cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/28766591.v1
title: High-Order Computation of Gradient Flows and Barycenters in Generalized Wasserstein Spaces

description: This dissertation presents a robust, scalable, and highly accurate numerical framework for computing gradient flows and barycenters in generalized Wasserstein spaces. These spaces are constructed by equipping the set of positive measures on a ground metric space with mean-field control-based extensions of the Benamou–Brenier formula. Gradient flow trajectories in such spaces represent solutions to a class of partial differential equations (PDEs) that model dissipative dynamics. A notable example is the porous medium equation which frequently appears in applications involving non-linear diffusion.

The first part of the work develops two classical first-order finite element schemes for solving the porous medium equation. These schemes are carefully designed to preserve key properties—mass conservation, positivity, and energy dissipation—implied by its underlying gradient flow structure. Their performance is validated through standard benchmark problems, and their convergence rates are numerically demonstrated.

Recognizing inherent limitations in extending classical methods to high-order temporal accuracy, a novel variational method is then introduced that obtains PDE solutions as gradient flow trajectories in Wasserstein spaces via mean-field control. This formulation is discretized using high-order spacetime finite elements and solved via the primal-dual hybrid gradient method. The efficacy of the numerical scheme is illustrated through application on a heat equation system and a porous medium equation system containing source terms, and high-order convergence rates are demonstrated in one and two spatial dimensions.

Building upon these developments, this work culminates in a generalized, mean-field control formulation of the Wasserstein barycenter problem, also discretized using high-order finite elements, and solved via the primal-dual hybrid gradient method.
The proposed formulation handles systems of unnormalized densities without relying on entropy regularization and incorporates reaction–diffusion dynamics. The robustness of the proposed method is demonstrated through several numerical experiments that compute barycenters in a three-dimensional Euclidean domain and on embedded two-dimensional manifolds.
DOI: 10.13130/rd_unimi/u3wu1r/usatiy
title: 000542.hea
DOI: 10.13130/rd_unimi/u3wu1r/y73ulj
title: 000982.hea
DOI: 10.13130/rd_unimi/u3wu1r/5ghbub
title: 000459.hea
DOI: 10.13130/rd_unimi/u3wu1r/myu3pd
title: 000310.hea
DOI: 10.13130/rd_unimi/u3wu1r/wvifb1
title: 000578.hea
DOI: 10.13130/rd_unimi/u3wu1r/cpnpzw
title: 000855.hea
DOI: 10.13130/rd_unimi/u3wu1r/xl7di7
title: 000339.hea
DOI: 10.13130/rd_unimi/u3wu1r/k1oayi
title: 000627.hea
DOI: 10.13130/rd_unimi/u3wu1r/uuvjor
title: 000381.hea
DOI: 10.13130/rd_unimi/u3wu1r/5dqram
title: 000273.hea
DOI: 10.13130/rd_unimi/u3wu1r/bo0wqh
title: 000956.hea
DOI: 10.13130/rd_unimi/u3wu1r/wjx61d
title: 000957.hea
DOI: 10.13130/rd_unimi/u3wu1r/xlluzb
title: 000687.hea
DOI: 10.13130/rd_unimi/u3wu1r/8c3oks
title: 000839.hea
DOI: 10.13130/rd_unimi/u3wu1r/j4wkvz
title: 000308.hea
DOI: 10.13130/rd_unimi/u3wu1r/uniuop
title: 000514.hea
DOI: 10.13130/rd_unimi/u3wu1r/cl79vf
title: 000294.hea
DOI: 10.13130/rd_unimi/u3wu1r/12p4mm
title: 000368.hea
DOI: 10.15468/gyy0vt
title: Atlas des amphibiens reptiles du Nord et du Pas-de-Calais (GON) - Atlas des amphibiens-reptiles du Nord et du Pas-de-Calais (GON)

