cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/f78bmhr628.2
title: Temperature characterization of an high sensitivity tri-axial accelerometer through multiple thermometers

description: Temperature fluctuations significantly affect the precision of high sensitive accelerometers and gravimeters. This dataset comprises acceleration measurements obtained from a high-accuracy accelerometer, with background noise levels as low as 10^-7 m/s^2/sqrt(Hz). Additionally, it includes data from 11 thermometers strategically placed both inside the accelerometer casing and on its sensitive components. Analyzing these data, can clearly show how important is to measure the temperature in multiple points for effectively compensating for its effects over the acceleration measurements. 
Throughout the measurements, the accelerometer remained stationary on the laboratory floor, primarily detecting micro-seismic noise which could, to a first approximation, be disregarded. However, the accelerometer's output was not constant due to temperature variations, which were deliberately induced using heating mats affixed to the accelerometer's enclosure and a 260-watt lamp. Using these devices, we could create varying temperature fields and gradients.
Assuming that the accelerometer's output is solely influenced by the temperature variations, it can be predicted using only the thermometer measurements. In other words, the errors introduced by temperature can be reconstructed using the thermometer readings.
To reconstruct the effects of temperature on the accelerometer readings, supervised machine learning (ML) methods can be employed, with the accelerometer output serving as the desired output and the temperatures as the inputs.
The dataset is divided into three parts: two comprise data obtained solely by employing heating mats to manipulate the temperature, while the third (with the filename "resampled_2023-03-25_2023-04-14.csv") consists of data obtained exclusively through the use of the heating lamp. Consequently, first two subsets can be used for training, and the other for testing, clearly demonstrating the ML model's ability to generalize across different scenarios.
Each record in the dataset includes the following information: date ("Date"); accelerometer outputs ("z [m/s^2]", "x [m/s^2]", "y [m/s^2]"), with the z-axis predominantly aligned with the local vertical; temperatures of the accelerometer sensors ("T z [°C]", "T x [°C]", "T y [°C]"), measured by thermometers attached to the accelerometer's sensitive elements; temperatures of the six faces of the accelerometer box ("bottom x1 side [°C]", "x sensor face [°C]", "y sensor face [°C]", "bottom x2 side [°C]", "x elect face [°C]", "y ACQ face [°C]", "top [°C]"), where the bottom face features two thermometers; temperature of the air inside the box ("inside air [°C]"); external temperature ("external [°C]"); and altitude ("Altitude [m]"), measured by a barometer and thus reflecting variations in pressure.
The data from the accelerometers and the thermometers were synchronized between each other and then resampled at 1 Hz. No filtering was applied and missing data are due to technical stops.
DOI: 10.17632/f78bmhr628.1
title: Temperature characterization of an high sensitivity tri-axial accelerometer through multiple thermometers

