Find research datasets worth reusing

opencc-by-4.0Jun 2023View details →

Genome-wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase

Primary data associated with the figure 1 of the manuscript "Genome wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase" (Herschel S. Dhekne, Francesca Tonelli, Wondwossen M. Yeshaw, Claire Y. Chiang, Charles Limouse, Ebsy Jaimon, Elena Purlyte, Dario Alessi, and Suzanne Pfeffer).  These include  - data (annotated .tiff exports) from Metamorph acquired spinning disk confocal microscope - sequencing data as fastq files .gz files from Hiseq or Miseq next generation sequencing,   - graphs made using GraphPad Prism (.pzf files). - flow cytometry .fcs files and workspace files - <a href="https://zenodo.org/api/files/de4d1da1-785a-4f55-a542-876647a2a5ad/Figure%201-%20file%20names%20legend.xlsx">Figure 1- file names legend.xlsx</a> excel sheet explaining the details

opencc-by-4.0Dec 2021View details →

Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).

This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis. CyTOF data: <ul> <li>The debarcoded fcs files (before batch-correction) can be found in <a href="https://flowrepository.org/id/FR-FCM-Z4P5">https://flowrepository.org/id/FR-FCM-Z4P5</a>. \</li> <li>Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): <ul> <li>data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).</li> <li>data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.</li> <li>phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (<a href="https://github.com/jacoblevine/PhenoGraph">https://github.com/jacoblevine/PhenoGraph</a>) per T cell compartment.</li> <li>clusterannotation.csv: annotation for each cluster and metacluster</li> </ul> </li> </ul> scRNA-seq data: <ul> <li>The raw data can be found in <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450</a></li> <li>Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): <ul> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_Sawitzki_RECAST_09_2021.xlsx">scRNAseq_Sawitzki_RECAST_09_2021.xlsx</a>: Single-cell metadata.</li> <li>scRNAseq_samples.tsv: Samples metadata.</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_genelist_annotation.xlsx">scRNAseq_genelist_annotation.xlsx</a>: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">G</a>ene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt">scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt</a>, <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt">scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt</a>, , <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt">scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt</a>: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_traj18_trav10.txt">scRNAseq_traj18_trav10.txt</a>,<a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_trbv25.txt">scRNAseq_trbv25.txt</a>: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.</li> </ul> </li> </ul>

opencc-by-4.0Mar 2023View details →

EOAD (Egocentric Outdoor Activity Dataset)

