cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/ca2x-2c22
title: Parkes observations for project P1193 semester 2023APRS_02

description: In a search for steep spectrum sources in the combined data from the Rapid ASKAP Continuum Survey Low-band (RACS-low at 888 MHz) and high-band (RACS-high at 1667 MHz), we have found 14 compact objects with an ultra steep radio spectrum (\alpha &lt; -1.6) and matched with a Fermi unassociated source. Sources with ultra steep radio spectra and gamma-ray emission are likely to be previously unidentified pulsars. We propose to observe these targets with Murriyang (Parkes) UWL receiver. The deep targeted pulsar search will help us confirm new pulsars in extreme systems or environments, such as pulsars in compact binaries, and highly scattered pulsars.
DOI: 10.22002/26ac7-yx005
title: #92+E.coli bamA purified

description: Raw data files of #92+E.coli bamA purified
description: <strong>Tilt Series Date:</strong> 2015-04-29</p>
<strong>Data Taken By:</strong> Poorna Subramanian</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-66.0°, 66.0°), step: 2.0°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -10.0 μm,  magnification: 33000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Chris Hayes and David Low UCSB</p>
<strong>Sample Preparation:</strong> These are E.cloacae mini cells (#92) mixed with purified E.coli BamA protein receptor. Both cells and BamA were blocked with BSA- 100g/ml for 30 minutes before mixing for 20 minutes.</p>
description: Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-04-29-15</p>PS96A-tomo0019.mrc,  Tilt Series (Pixel Size 0.33 nm), 1.8 GB  
        
<a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-04-29-15/rawdata/PS96A-tomo0019.mrc"
        > <i class="download icon"></i> Download </a></p>

        PS96A-tomo0019_full.rec,  Reconstruction (Pixel Size 1.32 nm), 430.6 MB  
        
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DOI: 10.22002/7a0wg-n6j32
title: JV6781_ΔdotLOB

description: ΔdotLOB (Deletion of Dot/Icm ATPases dotL, dotO, dotB)
description: <strong>Tilt Series Date:</strong> 2017-07-17</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -7.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Joseph P. Vogel, Washington University School of Medicine. Data collected and processed by Debnath Ghosal at F30, Caltech.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown in AYE liquid culture with following supplements: ferric nitrate, cysteine hydrochloride and thymidine. Scrape cells off a plate, dissolve in the media, adjust the cell density to 0.6 and make a dilution series. Start O/N culture with all of them and freeze cells next day from the one in early stationary phase that is OD ~ 3.0. This time cell density was ~2.6</p>
<strong>Sample Preparation:</strong> 6 grids were frozen for this strain at OD~ 2.6, cell density was increased to 2x by centrifugation. Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 4 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.</p>
description: Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-07-17-62</p>20170718_JV67810006.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.7 GB  
        
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        > <i class="download icon"></i> Download </a></p>

        20170718_JV67810006_full.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-07-17-62/3dimage_87002/20170718_JV67810006_full.rec"
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DOI: 10.22002/n4r56-zg714
title: Agro_AD1526_GFP-VirB11_20150826

description: Agro_AD1526_GFP-VirB11 (Cb+Kan+AS plates, Mes pH5.8). Add 200 ul of induction media to one of the induction plate (containing lawn of Agrobacterium AD1526) and wash the plate by gently rolling the liquid. Take this liquid and add to the other plate. Repeat the same. This should get enough cells dissolved in the liquid.Add gold to this: 1ul of gold to 4 ul of cells.
description: <strong>Tilt Series Date:</strong> 2015-08-26</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Agrobacterium tumefaciens</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2.0°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -8.0 μm,  magnification: 26000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain constructed by Anath Das. Data collected and processed by Debnath Ghosal using Caltech tomography pipeline. This data was collected at the Janelia Krios.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Grow AD1526 cells on AB solid medium (pH 7) at 30C. For induction, use one single colony and spread on induction plates (AB Mes pH 5.8) containing  Cb (50ug/ml)+Kan (50ug/ml)+AS (200µM). Grow at 19C for 48 hr. Add 200 ul of induction media to one of the induction plate (containing lawn of Agrobacterium AD1526) and wash the plate by gently rolling the liquid. Add gold to this: 1ul of gold to 4 ul of cells, freeze.</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 3 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200.</p>
description: Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2015-08-26-62</p>AD1526tomo0081_part61_2.rec,  Reconstruction (Pixel Size 1.68 nm), 430.6 MB  
        