description: Temperature fluctuations significantly affect the precision of high sensitive accelerometers and gravimeters. This dataset comprises acceleration measurements obtained from a high-accuracy accelerometer, with background noise levels as low as 10^-7 m/s^2/sqrt(Hz). Additionally, it includes data from 11 thermometers strategically placed both inside the accelerometer casing and on its sensitive components. Analyzing these data, can clearly show how important is to measure the temperature in multiple points for effectively compensating for its effects over the acceleration measurements. 
Throughout the measurements, the accelerometer remained stationary on the laboratory floor, primarily detecting micro-seismic noise which could, to a first approximation, be disregarded. However, the accelerometer's output was not constant due to temperature variations, which were deliberately induced using heating mats affixed to the accelerometer's enclosure and a 260-watt lamp. Using these devices, we could create varying temperature fields and gradients.
Assuming that the accelerometer's output is solely influenced by the temperature variations, it can be predicted using only the thermometer measurements. In other words, the errors introduced by temperature can be reconstructed using the thermometer readings.
To reconstruct the effects of temperature on the accelerometer readings, supervised machine learning (ML) methods can be employed, with the accelerometer output serving as the desired output and the temperatures as the inputs.
The dataset is divided into two parts: one comprises data obtained solely by employing heating mats to manipulate the temperature, while the other (with the filename "resampled_2023-03-25_2023-04-14.csv") consists of data obtained exclusively through the use of the heating lamp. Consequently, one subset can be used for training, and the other for testing, clearly demonstrating the ML model's ability to generalize across different scenarios.
Each record in the dataset includes the following information: date ("Date"); accelerometer outputs ("z [m/s^2]", "x [m/s^2]", "y [m/s^2]"), with the z-axis predominantly aligned with the local vertical; temperatures of the accelerometer sensors ("T z [°C]", "T x [°C]", "T y [°C]"), measured by thermometers attached to the accelerometer's sensitive elements; temperatures of the six faces of the accelerometer box ("bottom x1 side [°C]", "x sensor face [°C]", "y sensor face [°C]", "bottom x2 side [°C]", "x elect face [°C]", "y ACQ face [°C]", "top [°C]"), where the bottom face features two thermometers; temperature of the air inside the box ("inside air [°C]"); external temperature ("external [°C]"); and altitude ("Altitude [m]"), measured by a barometer and thus reflecting variations in pressure.
The data from the accelerometers and the thermometers were synchronized between each other and then resampled at 1 Hz. No filtering was applied and missing data are due to technical stops.
DOI: 10.17632/5j92cjhmdj.1
title: LINF_350054500

description: meiotic recombination protein DMC1
Leishmania infantum (strain JPCM5)
DOI: 10.17632/g9cdgwtyrr.1
title: LINF_360014000

description: Cyclic nucleotide-binding domain-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/kcp6vzr63s.1
title: LINF_350048700

description: Hypothetical protein - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/67nnknj8cw.1
title: Reversing Hall-Petch Supplementary Data

description: This dataset demonstrates the compelling story that the same GB functions as a strong obstacle to GB transmission giving rise to HP behavior but then also provides the genesis for IHP behavior through the formation of glissile GBDs that cause GB migration (non-HP deformation) at much lower stresses in the single dislocation regime. Further, this research offers strong evidence that the dislocation pile-up model, which naturally gives rise to HP physics, is also able to provide a simple explanation for IHP behavior by transitioning in a natural manner from GB transmission to GB sliding and migration.
DOI: 10.17632/42cdw45xsk.1
title: LINF_350055400

description: sphingomyelin/ceramide phosphorylethanolamine synthase - bifunctional
Leishmania infantum (strain JPCM5)
DOI: 10.17632/4bkr9b5ggv.1
title: LINF_350054900

description: protein of unknown function - conserved
Leishmania infantum (strain JPCM5)
DOI: 10.17632/p92kt26rht.1
title: LDHU3_13.1660

description: Programmed cell death 6 protein-like protein; Leishmania donovani (HU3 strain)
DOI: 10.15468/dl.zw4xsz
title: Occurrence Download

description: A dataset containing 157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.24215 -18.21966,-69.61875 -18.21966,-69.61875 -15.59051,-74.24215 -15.59051,-74.24215 -18.21966))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Podiceps occipitalis Garnot, 1826",
 "Year 2021-2025"
 ]
}

 The dataset includes 157 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000191-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/z4rhw2vsx5.1
title: Model output of agrochemical fate and transport on a catchment scale under extreme climate simulation scenarios.