EOAD is a collection of videos captured by wearable cameras, mostly of sports activities. It contains both visual and audio modalities. It was initiated by the <a href="https://www.vision.huji.ac.il/egoseg/videos/dataset.html">HUJI</a> and <a href="https://github.com/azuxmioy/fpvsum">FPVSum</a> egocentric activity datasets. However, the number of samples and diversity of activities for HUJI and FPVSum were insufficient. Therefore, we combined these datasets and populated them with new YouTube videos. The selection of videos was based on the following criteria: <ul> <li>The videos should not include text overlays.</li> <li>The videos should contain natural sound (no external music)</li> <li>The actions in videos should be continuous (no cutting the scene or jumping in time)</li> </ul> Video samples were trimmed depending on scene changes for long videos (such as driving, scuba diving, and cycling). As a result, a video may have several clips depicting egocentric actions. Hence, video clips were extracted from carefully defined time intervals within videos. The final dataset includes video clips with a single action and natural audio information. Statistics for EOAD: <ul> <li>30 activities</li> <li>303 distinct videos</li> <li>1392 video clips</li> <li>2243 minutes labeled videos clips</li> </ul> The detailed statistics for the selected datasets and the crawled videos clips from YouTube are given below: <ul> <li><a href="https://www.vision.huji.ac.il/egoseg/videos/dataset.html">HUJI</a>: 49 distinct videos - 148 video clips for 9 activities (driving, biking, motorcycle, walking, boxing, horse riding, running, skiing, stair climbing)</li> <li><a href="https://github.com/azuxmioy/fpvsum">FPVSum</a>: 39 distinct videos - 124 video segments for 8 activities (biking, horse riding, skiing, longboarding, rock climbing, scuba, skateboarding, surfing)</li> <li>YouTube: 216 distinct videos - 1120 video clips for 27 activities (american football, basketball, bungee jumping, driving, go-kart, horse riding, ice hockey, jet ski, kayaking, kitesurfing, longboarding, motorcycle, paintball, paragliding, rafting, rock climbing, rowing, running, sailing, scuba diving, skateboarding, soccer, stair climbing, surfing, tennis, volleyball, walking)</li> </ul> The video clips used for training, validation and test sets for each activity are listed in Table 1. Multiple video clips may belong to a single video because of trimming it for some reasons (i.e., scene cut, temporary overlayed text on videos, or video parts unrelated to activities). While splitting the dataset, the minimum number of videos for each activity was selected as 8. Additionally, the video samples were divided as 50%, 25%, and 25% for training (minimum four videos), validation (minimum two videos), and testing (minimum two videos), respectively. On the other hand, videos were split according to the raw video footage to prevent the mixing of similar video clips (having the same actors and scenes) into training, validation, and test sets. Therefore, we ensured that the video clips trimmed from the same videos were split together into training, validation, or test sets to satisfy a fair comparison. Some activities have continuity throughout the video, such as scuba, longboarding, or riding horse, which also have an equal number of video segments with the number of videos. However, some activities, such as skating, occurred in a short time, making the number of video segments higher than the others. As a result, the number of video clips for training, validation, and test sets was highly imbalanced for the selected activities (i.e., jet ski and rafting have 4; however, soccer has 99 video clips for training).                                                                        Table 1 - Dataset splitting for EOAD <table align="center"> <thead> <tr> <th> </th> <th> </th> <th> Train </th> <th> </th> <th> Validation </th> <th> </th> <th> Test </th> <th> </th> </tr> </thead> <tbody> <tr> <td> </td> <td> Action Label </td> <td> #Clips </td> <td> Total Duration </td> <td> #Clips </td> <td> Total Duration </td> <td> #Clips </td> <td> Total Duration </td> </tr> <tr> <td> </td> <td> AmericanFootball </td> <td> 34 </td> <td> 00:06:09 </td> <td> 36 </td> <td> 00:05:03 </td> <td> 9 </td> <td> 00:01:20 </td> </tr> <tr> <td> </td> <td> Basketball </td> <td> 43 </td> <td> 01:13:22 </td> <td> 19 </td> <td> 00:08:13 </td> <td> 10 </td> <td> 00:28:46 </td> </tr> <tr> <td> </td> <td> Biking </td> <td> 9 </td> <td> 01:58:01 </td> <td> 6 </td> <td> 00:32:22 </td> <td> 11 </td> <td> 00:36:16 </td> </tr> <tr> <td> </td> <td> Boxing </td> <td> 7 </td> <td> 00:24:54 </td> <td> 11 </td> <td> 00:14:14 </td> <td> 5 </td> <td> 00:17:30 </td> </tr> <tr> <td> </td> <td> BungeeJumping </td> <td> 7 </td> <td> 00:02:22 </td> <td> 4 </td> <td> 00:01:36 </td> <td> 4 </td> <td> 00:01:31 </td> </tr> <tr> <td> </td> <td> Driving </td> <td> 19 </td> <td> 00:37:23 </td> <td> 9 </td> <td> 00:24:46 </td> <td> 9 </td> <td> 00:29:23 </td> </tr> <tr> <td> </td> <td> GoKart </td> <td> 5 </td> <td> 00:40:00 </td> <td> 3 </td> <td> 00:11:46 </td> <td> 3 </td> <td> 00:19:46 </td> </tr> <tr> <td> </td> <td> Horseback </td> <td> 5 </td> <td> 01:15:14 </td> <td> 5 </td> <td> 01:02:26 </td> <td> 2 </td> <td> 00:20:38 </td> </tr> <tr> <td> </td> <td> IceHockey </td> <td> 52 </td> <td> 00:19:22 </td> <td> 46 </td> <td> 00:20:34 </td> <td> 10 </td> <td> 00:36:59 </td> </tr> <tr> <td> </td> <td> Jetski </td> <td> 4 </td> <td> 00:23:35 </td> <td> 5 </td> <td> 00:18:42 </td> <td> 6 </td> <td> 00:02:43 </td> </tr> <tr> <td> </td> <td> Kayaking </td> <td> 28 </td> <td> 00:43:11 </td> <td> 22 </td> <td> 00:14:23 </td> <td> 4 </td> <td> 00:11:05 </td> </tr> <tr> <td> </td> <td> Kitesurfing </td> <td> 30 </td> <td> 00:21:51 </td> <td> 17 </td> <td> 00:05:38 </td> <td> 6 </td> <td> 00:01:32 </td> </tr> <tr> <td> </td> <td> Longboarding </td> <td> 5 </td> <td> 00:15:40 </td> <td> 4 </td> <td> 00:18:03 </td> <td> 4 </td> <td> 00:09:11 </td> </tr> <tr> <td> </td> <td> Motorcycle </td> <td> 20 </td> <td> 00:49:38 </td> <td> 21 </td> <td> 00:13:53 </td> <td> 8 </td> <td> 00:20:30 </td> </tr> <tr> <td> </td> <td> Paintball </td> <td> 7 </td> <td> 00:33:52 </td> <td> 4 </td> <td> 00:12:08 </td> <td> 4 </td> <td> 00:08:52 </td> </tr> <tr> <td> </td> <td> Paragliding </td> <td> 11 </td> <td> 00:28:42 </td> <td> 4 </td> <td> 00:10:16 </td> <td> 4 </td> <td> 00:19:50 </td> </tr> <tr> <td> </td> <td> Rafting </td> <td> 4 </td> <td> 00:15:41 </td> <td> 3 </td> <td> 00:07:27 </td> <td> 3 </td> <td> 00:06:13 </td> </tr> <tr> <td> </td> <td> RockClimbing </td> <td> 6 </td> <td> 00:49:38 </td> <td> 2 </td> <td> 00:21:59 </td> <td> 2 </td> <td> 00:18:50 </td> </tr> <tr> <td> </td> <td> Rowing </td> <td> 5 </td> <td> 00:47:05 </td> <td> 3 </td> <td> 00:13:21 </td> <td> 3 </td> <td> 00:03:26 </td> </tr> <tr> <td> </td> <td> Running </td> <td> 21 </td> <td> 01:21:56 </td> <td> 19 </td> <td> 00:46:29 </td> <td> 11 </td> <td> 00:42:59 </td> </tr> <tr> <td> </td> <td> Sailing </td> <td> 7 </td> <td> 00:39:30 </td> <td> 4 </td> <td> 00:14:39 </td> <td> 6 </td> <td> 00:15:43 </td> </tr> <tr> <td> </td> <td> Scuba </td> <td> 5 </td> <td> 00:35:02 </td> <td> 3 </td> <td> 00:23:43 </td> <td> 2 </td> <td> 00:18:52 </td> </tr> <tr> <td> </td> <td> Skate </td> <td> 91 </td> <td> 00:15:53 </td> <td> 30 </td> <td> 00:07:01 </td> <td> 10 </td> <td> 00:02:03 </td> </tr> <tr> <td> </td> <td> Ski </td> <td> 14 </td> <td> 01:48:15 </td> <td> 17 </td> <td> 01:01:59 </td> <td> 7 </td> <td> 00:39:15 </td> </tr> <tr> <td> </td> <td> Soccer </td> <td> 102 </td> <td> 00:48:39 </td> <td> 52 </td> <td> 00:13:17 </td> <td> 16 </td> <td> 00:06:54 </td> </tr> <tr> <td> </td> <td> StairClimbing </td> <td> 6 </td> <td> 01:05:32 </td> <td> 6 </td> <td> 00:17:18 </td> <td> 5 </td> <td> 00:20:22 </td> </tr> <tr> <td> </td> <td> Surfing </td> <td> 23 </td> <td> 00:12:51 </td> <td> 17 </td> <td> 00:06:52 </td> <td> 10 </td> <td> 00:07:04 </td> </tr> <tr> <td> </td> <td> Tennis </td> <td> 34 </td> <td> 00:27:04 </td> <td> 9 </td> <td> 00:06:03 </td> <td> 9 </td> <td> 00:03:14 </td> </tr> <tr> <td> </td> <td> Volleyball </td> <td> 87 </td> <td> 00:19:14 </td> <td> 35 </td> <td> 00:07:46 </td> <td> 7 </td> <td> 00:18:58 </td> </tr> <tr> <td> </td> <td> Walking </td> <td> 49 </td> <td> 00:43:02 </td> <td> 36 </td> <td> 00:38:25 </td> <td> 10 </td> <td> 00:10:23 </td> </tr> <tr> <td> Total </td> <td> 30 </td> <td> 740 </td> <td> 20:22:37 </td> <td> 452 </td> <td> 09:20:23 </td> <td> 200 </td> <td> 08:00:08 </td> </tr> </tbody> </table>   EOAD Code Repository Scripts for downloading raw videos and trim them in to video clips are provided in <a href="https://github.com/maliarabaci/eoad">this GitHub</a> repository. Regarding the questions, please contact mali.arabaci@gmail.com.

