu; Fu-Ming Lian; Yan-Bin Teng; Jingqun Ao; Yong-Liang Jiang; Yong-Xing He; Yuxing Chen; Cong-Zhao Zhou; Xinhua Chen (2023). Crystal parameters, data collection and structure refinement statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0057061.t001
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yinnan Mu; Fu-Ming Lian; Yan-Bin Teng; Jingqun Ao; Yong-Liang Jiang; Yong-Xing He; Yuxing Chen; Cong-Zhao Zhou; Xinhua Chen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    aThe values in parentheses refer to statistics in the highest bin.bRmerge = ∑hkl∑i|Ii(hkl)- |/∑hkl∑iIi(hkl), where Ii(hkl) is the intensity of an observation and is the mean value for its unique reflection; Summations are over all reflections.cR-factor = ∑h|Fo(h)-Fc(h)|/∑hFo(h), where Fo and Fc are the observed and calculated structure-factor amplitudes, respectively.dR-free was calculated with 5% of the data excluded from the refinement.eRoot-mean square-deviation from ideal values.fCategories were defined by Molprobity.

  5. h

    redpajama-c4-refined-by-data-juicer

    • huggingface.co
    Updated Apr 12, 2017
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    Data-Juicer (2017). redpajama-c4-refined-by-data-juicer [Dataset]. https://huggingface.co/datasets/datajuicer/redpajama-c4-refined-by-data-juicer
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 12, 2017
    Dataset authored and provided by
    Data-Juicer
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    RedPajama -- C4 (refined by Data-Juicer)

    A refined version of C4 dataset in RedPajama by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 832GB).

      Dataset Information
    

    Number of samples: 344,491,171 (Keep ~94.42% from the original dataset)

      Refining Recipe
    

    … See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/redpajama-c4-refined-by-data-juicer.

  6. f

    Data Collection and Refinement Statistics.

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Zhiyu Zhao; David Worthylake; Louis LeCour Jr; Grace A. Maresh; Seth H. Pincus (2023). Data Collection and Refinement Statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0052613.t002
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Zhiyu Zhao; David Worthylake; Louis LeCour Jr; Grace A. Maresh; Seth H. Pincus
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    aData in parenthesis pertain to the highest resolution shell (2.0 Å-1.9 Å).bRint = ∑|I - |/∑I, where I is the observed intensity of a measured reflection and is the mean intensity for all observation of symmetry-related reflections.cR factor = Σ |Foh – Fch|/Σ Foh, where Foh and Fch are the observed and calculated structure factor amplitudes for the 32,658 reflections h that were used in structure refinement.dR free = Σ |Foh – Fch|/Σ Foh, where Foh and Fch are the observed and calculated structure factor amplitudes pertaining to the 2,070 reflections h that were not used in structure refinement.

  7. f

    Data processing and refinement statistics.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Alejandro Buschiazzo; Romina Muiá; Nicole Larrieux; Tamara Pitcovsky; Juan Mucci; Oscar Campetella (2023). Data processing and refinement statistics. [Dataset]. http://doi.org/10.1371/journal.ppat.1002474.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS Pathogens
    Authors
    Alejandro Buschiazzo; Romina Muiá; Nicole Larrieux; Tamara Pitcovsky; Juan Mucci; Oscar Campetella
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    aValues in parentheses apply to the high-resolution shell.b; Nh, multiplicity for each reflection; Ii, the intensity of the ith observation of reflection h; , the mean of the intensity of all observations of reflection h, with ; is taken over all reflections; is taken over all observations of each reflection.c; ; Rcryst and Rfree were calculated using the working and test hkl reflection sets, respectively.dTotal refined protein residues equal 3172, from which 28 terminal amino acids (the N- and C-termini on the 9 chains; plus residues: TS#399, TS#409 (in chains A, B & C), Fab#27, Fab#29 (in chain H), Fab#137, Fab#139 (in chain I), all flanking unmodeled gaps) were not included in the Ramachandran analysis (as implemented in Coot v 0.6.2-pre-1).

  8. d

    Estimates of mineral abundances based on Rietveld refinement of X-ray...