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        AD1526tomo0081.mrc,  Tilt Series (Pixel Size 0.42 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2015-08-26-62/rawdata/AD1526tomo0081.mrc"
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DOI: 10.22002/yzhbc-tr688
title: Escherichia coli

description: Raw data files of Escherichia coli
description: <strong>Tilt Series Date:</strong> 2015-03-21</p>
<strong>Data Taken By:</strong> Matt Swulius</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> MG1655 sacculi</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -8.0 μm,  magnification: 22500x. </p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
description: Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/mst2015-03-21-3</p>032115_purified_actomyosin0003.mrc,  Tilt Series (Pixel Size 0.48 nm), 3.4 GB  
        
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        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/yrk0v-e5446
title: WT LP01 from Liu lab, Yale

description: Type four secretion system, T4SS
description: <strong>Tilt Series Date:</strong> 2016-11-28</p>
<strong>Data Taken By:</strong> Yuxi Liu</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-51.0°, 51.0°), step: 3°, constant angular increment, dosage: 50.0 eV/Å²,  defocus: -5.0 μm,  magnification: 15500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> None</p>
<strong>Collaborators and Roles:</strong> Data collected by Bo Hu in Jun Liu lab at Yale from their Polara. Partial WT strain data set. Primary literature:  doi: 10.1038/s41564-018-0165-z</p>
description: Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/ylx2016-11-28-224</p>T4SSWTDec12A069_driftcorr.mrc,  Tilt Series (Pixel Size 0.25 nm), 2.0 GB  
        
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        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/7msgb-4c885
title: JV3579_Polara_Mag27.5

description: JV3579 Legionella thyA ΔdotF
description: <strong>Tilt Series Date:</strong> 2017-01-15</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.50°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Joseph P. Vogel, Washington University School of Medicine. Data collected and processed by Debnath Ghosal at F30, Caltech.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown in AYE liquid culture with following supplements: ferric nitrate, cysteine hydrochloride and thymidine. Scrape cells off a plate, dissolve in the media, adjust the cell density to 0.6 and make a dilution series. Start O/N culture with all of them and freeze cells next day from the one in early stationary phase that is OD ~ 3.0. This time cell density was ~3.1</p>
<strong>Sample Preparation:</strong> 8 grids were frozen for the JV3579 strain at OD~ 3.1, 4 grids at density x and another 4 at density 2x. Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 3 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.</p>
description: Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-01-15-101</p>jv3579_area5a0018_part81_4.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
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        > <i class="download icon"></i> Download </a></p>

        jv3579_area5a0018.mrc,  Tilt Series (Pixel Size 0.39 nm), 2.3 GB  
        
<a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-01-15-101/rawdata/jv3579_area5a0018.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.57760/sciencedb.cryosphere.08183
title: snow water equavilent maps of Heihe basin during 2000-2015
title: 2000-2015年黑河流域上游雪水当量专题数据