opencc-by-4.0Mar 2023View details →

Fruit-feeding butterfly community data analysed in "Recovery patterns in community composition of fruit-feeding butterflies following 26 years of active forest restoration"

Community data of fruit-feeding butterflies collected from Kibale National Park, Uganda, in the periods 2011-2012 and 2020-2021 analysed in our paper Korkiatupa et al. 2023: "Recovery patterns in community composition of fruit-feeding butterflies following 26 years of active forest restoration" (Ecosphere 14(5): e4514. <a href="https://doi.org/10.1002/ecs2.4514">https://doi.org/10.1002/ecs2.4514</a>). The table consists of two parts. First part shows counts of individuals of butterfly species in each study site. Second part shows the metadata: code of studysite, census (2011-2012/2020-2021), planting year (planting year or "Primary forest"), and coordinates (WGS 84 coordinate system).

opencc-by-4.0Apr 2023View details →

An integrated approach including docking, MD simulations, and network analysis highlights the action mechanism of the cardiac hERG activator RPR260243

500 ns MD trajectories of the hERG bound states (protein and ligand) used for the analyses discussed in the paper. There are three replica for each system. The trajectories can be visualized using molecular visualization programs such as Pymol or VMD uploading the PDB and the DCD file. The PDB and the topology files of the hERG bound state (protein, membrane, ions and ligand) are also included. An example of input file used for the production step of the dynamics has been provided (production_1.conf).

opencc-by-4.0Apr 2023View details →

Total O3 columns at polar regions: TOMCAT/SLIMCAT passive and active tracers and merged SAOZ-MSR2 dataset