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Estimates of mineral abundances based on Rietveld refinement of X-ray diffraction data from mill tailings and other ore processing materials [Dataset]. https://catalog.data.gov/dataset/estimates-of-mineral-abundances-based-on-rietveld-refinement-of-x-ray-diffraction-data-fro
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    This worksheet displays the results of mineral abundance estimates based on Rietveld refinement of X-ray diffraction (XRD) analyses of mill tailings and other ore processing materials from worldwide localities. Data are also provided to show variation in mineral abundance estimates for subsplits in individual samples. Samples were analyzed using a PANalytical X'Pert Pro diffractometer using Cu radiation and the results interpreted using Highscore Plus v.4.7.

  9. h

    the-pile-uspto-refined-by-data-juicer

    • huggingface.co
    Updated Oct 23, 2023
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    Data-Juicer (2023). the-pile-uspto-refined-by-data-juicer [Dataset]. https://huggingface.co/datasets/datajuicer/the-pile-uspto-refined-by-data-juicer
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 23, 2023
    Dataset authored and provided by
    Data-Juicer
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    The Pile -- USPTO (refined by Data-Juicer)

    A refined version of USPTO dataset in The Pile by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 18G).

      Dataset Information
    

    Number of samples: 4,516,283 (Keep ~46.77% from the original dataset)

      Refining Recipe
    

    … See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/the-pile-uspto-refined-by-data-juicer.

  10. h

    redpajama-arxiv-refined-by-data-juicer

    • huggingface.co
    Updated Oct 24, 2023
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    Data-Juicer (2023). redpajama-arxiv-refined-by-data-juicer [Dataset]. https://huggingface.co/datasets/datajuicer/redpajama-arxiv-refined-by-data-juicer
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 24, 2023
    Dataset authored and provided by
    Data-Juicer
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    RedPajama -- ArXiv (refined by Data-Juicer)

    A refined version of ArXiv dataset in RedPajama by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 85GB).

      Dataset Information
    

    Number of samples: 1,655,259 (Keep ~95.99% from the original dataset)

      Refining Recipe… See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/redpajama-arxiv-refined-by-data-juicer.
    
  11. d

    Envrionmental DNA data for Refinement of eDNA as an early monitoring tool at...

    • catalog.data.gov
    • data.usgs.gov
    Updated Jul 6, 2024
    + more versions
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    U.S. Geological Survey (2024). Envrionmental DNA data for Refinement of eDNA as an early monitoring tool at the landscape-level: Data [Dataset]. https://catalog.data.gov/dataset/envrionmental-dna-data-for-refinement-of-edna-as-an-early-monitoring-tool-at-the-landscape
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    These environmental DNA data and corresponding water quality data were collected and analyzed by the Fish and Wildlife Service in 2017. The samples were collected from 4 sites in pools 17 and 18 in the Upper Mississippi River on 3 sampling trips. The data was used to study occupancy modeling of eDNA data and determine optimal sampling effort required for reliable detection of invasive Bighead Carp and Silver Carp in streams with similar attributes at the Mississippi River.

  12. t

    Particle detection by means of neural networks and synthetic training data...

    • service.tib.eu
    Updated Nov 28, 2024
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    (2024). Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry (data) - Vdataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/rdr-doi-10-35097-1333
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    Dataset updated
    Nov 28, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    TechnicalRemarks: This repository contains the supplementary data to our contribution "Particle Detection by means of Neural Networks and Synthetic Training Data Refinement in Defocusing Particle Tracking Velocimetry" to the 2022 Measurement Science and Technology special issue on the topic “Machine Learning and Data Assimilation techniques for fluid flow measurements”. This data includes annotated images used for the training of neural networks for particle detection on DPTV recordings as well as unannotated particle images used for training of the image-to-image translation networks for the generation of refined synthetic training data, as presented in the manuscript. The neural networks for particle detection trained on the aforementioned data are contained in this repository as well. An explanation on the use of this data and the trained neural networks, containing an example script can be found on GitHub (https://github.com/MaxDreisbach/DPTV_ML_Particle_detection)

  13. f

    Data collection, phasing and refinement statistics.