description: This dataset contains daily snow water equivalent data from January 2000 to December 2015 in the upper reaches of the Heihe River Basin, saved in tiff format and fully open for sharing. The data is calibrated using the distributed cold region hydrological model GBEHM and remote sensing parameter calibration method, utilizing long time series high spatial resolution snow cover remote sensing data to provide the calibration objective function and driving data for the model; Using the snow attribute data obtained from ground surveys, more accurate parameter information was provided for the simulation system, and daily snow water equivalent data from 2000 to 2015 were estimated. File name: swe_ Yyyymm. tiff, field description: swe refers to snow water equivalent, yyyy is the year, for example, 2010; Mm is the month, for example, March is 03
description: This dataset contains daily snow water equivalent data from January 2000 to December 2015 in the upper reaches of the Heihe River Basin, saved in tiff format and fully open for sharing. The data is calibrated using the distributed cold region hydrological model GBEHM and remote sensing parameter calibration method, utilizing long time series high spatial resolution snow cover remote sensing data to provide the calibration objective function and driving data for the model; Using the snow attribute data obtained from ground surveys, more accurate parameter information was provided for the simulation system, and daily snow water equivalent data from 2000 to 2015 were estimated. File name: swe_ Yyyymm. tiff, field description: swe refers to snow water equivalent, yyyy is the year, for example, 2010; Mm is the month, for example, March is 03
DOI: 10.57760/sciencedb.cryosphere.00003
title: Remote sensing monitoring data set of snow days in Qinghai Province from 2002 to 2021
title: 2002-2021年青海省积雪日数遥感监测数据集

description: The dynamic changes of snow cover in the Qinghai Tibet Plateau have significant impacts on regional energy balance, hydrology, climate, and ecological environment. Due to the seasonal snow cover in the Qinghai Tibet Plateau showing a generally shallow and patchy distribution of snow layers, the number of snow cover days will better reflect the spatiotemporal distribution characteristics. This data set is based on MODIS-NDSI data with a spatial resolution of 500m. Through the steps of daily snow coverage extraction, snow days synthesis, and site data correction, the remote sensing monitoring data set of snow days in Qinghai Province (2002-2021) was produced. The data has a high accuracy after the simultaneous comparison of the observation data of 51 surface meteorological stations in Qinghai Province. This dataset will provide data support for research work such as snow cover dynamic change monitoring, snow disaster assessment, and climate change. The dataset includes a total of 1 folder, including 24 snow cover days data in the tif format
description: The dynamic changes of snow cover in the Qinghai Tibet Plateau have significant impacts on regional energy balance, hydrology, climate, and ecological environment. Due to the seasonal snow cover in the Qinghai Tibet Plateau showing a generally shallow and patchy distribution of snow layers, the number of snow cover days will better reflect the spatiotemporal distribution characteristics. This data set is based on MODIS-NDSI data with a spatial resolution of 500m. Through the steps of daily snow coverage extraction, snow days synthesis, and site data correction, the remote sensing monitoring data set of snow days in Qinghai Province (2002-2021) was produced. The data has a high accuracy after the simultaneous comparison of the observation data of 51 surface meteorological stations in Qinghai Province. This dataset will provide data support for research work such as snow cover dynamic change monitoring, snow disaster assessment, and climate change. The dataset includes a total of 1 folder, including 24 snow cover days data in the tif format
DOI: 10.5281/zenodo.4674493
title: Raw Data for the article: On the severity of aortic stenosis in ascending aortic aneurysm: A computational tool to examine ventricular-arterial interaction and aortic wall stress