Passive and active total ozone columns simulated by the chemical transport model TOMCAT/SLIMCAT (Chipperfield, 1999) and the merged dataset of total ozone from Système d'Analyse par Observation Zénithale (SAOZ, Pommereau and Goutail, 1988) ground-based instruments and Multi-Sensor Reanalysis (MSR2, van der A et al., 2010, 2015) for the polar stations described in the Table here below. Table. Arctic and Antarctic stations included in the study: station name and ID, latitude, longitude and measurement periods of SAOZ and MSR2 datasets. <table> <tbody> <tr> <td> Station (ID) </td> <td> Lat, Lon </td> <td> SAOZ dataset </td> <td> MSR2 dataset </td> </tr> <tr> <td> Eureka, Nunavut (EU) </td> <td> 80.1°N, 86.4°W </td> <td> 2005-2020 </td> <td> 1990-2022 </td> </tr> <tr> <td> Ny-Alesund, Svalbard (NY) </td> <td> 78.9°N, 11.9° E </td> <td> 1991-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Thule, Greenland (TH) </td> <td> 76.5°N, 68.8°W </td> <td> 1999-2003, 2005-2016 </td> <td> 1990-2022 </td> </tr> <tr> <td> Scoresbysund, Greenland (SC) </td> <td> 70.5°N, 22.0°W </td> <td> 1991-2017, 2019-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Sodankyla, Finland (SK) </td> <td> 67.4°N, 26.6° E </td> <td> 1991-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Sondre Stromfjord, Greenland (SS) </td> <td> 67.0°N, 50.6°W </td> <td> 2018-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Zhigansk, Russia (ZH) </td> <td> 66.8°N, 123.4° E </td> <td> 1992-2013 </td> <td> 1990-2022 </td> </tr> <tr> <td> Salekhard, Russia (SA) </td> <td> 66.5°N, 66.7°E </td> <td> 2002-2016 </td> <td> 1990-2022 </td> </tr> <tr> <td> Marambio, Antarctica (MB) </td> <td> 64.2°S, 56.7°W </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Dumont d’Urville, Antarctica (DD) </td> <td> 66.7°S, 140.0°E </td> <td> 1989-2021 </td> <td> 1989-2021 </td> </tr> <tr> <td> Rothera, Antarctica (RO) </td> <td> 67.6°S, 68.1°W </td> <td> 2007-2021 </td> <td> 1989-2021 </td> </tr> <tr> <td> Syowa, Antarctica (SW) </td> <td> 69.0°S, 39.6°E </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Neumayer, Antarctica (NM) </td> <td> 70.7°S, 8.3°W </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Terra Nova, Antarctica (TN) </td> <td> 74.8°S, 164.5°E </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Concordia, Antarctica (DO) </td> <td> 75.1°S, 123.4°E </td> <td> 2007-2021 </td> <td> 1989-2021 </td> </tr> <tr> <td> Halley, Antarctica (HB) </td> <td> 75.6°S, 26.8°W </td> <td> - </td> <td> 1989-2021 </td> </tr> </tbody> </table>   Each file corresponds to the whole winter time data set of the station identified by its ID: O3_PassActSLIMCAT_CompositeMSR2SAOZ_ID.txt 1st column: Year 2nd column: Day of Year (DoY) 3rd column: Passive ozone column (without chemistry) modelled by TOMCAT/SLIMCAT (O3pasS). 4th column: Active ozone column (with full chemistry) modelled by TOMCAT/SLIMCAT (O3actS). 5th column: merged data from SAOZ observations and MSR2 (O3comp) Passive and active tracers of SLIMCAT were normalized to O3comp data in the beginning of the winter. NaN is used when there is no observation either from SAOZ instrument or MSR2 dataset or from the model.   References Chipperfield, M. P.: New version of the TOMCAT/SLIMCAT offline chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. Roy. Meteor. Soc., 132, 1179–1203, <a href="https://doi.org/10.1256/QJ.05.51">https://doi.org/10.1256/QJ.05.51</a>, 2006. Pommereau, J.-P., and Goutail, F.: O3 and NO2 ground-based measurements by visible spectrometry during arctic winter and spring 1988, Geophys. Res. Lett., 15, 891–894, <a href="https://doi.org/10.1029/GL015i008p00891">https://doi.org/10.1029/GL015i008p00891</a>,1988. van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Multi sensor reanalysis of total ozone, Atmos. Chem. Phys., 10, 11277–11294, <a href="https://doi.org/10.5194/acp-10-11277-2010">https://doi.org/10.5194/acp-10-11277-2010</a>, 2010. van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012, Atmos. Meas. Tech., 8, 3021–3035, <a href="https://doi.org/10.5194/amt-8-3021-2015">https://doi.org/10.5194/amt-8-3021-2015</a>, 2015.

opencc-by-4.0Apr 2023View details →

Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils

These are datasets and R codes linked to the paper: Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils.

opencc-by-4.0May 2023View details →

LungVis1.0: Active learning AI-powered 3D imaging ecosystem for spatial profiling of lung geometry and pulmonary nanoparticle delivery

The imaging dataset was obtained by light sheet fluorescence microscopy on tissue cleared murine lungs. It includes whole lung autofluorence image, particle fluorescence image, and artifical intelligence nnU-Net generated lung airway segments. The dataset provides 78 healthy murine lung strucutre and airway geometry for C57BL/6 mice and offers comprehensive delivery features including qualitative and quantitative analysis on the temporal and spatial inter- and intra-acinar deposition patterns and NP regional dosimetry for four commonly-used routes of pulmonary delivery,namely intranasal liquid aspiration, intratracheal liquid instillation, ventilator-assisted and nose-only aerosol inhalation. Raw LSFM imaging data collection was carried out between 2017-2021,  the AI code and generated airway segmention were performed in 2021-2022, the whole datasets were then compiled in 2023.  Please ensure to cite our paper for any reuse or reanalysis. Yang, L., Liu, Q., Kumar, P. et al. LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages. Nat Commun 15, 10138 (2024). https://doi.org/10.1038/s41467-024-54267-1 For any inquiries, please feel free to contact us at lin.yang@helmholtz-munich.de

opencc-by-4.0Jun 2023View details →

Publication & Supplementary data: Pro-health compounds and antioxidant activity of 65 potato cultivars

Metadata (climatic conditions, list of varieties, field plan, etc.) and data (carotenoid content, vitamin C content, radical scavenging activity DPPH and FRAP, yellow index) related to the paper of Tatarowska et al., "The content of total carotenoids, vitamin C and antioxidant properties of 65 potato cultivars characterised under the European project ECOBREED" published in Int. J. Mol. Sci. 24 (2023), 11716.

