cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25573/data.14773326
title: Hellbender skin transcriptome

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14773326.v1
title: Hellbender skin transcriptome

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14773293
title: Hellbender skin Bd transcriptome metadata

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14773293.v1
title: Hellbender skin Bd transcriptome metadata

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14773269
title: Hellbender skin Bd transcriptome BLAST nr annotations

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14773269.v1
title: Hellbender skin Bd transcriptome BLAST nr annotations

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14773260
title: Hellbender skin Bd transcriptome BLAST xml

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14773260.v1
title: Hellbender skin Bd transcriptome BLAST xml

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14785968
title: Hellbender skin transcriptome isoform counts matrix

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14785968.v1
title: Hellbender skin transcriptome isoform counts matrix

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14786088
title: Hellbender skin transcriptome BLAST nr annotations

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14786088.v1
title: Hellbender skin transcriptome BLAST nr annotations

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14786094
title: Hellbender skin transcriptome metadata

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14786094.v1
title: Hellbender skin transcriptome metadata

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14750622
title: Hellbender skin microbiome 16S OTU table

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14750622.v1
title: Hellbender skin microbiome 16S OTU table

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14751009
title: Hellbender skin prokaryote transcriptome

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.25573/data.14751009.v1
title: Hellbender skin prokaryote transcriptome

description: In this study, we explored the efficacy of Bd vaccination on hellbender salamanders and functionally characterized host, host-associated microbiome, and pathogen throughout immunization and infection. We compared the function of hellbender skin, the skin microbiome, and Bd with a metatranscriptome approach at four experimental time points, (i) pre-vaccination and pre-infection, (ii) post-vaccination and pre-infection, (iii) early infection, and (iv) late infection.
DOI: 10.5281/zenodo.7570583
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain 8-class segmentation of RGB 768x768 NAIP images</strong></em> These Residual-UNet model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels classes: water<br> whitewater<br> sediment<br> other_bare_natural_terrain<br> marsh_vegetation<br> terrestrial_vegetation<br> agricultural<br> development File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. et al. A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. Sci Data 10, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.5281/zenodo.7570582
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain 8-class segmentation of RGB 768x768 NAIP images</strong></em> These Residual-UNet model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels classes: water<br> whitewater<br> sediment<br> other_bare_natural_terrain<br> marsh_vegetation<br> terrestrial_vegetation<br> agricultural<br> development File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. et al. A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. Sci Data 10, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.5281/zenodo.7132869
title: Measurement report: Radiative efficiencies of (CF3)2CFC, CF3OCFCF2, and CF3OCF2CF3

description: Absorption cross-sections of emerging greenhouse gases (GHG) were measured to estimate the radiative efficiency using high-resolution Fourier transform infrared spectroscopy (HR-FTIR). For quantitative spectroscopy, the Beer–Lambert parameters of absorber pressure, temperature, and optical path length (OPL) were accurately determined to be traceable to the primary standards. The OPL of the multipass cell mounted on the HR-FTIR spectrometer was spectroscopically calibrated. A ratio of the averaged N<sub>2</sub>O absorptions was found to be in the range of 2217.4–2219.0 cm<sup>-1</sup>, with a spectral resolution of 0.026 cm<sup>-1</sup>, yielding a ratio of OPLs that falls between the multipass cell and reference cell. This cell-to-cell comparison method is free from the uncertainty in the referring line strength, which reduced the calibration uncertainty compared with the direct line-strength referring method. With the OPL-calibrated multipass cell (3.169 ± 0.079 m), the absorption cross-sections were measured at low absorber pressures with a spectral resolution of 2 cm<sup>-1</sup>, integrated at 10 cm<sup>-1</sup> intervals, and multiplied by the new narrow band model to yield the radiative efficiencies. The radiative efficiency values of CF<sub>4</sub>, SF<sub>6</sub>, and NF<sub>3</sub> were evaluated to be 0.085 ± 0.002, 0.573 ± 0.016, and 0.195 ± 0.008 W m<sup>-2</sup> ppb<sup>-1</sup>, respectively, which are consistent with previously reported values. For the emerging GHGs, the radiative efficiency values were determined to be 0.201 ± 0.008 Wm<sup>-2</sup>ppb<sup>-1</sup> for heptafluoroisobutyronitrile (CF<sub>3</sub>)<sub>2</sub>CFCN; commercially referred to as <em>Novec-4710</em>), 0.328 ± 0.013 Wm<sup>-2</sup>ppb<sup>-1</sup> for perfluoro methyl vinyl ether (CF<sub>3</sub>OCFCF<sub>2</sub>; PMVE), and 0.544 ± 0.022 Wm<sup>-2</sup>ppb<sup>-1</sup> for 1,1,1,2,2-pentafluoro-2-(trifluoromethoxy)ethane (CF<sub>3</sub>OCF<sub>2</sub>CF<sub>3</sub>; PFMEE).
DOI: 10.18112/openneuro.ds004457.v1.0.0
title: Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex
DOI: 10.57745/rapwcd
title: stations_shapefile_LambertII.shp

description: Infos stations
DOI: 10.57745/bmgz2w
title: stations_shapefile_LambertII.prj