description: An ascending thoracic aortic aneurysm (ATAA) is a life-threatening cardiovascular consequence of vessel dilatation that portends adverse events and death. From a clinical perspective, ATAA should not be treated as an isolated disease, and surgery is often carried out in the presence of AS, aortic insufficiency or a calcified valve leaflet. Aortic stenosis (AS) is common in ATAAs and leads to both vessel rigidity and left ventricular (LV) impairment. In this study, lumped-parameter modeling and computational analysis were used to assess the change in the wall shear stress (WSS) and intramural wall stress of patient-specific ATAA models with different degrees of AS (i.e., mild to severe). The ATAAs of four patients were reconstructed from imaging data and AS was simulated virtually using the lumped-based CircAdapt tool using clinical and echocardiographic data. Results show that LV work derived from pressure-volume loops increased with the severity of AS. Post-stenotic hemodynamic and structural variables markedly increased with AS severity, with WSS showing a 10-fold increase for the most severe AS model as compared to the baseline model with a well-functioning aortic valve. Most importantly, the increase in WSS and aortic wall stress was associated with pronounced values of valvulo-arterial impedance as an indicator of LV dysfunction. This study provides novel insights into progression of AS in patients with ATAAs at high risk of adverse events, and the potential value of valvulo-arterial impedance to predict changes in hemodynamic and structural parameters with the severity of AS. © 2020
DOI: 10.5281/zenodo.4593544
title: Age-related effects on the neural processing of semantic complexity in a continuous narrative: Modulation by gestures already present in young to middle-aged adults.

description: Analyses of the publication: Cuevas, P., He, Y., Billino, J., Kozasa, E., & Straube, B. (2021). Age-related effects on the neural processing of semantic complexity in a continuous narrative: Modulation by gestures already present in young to middle-aged adults. <em>Neuropsychologia</em>, <em>151</em>(107725). https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2020.107725 <strong>Abstract:</strong> The processing of semantically complex speech is a demanding task which can be facilitated by speech-associated arm and hand gestures. However, the role of age concerning the perception of semantic complexity and the influence of gestures in this context remains unclear. The goal of this study was to investigate if age-related differences are already present in early adulthood during the processing of semantic complexity and gestures. To this end, we analyzed fMRI images of a sample of 38 young and middle-aged participants (age-range: 19–55). They had the task to listen and to watch a narrative. The narrative contained segments varying in the degree of semantic complexity, and they were spontaneously accompanied by gestures. The semantic complexity of the story was measured by the idea density. Consistent with previous findings in young adults, we observed increased activation for passages with lower compared to higher complexity in bilateral temporal areas and the precuneus. BOLD signal in the left frontal and left parietal regions correlated during the perception of complex passages with increasing age. This correlation was reduced for passages presented with gestures. Median-split based post-hoc comparisons confirmed that group differences between younger (19–23 years) and older adults within the early adult lifespan (24–55 years) were significantly reduced in passages with gestures. Our results suggest that older adults within early adulthood adapt to the requirements of highly complex passages activating additional regions when no gesture information is available. Gestures might play a facilitative role with increasing age, especially when speech is complex.<br>
description: {"references": ["Cuevas, P., He, Y., Billino, J., Kozasa, E., & Straube, B. (2021). Age-related effects on the neural processing of semantic complexity in a continuous narrative: Modulation by gestures already present in young to middle-aged adults. Neuropsychologia, 151(107725). https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2020.107725", "Cuevas, P., Steines, M., He, Y., Nagels, A., Culham, J., & Straube, B. (2019). The facilitative effect of gestures on the neural processing of semantic complexity in a continuous narrative. NeuroImage, 195, 38\u201347. https://doi.org/10.1016/J.NEUROIMAGE.2019.03.054"]}
DOI: 10.5281/zenodo.4674580
title: Raw Data for the article: Statistical Shape Analysis of Ascending Thoracic Aortic Aneurysm: Correlation between Shape and Biomechanical Descriptors

description: An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of <em>n</em> = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allowed to reduce the complex ATAA anatomy to a few shape modes, which were correlated to shear stress and aortic strain, as determined by computational analysis. Findings demonstrated that shape modes are associated to specific morphological features of aneurysmal aorta as the vessel tortuosity and local bulging of the ATAA. A predictive model, built with principal shape modes of the ATAA wall, achieved better performance in stratifying surgically operated ATAAs versus monitored ATAAs, with respect to a baseline model using the maximum aortic diameter. Using current imaging resources, this study demonstrated the potential of SSA to investigate the association between shape and function in ATAAs, with the goal of developing a personalized approach for the treatment of the severity of aneurysmal aorta.