    • figshare.com
    xls
    Updated Jun 2, 2023
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    Peter Canning; Dean Rea; Rory E. Morty; Vilmos Fülöp (2023). Data collection, phasing and refinement statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0079349.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Peter Canning; Dean Rea; Rory E. Morty; Vilmos Fülöp
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Numbers in parentheses refer to values in the highest resolution shell.aRsym = ΣjΣh|Ih,j−|/ΣjΣh where Ih,j is the jth observation of reflection h, and is the mean intensity of that reflection.bRcryst = Σ||Fobs|−|Fcalc||/Σ|Fobs| where Fobs and Fcalc are the observed and calculated structure factor amplitudes, respectively.cRfree is equivalent to Rcryst for a 4% subset of reflections not used in the refinement.

  14. f

    Data collection and refinement statistics.

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    John J. Miles; Anna M. Bulek; David K. Cole; Emma Gostick; Andrea J. A. Schauenburg; Garry Dolton; Vanessa Venturi; Miles P. Davenport; Mai Ping Tan; Scott R. Burrows; Linda Wooldridge; David A. Price; Pierre J. Rizkallah; Andrew K. Sewell (2023). Data collection and refinement statistics. [Dataset]. http://doi.org/10.1371/journal.ppat.1001198.t001
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    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS Pathogens
    Authors
    John J. Miles; Anna M. Bulek; David K. Cole; Emma Gostick; Andrea J. A. Schauenburg; Garry Dolton; Vanessa Venturi; Miles P. Davenport; Mai Ping Tan; Scott R. Burrows; Linda Wooldridge; David A. Price; Pierre J. Rizkallah; Andrew K. Sewell
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    aValues in parentheses are for the highest resolution shell.bValues in parentheses are target values.N.B. One crystal was used for the full data set.

  15. f

    Data collection and refinement statistics.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Agnieszka J. Pietrzyk; Anna Bujacz; Jochen Mueller-Dieckmann; Malgorzata Lochynska; Mariusz Jaskolski; Grzegorz Bujacz (2023). Data collection and refinement statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0061303.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Agnieszka J. Pietrzyk; Anna Bujacz; Jochen Mueller-Dieckmann; Malgorzata Lochynska; Mariusz Jaskolski; Grzegorz Bujacz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    aValues in parentheses are for the highest resolution shell.bRmerge = ∑h∑j | Ihj - h> |/∑h∑j Ihj, where Ihj is the intensity of observation j of reflection h.cRwork = ∑h | | Fo| - | Fc| |/∑h | Fo| for all reflections, where Fo and Fc are the observed and calculated structure factors, respectively. Rfree is calculated analogously for the test reflections, randomly selected and excluded from the refinement.

  16. Petroleum Data: Refining and Processing Application Programming Interface...

    • catalog.data.gov
    • data.amerigeoss.org
    Updated Jul 6, 2021
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    U.S. Energy Information Administration (2021). Petroleum Data: Refining and Processing Application Programming Interface (API) [Dataset]. https://catalog.data.gov/dataset/petroleum-data-refining-and-processing-application-programming-interface-api
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    Dataset updated
    Jul 6, 2021
    Dataset provided by
    Energy Information Administrationhttp://www.eia.gov/
    Description

    Data on petroleum inputs, production, yield, and capacity. Weekly, monthly and annual data available. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

  17. h

    the-pile-freelaw-refined-by-data-juicer

    • huggingface.co
    Updated Apr 28, 2010
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    Data-Juicer (2010). the-pile-freelaw-refined-by-data-juicer [Dataset]. https://huggingface.co/datasets/datajuicer/the-pile-freelaw-refined-by-data-juicer
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 28, 2010
    Dataset authored and provided by
    Data-Juicer
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    The Pile -- FreeLaw (refined by Data-Juicer)

    A refined version of FreeLaw dataset in The Pile by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available here (About 45GB).

      Dataset Information
    

    Number of samples: 2,942,612 (Keep ~82.61% from the original dataset)

      Refining Recipe… See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/the-pile-freelaw-refined-by-data-juicer.
    