description: The export of agrochemicals and their transformation products (TPs) following their application in the agricultural fields poses a threat to water quality. In this research, we use an integrated modelling framework to understand the impact of extreme climate events on the fate and transport of the agrochemical S-Metolachlor and two of its TPs (M-OXA, Metolachlor Oxanilic Acid and M-ESA, Metolachlor Ethyl Sulfonic Acid). This is done by coupling climate model outputs to the Zin-AgriTra agrochemical reactive transport model in four simulation scenarios. 1) Reference (2015-2018), 2) Very dry (2038-2041), 3) Very wet (2054-2057) and 4) High temperature (2096-2099) conditions of a selected RCP8.5 based regional climate scenario. The modelling framework is tested on an agricultural catchment, Wulka, in Burgenland, Austria. The model output of the four simulation scenarios and an additional model run S1_eq are presented in the dataset. The additional simulation scenario also represents the reference scenario but uses daily disaggregated rainfall to get a realistic comparison with the climate simulation scenarios. The overview of different model output parameters and their description are included.
DOI: 10.17632/h75yyspxv6.1
title: Proteomic Profiling of Salmonella enterica Strains from Environmental and Laboratory Sources: Comparative Analysis of Wastewater Isolates and Lab Strain UK-1

description: This dataset represents a comprehensive proteomic analysis of three Salmonella enterica strains, specifically two wastewater isolates—Salmonella enterica serovar Diarizonae (61:c:1,5,7) and S. enterica serovar Enteritidis (9:g,m:-)—alongside the well-characterized S. Typhimurium UK-1 lab strain. The preparation and analysis methods were carefully optimized to provide high-quality protein identifications and quantifications across these distinct strains, allowing for comparative proteomic analysis.

To generate the dataset, stationary-phase cultures of each strain were processed meticulously. Cultures were centrifuged at high speed (13,000 x g) at 4ºC to obtain cell pellets, which were washed twice—initially with PBS containing 5 mM EDTA to eliminate metal ion contaminants and then with PBS alone to enhance sample purity. The cells were lysed using a Sonifier Cell Disruptor, a sonication-based technique that preserves protein integrity, and then centrifuged again to yield a protein-rich supernatant, which was subsequently filter-sterilized using a 0.22-µm PES filter.

For protein separation, 25 µg of protein from each sample was loaded onto SDS-PAGE gels, with three biological replicates for each strain, ensuring reproducibility and robustness in measurements. Each gel lane was sectioned, and in-gel digestion with trypsin was performed to produce peptides suitable for mass spectrometry. The resulting peptide samples were analyzed using a 250-mm Ultrahigh-Performance Liquid Chromatography (UHPLC) system coupled to an Orbitrap Fusion mass spectrometer. This setup enabled high-resolution detection and precise mass spectrometry data collection.

Mass spectrometry data were analyzed using Sequest and X! Tandem, which compared spectral data against a Salmonella Uniprot database, assuming trypsin digestion specificity. Scaffold software validated peptide and protein identifications using a stringent probability threshold (&gt;95.0%) through the Scaffold Local FDR algorithm. Additionally, protein probabilities were refined with the Protein Prophet algorithm, requiring at least two peptides for protein identification. Proteins with overlapping peptide sequences were grouped to streamline interpretation, with the top-scoring hit reported.

The dataset achieved a peptide-level false discovery rate (FDR) of 0.35%, well below the commonly accepted threshold of 1%, and a protein FDR of 3.3%, within the standard 1-5% range, ensuring high reliability in protein identifications. Quantification across strains was performed using normalized spectral counts, and statistical significance was evaluated using t-test.

This dataset provides a resource for understanding the proteomic landscape across two environmentally derived Salmonella wastewater isolates and compared to a lab strain.
DOI: 10.17632/kjmxgbkj73.1
title: Supplementary material-Optimisation on multi-visit drone-truck combined pickup ande delivery routing problem with soft time windows

description: 1. Supplementary material
2. Instances
DOI: 10.17632/pp8kcdymcj.1
title: LDHU3_13.1790

description: Protein of unknown function - conserved; Leishmania donovani (HU3 strain)
DOI: 10.17632/c8vyzkhkwf.1
title: LINF_350057000

description: biopterin transporter | BT1
Leishmania infantum (strain JPCM5)
DOI: 10.17632/3hnyhjbjhc.1
title: LINF_350056600