opencc-by-4.0Mar 2023View details →

A Dataset of Bot and Human Activities in GitHub

A Dataset of Bot and Human Activities in GitHub This repository provides an updated version of a dataset of GitHub contributor activities that is accompanied by a paper published at MSR 2023 in the Data and Tool Showcase Track. The paper is entitled A Dataset of Bot and Human Activities in GitHub and is co-authored by Natarajan Chidambaram, Alexandre Decan and Tom Mens (Software Engineering Lab, University of Mons, Belgium). DOI: <a href="https://www.doi.org/10.1109/MSR59073.2023.00070">https://www.doi.org/10.1109/MSR59073.2023.00070</a>. This work is done as a part of Natarajan Chdiambaram's PhD research in the context of <a href="https://www.digitalwallonia.be/ia/">DigitalWallonia4.AI research project ARIAC (grant number 2010235)</a> and <a href="https://trail.ac/en/">TRAIL</a>. The dataset contains 1,015,422 high-level activities made by 350 bots and 620 human contributors on GitHub between 25 November 2022 and 15 April 2023. The activities were generated from 1,221,907 low-level events obtained from the GitHub's Event API and cover 24 distinct activity types. This dataset facilitates the characterisation of bot and human behaviour in GitHub repositories, by enabling the analysis of activity sequences and activity patterns of bot and human contributors. This dataset could lead to better bot identification tools and empirical studies on how bots play a role in collaborative software development. Files description The following files are provided as part of the archive: <ul> <li>bot_activities.json - A JSON file containing 754,165 activities made by 350 bot contributors;</li> <li>human_activities.json - A JSON file containing 261,258 activities made by 620 human contributors (anonymized);</li> <li>JsonSchema.json - A JSON schema that validates the above datasets;</li> <li>bots.txt - A TEXT file containing login names of all the 350 bots</li> </ul> Example Below is an example of a Closing pull request activity: <pre><code>{ "date": "2022-11-25T18:49:09+00:00", "activity": "Closing pull request", "contributor": "typescript-bot", "repository": "DefinitelyTyped/DefinitelyTyped", "comment": { "length": 249, "GH_node": "IC_kwDOAFz6BM5PJG7l" }, "pull_request": { "id": 62328, "title": "[qunit] Add `test.each()`", "created_at": "2022-09-19T17:34:28+00:00", "status": "closed", "closed_at": "2022-11-25T18:49:08+00:00", "merged": false, "GH_node": "PR_kwDOAFz6BM4_N5ib" }, "conversation": { "comments": 19 }, "payload": { "pr_commits": 1, "pr_changed_files": 5 } }</code></pre> List of activity types In total, we have identified 24 different high-level activity types from 15 different low-level event types. They are Creating repository, Creating branch, Creating tag, Deleting tag, Deleting repository, Publishing a release, Making repository public, Adding collaborator to repository, Forking repository, Starring repository, Editing wiki page, Opening issue, Closing issue, Reopening issue, Transferring issue, Commenting issue, Opening pull request, Closing pull request, Reopening pull request, Commenting pull request, Commenting pull request changes, Reviewing code, Commenting commits, Pushing commits. List of fields Not only does the dataset contain a list of activities made by bot and human contributors, but it also contains some details about these activities. For example, commenting issue activities provide details about the author of the comment, the repository and issue in which the comment was created, and so on. For all activity types, we provide the date of the activity, the contributor that made the activity, and the repository in which the activity took place. Depending on the activity type, additional fields are provided. In this section, we describe for each activity type the different fields that are provided in the JSON file. It is worth to mention that we also provide the corresponding JSON schema alongside with the datasets. Properties <ul> <li>date <ul> <li>Date on which the activity is performed</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-25T09:55:19+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>activity <ul> <li>The activity performed by the contributor</li> <li>Type: <code>string</code></li> <li>e.g., "Commenting pull request"</li> </ul> </li> <li>contributor <ul> <li>The login name of the contributor who performed this activity</li> <li>Type: <code>string</code></li> <li>e.g., "analysis-bot", "anonymised" in the case of a human contributor</li> </ul> </li> <li>repository <ul> <li>The repository in which the activity is performed</li> <li>Type: <code>string</code></li> <li>e.g., "apache/spark", "anonymised" in the case of a human contributor</li> </ul> </li> <li>issue <ul> <li>Issue information - provided for Opening issue, Closing issue, Reopening issue, Transferring issue and Commenting issue</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>id <ul> <li>Issue number</li> <li>Type: <code>integer</code></li> <li>e.g., 35471</li> </ul> </li> <li>title <ul> <li>Issue title</li> <li>Type: <code>string</code></li> <li>e.g., "error building handtracking gpu example with bazel", "anonymised" in the case of a human contributor</li> </ul> </li> <li>created_at <ul> <li>The date on which this issue is created</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-10T13:07:23+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>status <ul> <li>Current state of the issue</li> <li>Type: <code>string</code></li> <li>"open" or "closed"</li> </ul> </li> <li>closed_at <ul> <li>The date on which this issue is closed. "null" will be provided if the issue is open</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "2022-11-25T10:42:39+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>resolved <ul> <li>The issue is resolved or not_planned/still open</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this issue</li> <li>Type: <code>string</code></li> <li>e.g., "IC_kwDOC27xRM5PHTBU", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>pull_request <ul> <li>Pull request information - provided for Opening pull request, Closing pull request, Reopening pull request, Commenting pull request changes and Reviewing code</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>id <ul> <li>Pull request number</li> <li>Type: <code>integer</code></li> <li>e.g., 35471</li> </ul> </li> <li>title <ul> <li>Pull request title</li> <li>Type: <code>string</code></li> <li>e.g., "error building handtracking gpu example with bazel", "anonymised" in the case of a human contributor</li> </ul> </li> <li>created_at <ul> <li>The date on which this pull request is created</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-10T13:07:23+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>status <ul> <li>Current state of the pull request</li> <li>Type: <code>string</code></li> <li>"open" or "closed"</li> </ul> </li> <li>closed_at <ul> <li>The date on which this pull request is closed. "null" will be provided if the pull request is open</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "2022-11-25T10:42:39+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>merged <ul> <li>The PR is merged or rejected/still open</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this pull request</li> <li>Type: <code>string</code></li> <li>e.g., "PR_kwDOC7Q2kM5Dsu3-", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>review <ul> <li>Pull request review information - provided for Reviewing code</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>status <ul> <li>Status of the review</li> <li>Type: <code>string</code></li> <li>"changes_requested" or "approved" or "dismissed"</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this review</li> <li>Type: <code>string</code></li> <li>e.g., "PRR_kwDOEBHXU85HLfIn", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>conversation <ul> <li>Comments information in issue or pull request - Provided for Opening issue, Closing issue, Reopening issue, Transferring issue, Commenting issue, Opening pull request, Closing pull request, Reopening pull request and Commenting pull request</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>comments <ul> <li>Number of comments present in the corresponding issue or pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 5</li> </ul> </li> </ul> </li> </ul> </li> <li>comment <ul> <li>Comment information - Provided for all the activities for which the field issue or pull_request is reported and additionally for commit comment</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>length <ul> <li>Length of the comment text (or description text if comment is not provided)</li> <li>Type: <code>integer</code></li> <li>e.g., 25</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this comment or description. "null" will be provided if there is no comment expected</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "IC_kwDOEj6V8c5PHT78", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>gitref <ul> <li>Tag information - provided for Creating branch, Creating tag, Deleting branch, Deleting tag, Editing wiki page and Publishing a release</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>type <ul> <li>Type of the gitref</li> <li>Type: <code>string</code></li> <li>"tag" or "branch" or "commit"</li> </ul> </li> <li>name <ul> <li>Name of the gitref</li> <li>Type: <code>string</code></li> <li>e.g., "cherry-pick-11-to-release-4.10"</li> </ul> </li> <li>description_length <ul> <li>Length of the description text provided while creating the gitref. "null" be provided if the type is "branch" or "commit" as they do not have any description</li> <li>Type: <code>integer</code>, <code>null</code></li> <li>e.g., 23</li> </ul> </li> </ul> </li> </ul> </li> <li>release <ul> <li>Release information - provided for Publishing a release</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>name <ul> <li>The name of the release that is created. "null" will be provided if the name is not provided</li> <li>Type: <code>string</code>, <code>null</code></li> <li>e.g., "v0.65.9"</li> </ul> </li> <li>description_length <ul> <li>Length of the description of the release that is created</li> <li>Type: <code>integer</code></li> <li>e.g., 888</li> </ul> </li> <li>created_at <ul> <li>The date at which the release is created (activity date is the release published date)</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-25T11:34:48+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>prerelease <ul> <li>If the release that is created is a prerelease or not</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>new_tag <ul> <li>If a new tag is created for this release or another tag is re-used</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>GH_node <ul> <li>The corresponding release node ID</li> <li>Type: <code>string</code></li> <li>e.g., "RE_kwDOCm6M2s4FBGxT", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>page <ul> <li>Page information - provided for Editing wiki page</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>name <ul> <li>Name of the page</li> <li>Type: <code>string</code></li> <li>e.g., "Workflow-status"</li> </ul> </li> <li>title <ul> <li>Title of the page</li> <li>Type: <code>string</code></li> <li>e.g., "Workflow status"</li> </ul> </li> <li>new <ul> <li>If the page is created new or existing page is edited</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> </ul> </li> </ul> </li> <li>payload <ul> <li>Other additional details - Provided for Opening pull request, Closing pull request, Reopening pull request and pushing commits</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>pr_commits <ul> <li>The number of commits in this pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 3</li> </ul> </li> <li>pr_changed_files <ul> <li>The number of files that are changed in this pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 2</li> </ul> </li> <li>pushed_commits <ul> <li>The number of commits present in this push</li> <li>Type: <code>integer</code></li> <li>e.g., 4</li> </ul> </li> <li>distinct_pushed_commits <ul> <li>The distinct number of commits present in this push</li> <li>Type: <code>integer</code></li> <li>e.g., 1</li> </ul> </li> <li>github_push_id <ul> <li>The corresponding GitHub push ID</li> <li>Type: <code>integer</code></li> <li>e.g., 11790446870, "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> </ul> Mapping between activities and events For many activity types, the corresponding activity can be observed by the occurrence of a single event type. For example, the activity types Forking repository and Starring repository would require the occurrence of a single event type for each as given below. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Forking repository</td> <td><code>ForkEvent</code></td> <td>-</td> </tr> <tr> <td>Starring repository</td> <td><code>WatchEvent</code></td> <td>action = "started"</td> </tr> </tbody> </table> However, in some cases, the same event type yields different activity types depending on the value present in the payload. For example, three different activity types can be generated from the same low-level event type CreateEvent, depending on the value of its ref_type (either "repository", "branch", or "tag") present in the payload. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Creating repository</td> <td><code>CreateEvent</code></td> <td>ref_type = "repository"</td> </tr> <tr> <td>Creating branch</td> <td><code>CreateEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td>Creating tag</td> <td><code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> </tbody> </table> In some cases, there is no one-to-one mapping between events and activities. This is because some actions on GitHub may generate more than a single event and lead to a sequence of one mandatory event and a second optional event (marked with ?). For example, for the activity type Publishing a release, event type ReleaseEvent is mandatory with payload's action value = "published", while event type CreateEvent is optional as it is required only when a new tag is created along with the published release. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Publishing a release</td> <td><code>ReleaseEvent</code></td> <td>action = "published"</td> </tr> <tr> <td> </td> <td>? <code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> </tbody> </table> All the identified activities along with their events type(s) and payload information is given in the following table. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Creating repository</td> <td><code>CreateEvent</code></td> <td>ref_type = "repository"</td> </tr> <tr> <td>Creating branch</td> <td><code>CreateEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td>Creating tag</td> <td><code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td>Deleting tag</td> <td><code>DeleteEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td>Deleting repository</td> <td><code>DeleteEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td>Publishing a release</td> <td><code>ReleaseEvent</code></td> <td>action = "published"</td> </tr> <tr> <td> </td> <td>? <code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td>Making repository public</td> <td><code>PublicEvent</code></td> <td>-</td> </tr> <tr> <td>Adding collaborator to repository</td> <td><code>MemberEvent</code></td> <td>action = "added"</td> </tr> <tr> <td>Forking repository</td> <td><code>ForkEvent</code></td> <td>-</td> </tr> <tr> <td>Starring repository</td> <td><code>WatchEvent</code></td> <td>action = "started"</td> </tr> <tr> <td>Editing wiki page</td> <td><code>GollumEvent</code></td> <td>pages-->action = "created" or "edited"</td> </tr> <tr> <td>Opening issue</td> <td><code>IssuesEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td>Closing issue</td> <td><code>IssuesEvent</code></td> <td>action = "closed"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Reopening issue</td> <td><code>IssuesEvent</code></td> <td>action = "reopened"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Transferring issue</td> <td><code>IssuesEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td>Commenting issue</td> <td><code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Opening pull request</td> <td><code>PullRequestEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td>Closing pull request</td> <td><code>PullRequestEvent</code></td> <td>action = "closed"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Reopening pull request</td> <td><code>PullRequestEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Commenting pull request</td> <td><code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Commenting pull request changes</td> <td><code>PullrequestReviewCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td> </td> <td>? <code>PullRequestReviewEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Reviewing code</td> <td><code>PullRequestReviewEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Commenting commits</td> <td><code>CommitCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Pushing commits</td> <td><code>PushEvent</code></td> <td>-</td> </tr> </tbody> </table>