description: Les données sont issues des contributions au Système d'Information Régional sur la Faune (SIRF) rattachées aux groupes "Amphibiens" et "Reptiles" hormis les données déclarées confidentielles par les contributeurs et utilisées dans le cadre de la rédaction de l'atlas des amphibiens-reptiles
DOI: 10.13130/rd_unimi/u3wu1r/9ql5ra
title: 000665.hea
DOI: 10.13130/rd_unimi/u3wu1r/c2y5lt
title: 000150.hea
DOI: 10.13130/rd_unimi/u3wu1r/od43wn
title: 000092.hea
DOI: 10.13130/rd_unimi/u3wu1r/ow6ryh
title: 000625.hea
DOI: 10.13130/rd_unimi/u3wu1r/f7dqgo
title: 000201.hea
DOI: 10.13130/rd_unimi/u3wu1r/tt54hp
title: 000921.hea
DOI: 10.13130/rd_unimi/u3wu1r/zdragn
title: 000643.hea
DOI: 10.13130/rd_unimi/u3wu1r/r8bkrg
title: 000505.hea
DOI: 10.60893/figshare.sss.30543266
title: 02059-02

description: PFBS in negative polarity
DOI: 10.13130/rd_unimi/u3wu1r/xobdm2
title: 000963.hea
DOI: 10.60893/figshare.sss.30543266.v1
title: 02059-02

description: PFBS in negative polarity
DOI: 10.60893/figshare.sss.30543269
title: 02059-01

description: PFBS in positive polarity
DOI: 10.13130/rd_unimi/u3wu1r/wneadz
title: 000384.hea
DOI: 10.60893/figshare.sss.30543269.v1
title: 02059-01

description: PFBS in positive polarity
DOI: 10.60893/figshare.sss.30543275
title: 02058-02

description: PFOS in negative polarity
DOI: 10.60893/figshare.sss.30543275.v1
title: 02058-02

description: PFOS in negative polarity
DOI: 10.13130/rd_unimi/u3wu1r/vpgmbz
title: 000653.hea
DOI: 10.13130/rd_unimi/u3wu1r/zbski9
title: 000295.hea
DOI: 10.60893/figshare.sss.30543278
title: 02058-01

description: PFOS in positive polarity
DOI: 10.60893/figshare.sss.30543278.v1
title: 02058-01