description: RNA pseudouridylate synthase | RluA
Leishmania infantum (strain JPCM5)
DOI: 10.17632/twj4hy6kz8.1
title: LDHU3_13.1760

description: AAA+ ATPase; Leishmania donovani (HU3 strain)
DOI: 10.17632/6jw3bg9tdr
title: LDHU3_20.0580

description: Ribosomal RNA-processing protein 8 | RRP8
Leishmania donovani (HU3 strain)
DOI: 10.17632/6jw3bg9tdr.1
title: LDHU3_20.0580

description: Ribosomal RNA-processing protein 8 | RRP8
Leishmania donovani (HU3 strain)
DOI: 10.17632/p69z3bgyz2
title: LDHU3_13.1700

description: Ran-binding protein 1; Leishmania donovani (HU3 strain)
DOI: 10.17632/p69z3bgyz2.1
title: LDHU3_13.1700

description: Ran-binding protein 1; Leishmania donovani (HU3 strain)
DOI: 10.17632/v8k6c5s9cy.3
title: Credal Bird-2

description: Most datasets used for classification use hard labels. Credal Bird-2 was labeled uncertainly and imprecisely by contributors during crowdsourcing campaigns. Resulting in richer labels, modeled with the theory of belief funtions, which generalizes several reasoning frameworks with uncertainty. These datasets can be used with classical models using hard labels but also with probabilistic, fuzzy or even evidential models.

Dataset:
10 classes, 40 observations, 17 features (or raw pictures)

When using the dataset please cite:
A. Hoarau, C. Thierry, A. Martin, J.-C. Dubois, Y. Le Gall, "Datasets with rich labels for machine learning", in: FUZZ-IEEE, 2023.

Credal Bird-2 dataset
├── data
│ ├── classes.csv: Classes of the dataset
│ ├── X.csv: Features of the dataset
│ ├── X_512.csv: Learge 512 features vector of the dataset
│ ├── X_pictures.csv: Raw features (Pictures themselves)
│ ├── y.csv: Rich labels
│ └── y_true.csv: True labels
└── extra
 ├── y_hard.csv: Hard labels given during a new campaign
 ├── DATA_imperfect.csv: Imperfect answers given during the campaign
 ├── ITERATION_imperfect.csv: Imperfect 2nd step answers given during the campaign
 ├── EVENT_imperfect.csv: Contributors events
 ├── ID_imperfect.csv: Contributors IDs
 └── DATA_perfect.csv: Precise answers given during a new campaign

Credal Dog-7:
https://data.mendeley.com/datasets/4hz3wx6wm5
Credal Dog-4:
https://data.mendeley.com/datasets/44zpkbrpxb
Credal Dog-2:
https://data.mendeley.com/datasets/95mmjfrsyh
Credal Bird-10:
https://data.mendeley.com/datasets/9sz5vm8g7h
DOI: 10.17632/22f6j3k47r.1
title: LINF_350057500

description: protein kinase
Leishmania infantum (strain JPCM5)
DOI: 10.17632/9rtxmzckct.1
title: LDHU3_13.1830

description: Ubiquitin-conjugating enzyme E2 | UEV1; Leishmania donovani (HU3 strain)
DOI: 10.17632/d6z94395wp.1
title: LINF_350058300

description: WD repeat-containing protein
Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.vdhmb5
title: Occurrence Download