CollecTRI Data for Investigation of SETBP1 gene expression and transcription factor activity across human tissues

Here we provide the human CollecTRI prior (accessed May 2023) for inference of TF activity across 31 GTEx tissues using multivariate linear modeling method decoupleR. The `human_prior_tri.csv` includes 1,178 unique TFs (referred to as the source) that target 6,627 unique genes (referred to as targets) to give us 42,595 interactions in the CollecTRI prior input. Interactions are represented as a + or - 1 (mor).

The Plasma-Prescribed Active Region Static Extrapolation Dataset

The Plasma-Prescribed Active Region Static Extrapolation (PARSE) Dataset consists of approximately seven thousand magnetohydrostatic extrapolations of solar active regions for use in statistical or machine learning applications. The extrapolations are based on the Spaceweather HMI Active Region Patch (SHARP) library (doi <a href="https://doi.org/10.1007/s11207-014-0529-3">10.1007/s11207-014-0529-3</a>), and the magnetohydrostatic extrapolation is performed by the routine detailed in Mathews et al 2022 (doi <a href="https://doi.org/10.1016/j.jcp.2022.111214">10.1016/j.jcp.2022.111214</a>).

Distinct States in an Active Liquid Crystal of Flagella and Kinesin-Powered Microtubules under Varying Kinesin Concentrations or Active Stress

At a constant nematic elasticity (flagella concentration) and confinement (H=100μm), higher KSA (120nM) or motor concentrations induce robust active stress that disrupts nematic alignment, resulting in chaotic behavior. Conversely, lower KSA (<60nM) concentrations fail to overcome the nematic elastic background's alignment, leading to a unique state where active microtubule bundles phase-separate into lanes within a uniform flagella background. The confocal micrograph reveals this distinct phenomenon, showing uniform flagella distribution alongside microtubule segregation into lane-like structures within the channel's limited confinement.

Distinct States in an Active Liquid Crystal of Flagella and Kinesin-Powered Microtubules under Varying Confinement

At significant confinement levels, the potent active stress disrupts the alignment of the flagella, leading to a turbulent state in the composite liquid crystal. Conversely, minimal confinement enables the dominant nematic alignment to counteract the active stress, inducing the phase separation of microtubules within a uniform flagella background. The confocal micrograph unveils uniform flagella distribution across the medium, while the microtubules segregate into a distinct lane-like structure within the channel's narrowest confinement.

ERK activity in migrating MDA-MB-231 cells (clover-ERK-KTR + sir-DNA)

MDA-MB-231 or U2OS cells stably expressing clover-ERK-KTR were seeded on fibronectin-coated (1 µg /ml) Ibidi 8-well slides (Ibidi) 1 day before imaging. Four hours before imaging, the medium was supplemented with 250 nM sir-DNA (Cytoskeleton) and 25 mM HEPES (Sigma). Cells were then imaged live (37 °C, 5% CO2) using a Nikon Eclipse Ti2-E microscope (Nikon) equipped with an sCMOS Orca Flash4.0 camera (Hamamatsu) and controlled by the NIS-Elements software (Nikon, v 5.11.01). MDA-MB-231 cells were imaged using a 20× Nikon CFI Plan Apo Lambda objective (NA 0.75), either 1 frame per minute for 2 hours or 1 frame every 5 minutes for 17 hours. In these experiments, a camera binning of 2 × 2 was used.  This dataset consists of 20 videos.

opencc-by-4.0Dec 2022View details →

Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions

Scripts and dataset for Vinchira-Villarraga et al., 2024 "Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions". The files label as A1, C6, H2/HCN2 and O1 correspond to the dataset obtained for Ash, Cherry, Horse-chestnut and Oak respectively.  _batch.xml files contains the script for the pre-processing of .mzML mass spectrometry data created on MzMine 3.2.8. The file can be uploaded to newer versions of MzMine, but due to the addition of new modules, some parameters name have changed. _SIRIUS.mfg files contains the mass spectrometry data of each dataset for analysis in SIRIUS. The files were used for chemical formula prediction, classification and compound annotation using SIRIUS 5.0 _quant.csv correspond to the output matrix as it was obtained from MzMine without noise, adducts and highly variable features filtering. _abundance.csv correspond to the output matrix after noise, adducts and highly variable features filtering.