description: PFOS in positive polarity
DOI: 10.13130/rd_unimi/u3wu1r/hysrxr
title: 000137.hea
DOI: 10.13130/rd_unimi/u3wu1r/i7lqxa
title: 000515.hea
DOI: 10.13130/rd_unimi/u3wu1r/l2t8gx
title: 000848.hea
DOI: 10.13130/rd_unimi/u3wu1r/banbis
title: 000747.hea
DOI: 10.13130/rd_unimi/u3wu1r/d1ecrz
title: 000994.hea
DOI: 10.13130/rd_unimi/u3wu1r/nbl197
title: 000694.hea
DOI: 10.13130/rd_unimi/u3wu1r/maoqod
title: 000211.hea
DOI: 10.13130/rd_unimi/u3wu1r/v8ydr2
title: 000323.hea
DOI: 10.13130/rd_unimi/u3wu1r/0y9kuz
title: 000429.hea
DOI: 10.13130/rd_unimi/u3wu1r/oih6c9
title: 000110.hea
DOI: 10.13130/rd_unimi/u3wu1r/jdmj2g
title: 000052.hea
DOI: 10.13130/rd_unimi/u3wu1r/uqjitj
title: 000241.hea
DOI: 10.13130/rd_unimi/u3wu1r/knwqgy
title: 000778.hea
DOI: 10.13130/rd_unimi/u3wu1r/tcfd9t
title: 000379.hea
DOI: 10.13130/rd_unimi/u3wu1r/hlxkae
title: 000773.hea
DOI: 10.13130/rd_unimi/u3wu1r/qw2plb
title: 000551.hea
DOI: 10.13130/rd_unimi/u3wu1r/pcxibi
title: 000904.hea
DOI: 10.13130/rd_unimi/u3wu1r/9u8wzd
title: 000178.hea
DOI: 10.13130/rd_unimi/u3wu1r/eyg3v3
title: 000709.hea
DOI: 10.13130/rd_unimi/u3wu1r/iyxm5c
title: 000385.hea
DOI: 10.13130/rd_unimi/u3wu1r/wvlz41
title: 000222.hea
DOI: 10.13130/rd_unimi/u3wu1r/kmxnmy
title: 000473.hea
DOI: 10.13130/rd_unimi/u3wu1r/123slo
title: 000693.hea
DOI: 10.13130/rd_unimi/u3wu1r/mtfxpl
title: 000805.hea
DOI: 10.13130/rd_unimi/u3wu1r/rl7jdj
title: 000883.hea
DOI: 10.13130/rd_unimi/u3wu1r/4rkajy
title: 000014.hea
DOI: 10.13130/rd_unimi/u3wu1r/z9tqld
title: 000697.hea
DOI: 10.13130/rd_unimi/u3wu1r/lps7c2
title: 000724.hea
DOI: 10.13130/rd_unimi/u3wu1r/cotgjn
title: 000600.hea
DOI: 10.13130/rd_unimi/u3wu1r/ogfgqs
title: 000523.hea
DOI: 10.13130/rd_unimi/u3wu1r/17crcs
title: 000334.hea
DOI: 10.13130/rd_unimi/u3wu1r/ta2v9p
title: 000672.hea
DOI: 10.13130/rd_unimi/u3wu1r/kdoaxm
title: 000315.hea
DOI: 10.13130/rd_unimi/u3wu1r/obgkof
title: 000165.hea
DOI: 10.13130/rd_unimi/u3wu1r/ucpckd
title: 000103.hea
DOI: 10.13130/rd_unimi/u3wu1r/9yqdrt
title: 000585.hea
DOI: 10.13130/rd_unimi/u3wu1r/inphdg
title: 000386.hea
DOI: 10.13130/rd_unimi/u3wu1r/tpwmjx
title: 000790.hea
DOI: 10.13130/rd_unimi/u3wu1r/fyyiwc
title: 000461.hea
DOI: 10.13130/rd_unimi/u3wu1r/2z9luv
title: 000589.hea
DOI: 10.13130/rd_unimi/u3wu1r/ixq4cf
title: 000363.hea
DOI: 10.13130/rd_unimi/u3wu1r/p9lrpy
title: 000337.hea
DOI: 10.15468/dl.zkxy3y
title: Occurrence Download

description: A dataset containing 77093 records in GBIF matching the query:
 SELECT
 GBIF_EEARGCode(10000, decimalLatitude, decimalLongitude, 0) AS cellcode,
 COUNT(*)
 FROM occurrence
 WHERE occurrenceStatus = 'PRESENT'
 AND countryCode = 'AT' OR countryCode = 'BE' OR countryCode = 'BG' OR countryCode = 'HR' OR countryCode = 'CY' OR countryCode = 'CZ' OR countryCode = 'DK' OR countryCode = 'EE' OR countryCode = 'FI' OR countryCode = 'FR' OR countryCode = 'DE' OR countryCode = 'GR' OR countryCode = 'HU' OR countryCode = 'IE' OR countryCode = 'IT' OR countryCode = 'LV' OR countryCode = 'LT' OR countryCode = 'LU' OR countryCode = 'MT' OR countryCode = 'NL' OR countryCode = 'PL' OR countryCode = 'PT' OR countryCode = 'RO' OR countryCode = 'SK' OR countryCode = 'SI' OR countryCode = 'ES' OR countryCode = 'SE' OR countryCode = 'UK'
 AND taxonKey = 731
 AND hasCoordinate = TRUE
 AND (coordinateUncertaintyInMeters &lt;= 10000 OR coordinateUncertaintyInMeters IS NULL)
 AND speciesKey IS NOT NULL
 AND "year" &gt;= 2007
 AND "year" &lt;= 2018
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND basisofrecord IN ('HUMAN_OBSERVATION','MACHINE_OBSERVATION','OBSERVATION')
 GROUP BY cellcode

 The dataset's 77093 records were derived from 36407 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041921-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15526127
title: A Rapid, Green and Cost-Effective Synthesis of pH- and Hydroxyl Group Sensitive Carbon Dots for Sensing Applications
DOI: 10.5281/zenodo.15526128
title: A Rapid, Green and Cost-Effective Synthesis of pH- and Hydroxyl Group Sensitive Carbon Dots for Sensing Applications
DOI: 10.5281/zenodo.13269839
title: Forest roads (Congo Basin)

description: Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 6 years of road development (57,363 km) from 2019-2024. 