description: A dataset containing 48335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is one of (Observation.org, Nature data from around the World, iNaturalist Research-grade Observations, NABU|naturgucker)",
 "Geometry MULTIPOLYGON(((6.86548 53.58472,6.87525 53.59323,6.85269 53.58997,6.86548 53.58472)),((6.90697 53.64171,6.86519 53.64111,6.89177 53.62549,6.90697 53.64171)),((7.42847 53.72019,7.43095 53.7322,7.363 53.72729,7.42847 53.72019)),((7.93436 53.78984,7.84612 53.78699,7.97826 53.77784,7.93436 53.78984)),((7.09716 53.67936,7.02698 53.68517,6.85193 53.66265,7.09716 53.67936)),((7.74407 53.78123,7.66877 53.75391,7.8147 53.78137,7.74407 53.78123)),((7.50039 53.72198,7.63118 53.74877,7.46485 53.74084,7.50039 53.72198)),((7.23404 53.72278,7.13784 53.70588,7.3462 53.71806,7.23404 53.72278)),((6.74859 53.55777,6.81207 53.60444,6.65825 53.5991,6.74859 53.55777)),((9.43322 51.65223,9.68499 51.58263,9.54286 51.36268,9.73071 51.29477,9.7934 51.40733,10.1523 51.43109,10.36861 51.5866,10.65745 51.55894,10.70089 51.64161,10.57375 51.81081,10.64837 51.95448,10.56148 52.00464,10.96407 52.05733,11.08593 52.22895,10.9821 52.34239,11.07124 52.3728,10.93402 52.46176,10.97663 52.62412,10.79673 52.71425,10.76472 52.84241,11.31472 52.87846,11.5977 53.03666,11.34063 53.05522,10.92827 53.34851,9.90638 53.41597,9.2543 53.85983,8.61431 53.88203,8.48478 53.6557,8.65225 53.60728,8.65007 53.51113,8.4921 53.47448,8.55042 53.53773,8.28775 53.61175,8.23484 53.39807,8.06933 53.46684,8.1722 53.53578,8.02894 53.70757,7.31739 53.68338,7.03333 53.54506,6.99863 53.36028,7.26432 53.3256,7.21604 53.00717,7.05426 52.64391,6.71114 52.62835,6.69774 52.48647,6.98785 52.46917,7.06374 52.24129,7.68487 52.45622,7.9902 52.3094,7.89968 52.20732,8.0087 52.11569,7.88486 52.08323,7.98094 52.03609,8.51666 52.18498,8.29693 52.45606,8.65204 52.53158,8.70909 52.39534,8.85683 52.38918,9.09462 52.49793,9.11745 52.39882,8.96362 52.27948,9.07475 52.23396,8.98499 52.19426,9.15858 52.12009,9.32397 51.85504,9.45951 51.86128,9.37885 51.73808,9.43322 51.65223),(8.48114 53.22488,8.95647 53.14765,8.97916 53.04604,8.73277 53.03627,8.48114 53.22488)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (bb646dff-a905-4403-a49b-6d378c2cf0d9, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f, 28eb1a3f-1c15-4a95-931a-4af90ecb574d)",
 "TaxonKey is Orthoptera",
 "Year 2008-2024"
 ]
}

 The dataset includes 48335 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000189-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tfrgdr
title: Occurrence Download

description: A dataset containing 5959 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-92.61017 24.26973,68.61017 24.26973,68.61017 88.73027,-92.61017 88.73027,-92.61017 24.26973))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gammarus zaddachi Sexton, 1912"
 ]
}

 The dataset includes 5959 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000194-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28003040
title: Ocean extremes as a stress test for marine ecosystems and society [Dataset]

description: This database compiles a list of biological, physical and social impacts identified globally during the extreme marine heatwaves of 2023 and 2024. It includes published literature, news articles and social media reports
DOI: 10.6084/m9.figshare.28003040.v4
title: Summer of ocean extremes: 2023-2024 as a stress test for marine ecosystems and society [Dataset]

description: This database compiles a list of biological, physical and social impacts identified globally during the extreme marine heatwaves of 2023 and 2024. It includes published literature, news articles and social media reports
DOI: 10.15468/dl.eyw3pg
title: Occurrence Download