  18. f

    Data collection and refinement statistics.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Feb 20, 2013
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    Uchtenhagen, Hannes; Achour, Adnane; Friemann, Rosmarie; Raszewski, Grzegorz; Nilsson, Lennart; Spetz, Anna-Lena (2013). Data collection and refinement statistics. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001733292
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    Dataset updated
    Feb 20, 2013
    Authors
    Uchtenhagen, Hannes; Achour, Adnane; Friemann, Rosmarie; Raszewski, Grzegorz; Nilsson, Lennart; Spetz, Anna-Lena
    Description

    aNumber in parentheses indicate the outer-resolution shell.bRmerge = ∑hkl ∑i |Ii (hkl) - 〈I (hkl) 〉|/∑hkl ∑i Ii (hkl), where Ii(hkl) is the ith observation of reflection hkl and 〈I (hkl) 〉 is the weighted average intensity for all observations i of reflection hkl.cRcryst = Σhkl = ∑hkl|Fobs − Fcalc|/Σhkl |Fobs|.dRfree is the same as Rcryst except for 5% of the data excluded from the refinement.eSum of the TLS and Residual B-factor contributions.

  19. d

    Data from: ADAPTIVE MODEL REFINEMENT FOR THE IONOSPHERE AND THERMOSPHERE

    • catalog.data.gov
    • s.cnmilf.com
    • +2more
    Updated Apr 10, 2025
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    Dashlink (2025). ADAPTIVE MODEL REFINEMENT FOR THE IONOSPHERE AND THERMOSPHERE [Dataset]. https://catalog.data.gov/dataset/adaptive-model-refinement-for-the-ionosphere-and-thermosphere
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    Dataset updated
    Apr 10, 2025
    Dataset provided by
    Dashlink
    Description

    ADAPTIVE MODEL REFINEMENT FOR THE IONOSPHERE AND THERMOSPHERE ANTHONY M. D’AMATO∗, AARON J. RIDLEY∗∗, AND DENNIS S. BERNSTEIN∗∗∗ Abstract. Mathematical models of physical phenomena are of critical importance in virtually all applications of science and technology. This paper addresses the problem of how to use data to improve the fidelity of a given model. We approach this problem using retrospective cost optimization, a novel technique that uses data to recursively update an unknown subsystem interconnected to a known system. Applications of this research are relevant to a wide range of applications that depend on large-scale models based on firstprinciples physics, such as the Global Ionosphere-Thermosphere Model (GITM). Using GITM as the truth model, we demonstrate that measurements can be used to identify unknown physics. Specifically, we estimate static thermal conductivity parameters, and we identify a dynamic cooling process.

  20. f

    Data Collection and Refinement Statistics.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Apr 25, 2013
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    Fu, Panhan; Zhang, Xiaoqing; Xu, Li; Xia, Zongping; Wang, Chong; Jin, Mengmeng; Zhu, Yongqun (2013). Data Collection and Refinement Statistics. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001731349
    Explore at:
    Dataset updated
    Apr 25, 2013
    Authors
    Fu, Panhan; Zhang, Xiaoqing; Xu, Li; Xia, Zongping; Wang, Chong; Jin, Mengmeng; Zhu, Yongqun
    Description

    aThe data for the highest resolution shell are shown in parentheses.bRfree is calculated using 10% of the total number of reflections.

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Výborný, Karel (2024). Ionisation of Atoms Determined by Kappa Refinement against 3D Electron Diffraction Data [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10809375

Data from: Ionisation of Atoms Determined by Kappa Refinement against 3D Electron Diffraction Data

Related Article
Explore at:
Dataset updated
Oct 11, 2024
Dataset provided by
Výborný, Karel
Cabaj, Małgorzata
Chintakindi, Hrushikesh
Brázda, Petr
Yörük, Emre
Palatinus, Lukáš
Suresh, Ashwin
Sedláček, Ondřej
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The following submission contains the data reduction and processing files, dynamical refinement files, refinement files for theoretical structure factors, and CIF files of five inorganic compounds: quartz, natrolite, borane, caesium lead bromide, and lutetium aluminium garnet collected by 3D electron diffraction (3D ED) for studying ionisation of atoms by kappa refinement against 3D ED data.

The data set for quartz was collected using the precession-assisted 3D ED method and for borane, caesium lead bromide, and lutetium aluminium garnet was collected using the continuous-rotation 3D ED method. Two data sets were collected from the same crystal for natrolite using continuous-rotation and precession-assisted 3D ED method. The data reduction and processing were done using PETS2 (1) software and the dynamical refinements were performed using the JANA2020 (2) software. The refinements were performed in two primary stages: IAM refinements (without taking into consideration the effects of charge transfer between the atoms) and kappa refinements (by taking into consideration the effects of charge transfer between the atoms).