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:



NetworkID: A unique ID for each connected road network.

SegLenM: The length of the road segment (in meters).

NetLenM: The length of the connected road network (in meters).

Month: The road segment opening month.

Year: The road segment opening year.

MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.


 

Additional information



More information about the forest road mapping project can be found at: https://wur.eu/forest-roads

Continuously updated road maps can be interactively viewed  at: https://nrtwur.users.earthengine.app/view/forest-roads

The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2024-12')

The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380


 

Citation

Please cite the following when referring to this dataset:



Slagter B., Fesenmyer K., Hethcoat M., Belair E., Ellis P., Kleinschroth F., Peña-Claros M., Herold M., Reiche J. (2024). Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning. Remote Sensing of Environment.
DOI: 10.5281/zenodo.13269840
title: Forest roads (Congo Basin)

description: Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 4 years of road development (35,944 km) from 2019-2022, as described in Slagter et al. (2024). 

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:



NetworkID: A unique ID for each connected road network.

SegLenM: The length of the road segment (in meters).

NetLenM: The length of the connected road network (in meters).

Month: The road segment opening month.

Year: The road segment opening year.

MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.


 

Additional information



More information about the forest road mapping project can be found at: https://wur.eu/forest-roads

Continuously updated road maps can be interactively viewed  at: https://nrtwur.users.earthengine.app/view/forest-roads

The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2022-12')

The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380


 

Citation

Please cite the following when referring to this dataset:



Slagter B., Fesenmyer K., Hethcoat M., Belair E., Ellis P., Kleinschroth F., Peña-Claros M., Herold M., Reiche J. (2024). Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning. Remote Sensing of Environment
DOI: 10.5281/zenodo.13739812
title: Forest roads (Congo Basin)

description: Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 5 years of road development (46,311 km) from 2019-2023. 

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:



NetworkID: A unique ID for each connected road network.

SegLenM: The length of the road segment (in meters).

NetLenM: The length of the connected road network (in meters).

Month: The road segment opening month.

Year: The road segment opening year.

MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.


 

Additional information



More information about the forest road mapping project can be found at: https://wur.eu/forest-roads

Continuously updated road maps can be interactively viewed  at: https://nrtwur.users.earthengine.app/view/forest-roads

The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12')

The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380


 

Citation

Please cite the following when referring to this dataset:



Slagter B., Fesenmyer K., Hethcoat M., Belair E., Ellis P., Kleinschroth F., Peña-Claros M., Herold M., Reiche J. (2024). Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning. Remote Sensing of Environment
DOI: 10.5281/zenodo.15563307
title: Forest roads (Congo Basin)

description: Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 6 years of road development (57,363 km) from 2019-2024. 

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:



NetworkID: A unique ID for each connected road network.

SegLenM: The length of the road segment (in meters).

NetLenM: The length of the connected road network (in meters).

Month: The road segment opening month.

Year: The road segment opening year.

MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.


 

Additional information



More information about the forest road mapping project can be found at: https://wur.eu/forest-roads

Continuously updated road maps can be interactively viewed  at: https://nrtwur.users.earthengine.app/view/forest-roads

The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2024-12')

The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380


 

Citation

Please cite the following when referring to this dataset:



Slagter B., Fesenmyer K., Hethcoat M., Belair E., Ellis P., Kleinschroth F., Peña-Claros M., Herold M., Reiche J. (2024). Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning. Remote Sensing of Environment.
DOI: 10.22002/0yf95-v3327
title: unfixed Listeria

description: Raw data files of unfixed Listeria
description: <strong>Tilt Series Date:</strong> 2008-09-08</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Listeria monocytogenes</p>
<strong>Strain:</strong> 10403S</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (0.0°, 0.0°), step: 2°, constant angular increment, dosage: 0.0 eV/Å²,  defocus: -10.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> UCSF Tomo</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> IMOD</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2008-09-08-4/rawdata/</p>Listeria_34k_4.st,  Tilt Series (Pixel Size 0.644 nm), 520.1 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2008-09-08-4/rawdata/Listeria_34k_4.st"
        > <i class="download icon"></i> Download </a></p>

        Listeria_34k_4.rec,  Reconstruction (Pixel Size 1.288 nm), 311.1 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2008-09-08-4/3dimage_3608/Listeria_34k_4.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/d2084-b1j21
title: Caulobacter crescentus

description: Raw data files of Caulobacter crescentus
description: <strong>Tilt Series Date:</strong> 2005-04-14</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Caulobacter crescentus</p>
<strong>Strain:</strong> NA1000</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 0.5°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -12.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> UCSF Tomo</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> IMOD</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2005-04-14-2/rawdata/</p>Cc05apr14e200t01.mrc,  Tilt Series (Pixel Size 1.2 nm), 2.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2005-04-14-2/rawdata/Cc05apr14e200t01.mrc"
        > <i class="download icon"></i> Download </a></p>

        Cc14apr1bin.rec,  Reconstruction (Pixel Size 2.4 nm), 99.8 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2005-04-14-2/3dimage_721/Cc14apr1bin.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/704x2-qh983
title: Caulobacter, McpA

description: labeled McpA receptor .
description: <strong>Tilt Series Date:</strong> 2007-10-10</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Caulobacter crescentus</p>
<strong>Strain:</strong> NA1000</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -12.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> UCSF Tomo</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> IMOD</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2007-10-10-11/rawdata/</p>CCMcpA_1010_13.st,  Tilt Series (Pixel Size 0.961 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2007-10-10-11/rawdata/CCMcpA_1010_13.st"
        > <i class="download icon"></i> Download </a></p>

        CCMcpA_1010_13_full.rec,  Reconstruction (Pixel Size 1.922 nm), 704.6 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2007-10-10-11/3dimage_1164/CCMcpA_1010_13_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/xm3s5-t2q66
title: Flavobacterium johnsoniae

description: Flavobacterium johnsoniae
description: <strong>Tilt Series Date:</strong> 2007-06-01</p>
<strong>Data Taken By:</strong> Gregory Henderson</p>
<strong>Species / Specimen:</strong> Flavobacterium johnsoniae</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (0.0°, 0.0°), step: °, constant angular increment, dosage: 0.0 eV/Å²,  defocus: 0.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> </p>
<strong>Upload Method:</strong> rundir</p>
<strong>Processing Software Used:</strong> </p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ghp2007-06-01-3/rawdata/</p>FJ.st,  Tilt Series (Pixel Size 0.0 nm), 1.1 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ghp2007-06-01-3/rawdata/FJ.st"
        > <i class="download icon"></i> Download </a></p>

        FJ_full.rec,  Reconstruction (Pixel Size 0.0 nm), 104.9 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ghp2007-06-01-3/3dimage_94556/FJ_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/zqbsh-a1669
title: Caulobacter crescentus

description: Raw data files of Caulobacter crescentus
description: <strong>Tilt Series Date:</strong> 2008-01-20</p>
<strong>Data Taken By:</strong> Zhuo Li</p>
<strong>Species / Specimen:</strong> Caulobacter crescentus</p>
<strong>Strain:</strong> NA1000</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.5°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -12.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> UCSF Tomo</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> </p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/zla2008-01-20-13/rawdata/</p>mrebgfpbx290x6601dot57255cts12u008.mrc,  Tilt Series (Pixel Size 1.259 nm), 688.0 MB  
        
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        mrebgfpbx290x6601dot57255cts12u0081k.rec,  Reconstruction (Pixel Size 2.518 nm), 66.0 MB  
        
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