description: Infos stations
DOI: 10.57745/wn17z8
title: stations_shapefile_LambertII.dbf

description: Infos stations
DOI: 10.57745/wqjeuy
title: stations_shapefile_LambertII.shx

description: Infos stations
DOI: 10.57745/7piqfz
title: HydRE_V1.nc

description: HydRE
DOI: 10.5281/zenodo.7148592
title: Dataset for "Tunnelling measured in a very slow ion-molecule reaction"

description: Dataset for "Tunnelling measured in a very slow ion-molecule reaction"
DOI: 10.5281/zenodo.7148591
title: Dataset for "Tunnelling measured in a very slow ion-molecule reaction"

description: Dataset for "Tunnelling measured in a very slow ion-molecule reaction"
DOI: 10.6084/m9.figshare.21989717
title: Comau.vOTUs.fasta

description: vOTUs dataset from the Comau Fjord, Chile.
DOI: 10.6084/m9.figshare.21989717.v1
title: Comau.vOTUs.fasta

description: vOTUs dataset from the Comau Fjord, Chile.
DOI: 10.6084/m9.figshare.21732308
title: Supplementary Material for: Efficacy and Safety of Sevelamer Carbonate in Chinese Nondialysis Chronic Kidney Disease Patients with Hyperphosphatemia: A Randomized, Double-Blind, Parallel-Group Study

description: Introduction: Previous studies suggested that sevelamer carbonate is well tolerated with a favorable efficacy and safety profile in both dialysis and nondialysis patients in Europe; however, the efficacy remains controversial, and few studies have examined sevelamer carbonate therapy in other ethnic nondialysis CKD patients. This study assessed the efficacy and safety of sevelamer carbonate in Chinese nondialysis CKD patients with hyperphosphatemia. Methods: The multicenter, randomized, double-blind, parallel group, placebo-controlled, and phase 3 clinical trial enrolled 202 Chinese nondialysis CKD patients with serum phosphorus ≥1.78 mmol/L. Patients were randomly assigned 1:1 to receive sevelamer carbonate (2.4-12 g per day) or placebo for 8 weeks. The primary outcome was the change in serum phosphorous between baseline and week 8. Results: Totally 482 Chinese patients were screened and 202 were randomized (sevelamer carbonate, n=101; placebo, n=101). The mean serum phosphorous decreased significantly in patients treated with sevelamer carbonate compared with placebo (-0.22±0.47 vs. 0.05±0.44 mmol/L, p&lt;0.0001). Significantly (p&lt;0.0001) decreases of serum total cholesterol, low-density lipoprotein cholesterol and calcium-phosphorus (Ca × P) product levels from baseline to week 8 were shown in sevelamer carbonate group compared with placebo group. Serum intact parathyroid hormone (iPTH) was not significantly changed in the sevelamer carbonate group (p=0.83). Patients in the sevelamer carbonate group experienced similar adverse events as the placebo group. Conclusion: Sevelamer carbonate is an effective and well-tolerated phosphate binder in advanced nondialysis CKD Chinese patients with hyperphosphatemia.
DOI: 10.5281/zenodo.7594864
title: MetaTransformer embedding tables

description: DNA embedding tables for MetaTransformer with sizes ranging form 7 to 14 k-mer.
DOI: 10.5281/zenodo.7594863
title: MetaTransformer embedding tables

description: DNA embedding tables for MetaTransformer with sizes ranging form 7 to 14 k-mer.
DOI: 10.6084/m9.figshare.21702146
title: Supplementary Material for: Anti-inflammatory action of antidepressants: Investigating the longitudinal effect of antidepressants on white blood cell count

description: Introduction: Antidepressants have documented anti-inflammatory effects on pro-inflammatory biomarkers. However, the long-term effects of antidepressants on inflammatory markers and effects of different antidepressant classes on pro-inflammatory biomarkers is largely unexplored. Here, we evaluate the short and long-term effects of all antidepressant classes on a clinical immune marker, white blood cell count (WBC). Methods: Using a retrospective study design, we extracted WBC count and prescription medications from electronic health records at Vanderbilt University Medical Center. We created a longitudinal model to evaluate the short-term and long-term effects of these medications on WBC count. We validated our longitudinal model using two known anti-inflammatory medications, biologic immunosuppressants and chemotherapy, and one medication class without known immunomodulatory properties, contraceptives. We used the longitudinal model to determine the effects of antidepressant use on WBC count stratified by drug class. Results: Biologic immunosuppressant and chemotherapy use associated with decreased WBC count, but contraceptive use did not associate with changes in WBC count, validating our longitudinal modeling approach. All antidepressant classes associated with decreased WBC count in the long-term cohorts. SSRI and Atypical use also associated with decreased WBC count in the short-term cohort. Conclusions: Using electronic health record data, we show all anti-depressant classes exhibit anti-inflammatory effects on a clinical immune marker, WBC count. Additionally, our results indicate that in some cases the anti-inflammatory effects of antidepressants persist over at least a one-year time frame. Our work contributes to the immunomodulatory knowledge of antidepressants and motivates future studies investigating alternative therapeutic routes for antidepressants.