The submission also contains JANA2020 files of refinements against theoretical structure factors obtained using periodic DFT calculations and on the structure model obtained after IAM refinements of each of the experimental data sets.

The folders are divided according to the compounds. Each folder contains the relevant data reduction and processing files (PETS2 files), dynamical refinement files (JANA2020 files for IAM and kappa refinements), refinement files for theoretical structure factors (JANA2020 files for IAM and kappa refinements) and final CIF files (for IAM and kappa refinements).

References

  1. L. Palatinus, P. Brázda, M. Jelínek, J. Hrdá, G. Steciuk, M. Klementová, Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. Acta Cryst B 75, 512–522 (2019).
    
  2. V. Petříček, L. Palatinus, J. Plášil, M. Dušek, Jana2020 – a new version of the crystallographic computing system Jana. Zeitschrift für Kristallographie - Crystalline Materials 238, 271–282 (2023).
    

The following table summarises the crystallographic information and data collection parameters for the data sets.

Crystal data

Sample

Quartz

Natrolite

Natrolite

Borane

Caesium lead bromide

Lutetium Aluminium Garnet

Chemical formula

SiO2

Na2Al2Si3O12H4

Na2Al2Si3O12H4

B18H22

CsPbBr3

Lu3Al5O12

Mr

60.1

380.2

380.2

108.4

579.8

851.8

Crystal system, space group

Trigonal, P3221

Orthorhombic, Fdd2

Orthorhombic, Fdd2

Orthorhombic, Pccn

Orthorhombic, Pbnm

Cubic, Ia3 ̅d

a, b, c (Å)

4.9012(24), 4.9012, 5.4068(26)

18.3885(1), 18.7183(32), 6.6569(11)

18.4125(9), 18.7073(7), 6.6306(2)

10.7789(17), 11.9869(16), 10.7338(17)

8.1189(4), 8.359(4), 11.7593(5)

11.9105(4), 11.9105(4), 11.9105(4)

α, β, γ (°)

90, 90, 120

90, 90, 90

90, 90, 90

90, 90, 90

90, 90, 90

90, 90, 90

V (Å3)

112.48(8)

2291.31(54)

2283.90(16)

1386.87(36)

798.1(1)

1689.6(1)

Z

3

8

8

4

4

8

Crystal size (mm)

0.0004

0.0005

0.0005

0.0015

0.0004

0.0003

Data collection

Diffractometer

TEM FEI Technei G2 20

TEM FEI Technei G2 20

TEM FEI Technei G2 20

TEM FEI Technei G2 20

TEM FEI Technei G2 20

TEM FEI Technei G2 20

3D ED method

Precession

Precession

Continuous Rotation

Continuous Rotation

Continuous Rotation

Continuous Rotation

Detector

Medipix 3 ASI Cheetah

Medipix 3 ASI Cheetah

Medipix 3 ASI Cheetah

Medipix 3 ASI Cheetah

Medipix 3 ASI Cheetah

Medipix 3 ASI Cheetah

Radiation source

LaB6

LaB6

LaB6

LaB6

LaB6

LaB6

Radiation type

Electron, λ = 0.0251 Å

Electron, λ = 0.0251 Å

Electron, λ = 0.0251 Å

Electron, λ = 0.0251 Å

Electron, λ = 0.0251 Å

Electron, λ = 0.0251 Å

Temperature (K)

293

95

95

100

153

153

(sin θ/λ)max (Å−1)

1.25

1.1

1.00

0.85

1.00

1.4

No. of measured, independent andobserved [I > 3σ(I)] reflections

3631, 1076, 1004

15767, 6018, 4419

12368, 4546, 4422

30304, 13809, 4779

16736, 422, 363

23256, 1562, 1363

Software used

Data collection

RATS software

RATS software

RATS software

RATS software

RATS software

RATS software

Data reduction and processing

PETS2

PETS2

PETS2

PETS2

PETS2

PETS2

Refinement

JANA2020

JANA2020

JANA2020

JANA2020

JANA2020

JANA2020

DFT calculation

WIEN2k and Crystal23

WIEN2k

Crystal23

Crystal23

WIEN2k

WIEN2k

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