Monitoring valve activity in M.edulis and M.galloprovincialis: Valve signal processing

This repository provides access to the metadata and scripts used to monitor valve gaping activity of bivalves using valvometry (Valve-Trek ; Technosmart Europe srl, www.technosmart.eu). The dataset contains valve activity records in csv format per individual sampled from February to May 2023 at Agon-Coutainville (Normandy, France), as well as r scripts for processing, formatting and analyzing valve gaping data.

Mechanism-Based Redesign of GAP to Activate Oncogenic Ras

Additional material for Berta et al. J. Am. Chem. Soc. 2023, 145, 37, 20302–20310 <ul> <li>Paths: minimised structures corresponding to the the GTP hydrolysis mechanism.</li> <li>MD setup: equilibriated inputs for classical MD simulations for WT and mutant systems in CHARMM/NAMD format, set up for the charmm36m FF.</li> <li>NBO: outputs for Natrual Bonding Orbitals analysis carried out for TS and RS structures.</li> <li>GAP mutants: optimised TS and RS structures of the most promising designed GAP mutants.</li> </ul>

Soil redox potential vs. soil microbial activity and structure

The dataset was collected as a part of the Carbon Action field experiment in 2021. The experiment had 100 farms testing carbon farming practices 2019-2023. These samples were from a subset of 20 farms, which were routinely sampled every year. I measured the following parameters <ul> <li>redox potential Eh, with an ORP meter + electrode reference voltage at 50% pore filled moisture</li> <li> pH in distilled water 10:1 water:soil mixture</li> <li>VESS soil structure on the field</li> <li>CO2burst by rewetting dried soil and measuring how much CO2 was respired in 24 hours</li> </ul> pH and Eh were used to calculate a rH2 hydrogen potential, which combines Eh and pH Background information for clay and organic matter content was determined separately the clay content was defined by sieving and the organic matter content by loss of ignition. The analysis of the results is published in Plant and Soil journal in 2023.      Mattila, T.J., 2023. Redox potential as a soil health indicator - how does it compare to microbial activity and soil structure? <a href="https://doi.org/10.1007/s11104-023-06305-y">https://doi.org/10.1007/s11104-023-06305-y</a>

Dataset for "Phenolic Substitution in Fidaxomicin: A Semisynthetic Approach to Antibiotic Activity Across Species"

ZIP File: Characterisation data (such as e.g. NMR, IR, MS spectra) NMR raw data, .mnova files ChemDraw drawings (.cdx files) PDF file: Supporting information for Phenolic Substitution in Fidaxomicin: A Semisynthetic Approach to Antibiotic Activity Across Species Erik Jung,[a] Anastassia Kraimps,[a] Silvia Dittmann,[b] Tizian Griesser,[c] Jordan Costafrolaz,[d] Yves Mattenberger,[d] Simon Jurt,[a] Patrick H. Viollier,[d] Peter Sander,[c] Susanne Sievers,[b] and Karl Gademann*[a] [a] E. Jung, A. Kraimps, S. Jurt, Prof. Dr. K. Gademann Department of Chemistry, University of Zurich 8057 Zürich (Switzerland) E-mail: karl.gademann@uzh.ch [b] S. Dittmann, Dr. S. Sievers Department of Microbial Physiology and Molecular Biology Institute of Microbiology Center for Functional Genomics of Microbes University of Greifswald Greifswald (Germany) [c] T. Griesser, Prof. Dr. P. Sander Institute of Medical Microbiology University of Zurich Zurich (Switzerland) [d] J. Costafrolaz, Dr. Y. Mattenberger, Prof. Dr. P. H. Viollier Department of Microbiology and Molecular Medicine Faculty of Medicine, University of Geneva Geneva (Switzerland)   Abstract of the corresponding publication: Fidaxomicin (Fdx) is a natural product antibiotic with potent activity against Clostridioides difficile and other Gram-positive bacteria such as Mycobacterium tuberculosis. Only a few Fdx derivatives have been synthesized and examined for their biological activity in the 50 years since its discovery. Fdx has a well-studied mechanism of action, namely inhibition of the bacterial RNA polymerase. Yet, the targeted organisms harbor different target protein sequences, which poses a challenge for the rational development of new semisynthetic Fdx derivatives. We introduced substituents on the two phenolic hydroxy groups of Fdx and evaluated the resulting trends in antibiotic activity against M. tuberculosis, C. difficile, and the Gram-neg- ative model organism Caulobacter crescentus. As suggested by the target protein structures, we identified the preferable derivatisation site for each organism. The derivative ortho- methyl Fdx also exhibited activity against the Gram-negative C. crescentus wild type, a first for fidaxomicin antibiotics. These insights will guide the synthesis of next-generation fidaxomicin antibiotics.