description: A dataset containing 48335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is one of (Observation.org, Nature data from around the World, iNaturalist Research-grade Observations, NABU|naturgucker)",
 "Geometry MULTIPOLYGON(((6.86548 53.58472,6.87525 53.59323,6.85269 53.58997,6.86548 53.58472)),((6.90697 53.64171,6.86519 53.64111,6.89177 53.62549,6.90697 53.64171)),((7.42847 53.72019,7.43095 53.7322,7.363 53.72729,7.42847 53.72019)),((7.93436 53.78984,7.84612 53.78699,7.97826 53.77784,7.93436 53.78984)),((7.09716 53.67936,7.02698 53.68517,6.85193 53.66265,7.09716 53.67936)),((7.74407 53.78123,7.66877 53.75391,7.8147 53.78137,7.74407 53.78123)),((7.50039 53.72198,7.63118 53.74877,7.46485 53.74084,7.50039 53.72198)),((7.23404 53.72278,7.13784 53.70588,7.3462 53.71806,7.23404 53.72278)),((6.74859 53.55777,6.81207 53.60444,6.65825 53.5991,6.74859 53.55777)),((9.43322 51.65223,9.68499 51.58263,9.54286 51.36268,9.73071 51.29477,9.7934 51.40733,10.1523 51.43109,10.36861 51.5866,10.65745 51.55894,10.70089 51.64161,10.57375 51.81081,10.64837 51.95448,10.56148 52.00464,10.96407 52.05733,11.08593 52.22895,10.9821 52.34239,11.07124 52.3728,10.93402 52.46176,10.97663 52.62412,10.79673 52.71425,10.76472 52.84241,11.31472 52.87846,11.5977 53.03666,11.34063 53.05522,10.92827 53.34851,9.90638 53.41597,9.2543 53.85983,8.61431 53.88203,8.48478 53.6557,8.65225 53.60728,8.65007 53.51113,8.4921 53.47448,8.55042 53.53773,8.28775 53.61175,8.23484 53.39807,8.06933 53.46684,8.1722 53.53578,8.02894 53.70757,7.31739 53.68338,7.03333 53.54506,6.99863 53.36028,7.26432 53.3256,7.21604 53.00717,7.05426 52.64391,6.71114 52.62835,6.69774 52.48647,6.98785 52.46917,7.06374 52.24129,7.68487 52.45622,7.9902 52.3094,7.89968 52.20732,8.0087 52.11569,7.88486 52.08323,7.98094 52.03609,8.51666 52.18498,8.29693 52.45606,8.65204 52.53158,8.70909 52.39534,8.85683 52.38918,9.09462 52.49793,9.11745 52.39882,8.96362 52.27948,9.07475 52.23396,8.98499 52.19426,9.15858 52.12009,9.32397 51.85504,9.45951 51.86128,9.37885 51.73808,9.43322 51.65223),(8.48114 53.22488,8.95647 53.14765,8.97916 53.04604,8.73277 53.03627,8.48114 53.22488)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (bb646dff-a905-4403-a49b-6d378c2cf0d9, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f, 28eb1a3f-1c15-4a95-931a-4af90ecb574d)",
 "TaxonKey is Orthoptera",
 "Year 2008-2024"
 ]
}

 The dataset includes 48335 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000192-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/mwc5syyknz.1
title: LINF_350058100

description: alpha-1-2-mannosyltransferase | ALG11
Leishmania infantum (strain JPCM5)
DOI: 10.17632/y76pwdt367.1
title: A new technology using Mixed Reality Surgical Navigation with UCRT frame to assist pelvic fracture reduction and fixation

description: Accurate reduction is a prerequisite for optimal screw placement. However, due to the complexity of the pelvic anatomical structure, the closed reduction process cannot be directly monitored. Consequently, the success of a closed reduction procedure relies on a certain degree of luck. Several reduction tools and techniques, including the Matta, Starr, and unlocking close reduction technique (UCRT) frames, have been developed to allow surgical navigation-assisted closed reduction to be performed. More recently, a new technology using mixed reality surgical navigation (MRSN) with UCRT frame has been developed that allows real-time monitoring of fracture fragment displacement to instruct pelvic fracture reduction. This new technology can also help determine the CS trajectory for fixation of the pelvic ring after good reduction.