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Research Data Open Access Video data(Philipps-Universität Marburg) Schikora, Juliane; Dort, Aaron; Nickel, Antonia; Bergert, Jasmin; Schayan-Araghi, Stella Y.; Banerjee, Pratiti; Wolf, Hannah N.; May-Simera, Helen; Pauly, Diana; Pauly, DianaThis subset of the repository contains the video data of the study. This includes mp4-files that show stained proteins in an apical to basal direction. It further contains the Fiji macro used for generating mp4-files from tif-files that can be found in the image data subset of this collection.Research Data Open Access Vorschlag für ein gemeinsames Metadatenschema für die Sammlungen der Philipps-Universität Marburg(Philipps-Universität Marburg, Stabsstelle Forschungsdatenmanagement) Maul, AlexanderDie Philipps-Universität Marburg entwickelt derzeit ein Konzept zur digitalen Präsentation ihrer wissenschaftlichen Sammlungen. Hierbei verfolgt ein im Rahmen der Landesinitiative HeFDI vom Hessischen Ministerium für Wissenschaft und Forschung, Kunst und Kultur (HMWK) gefördertes Projekt den Auftrag, Datenbankmanagementsysteme zur Integration wissenschaftlicher Sammlungen anzu-passen. Neben der Konzeptionierung und Implementierung einer langfristig trag-fähigen Datenbank hat das Projekt die Pilotierung der Sammlungsdigitalisierung an der Philipps-Universität zur Aufgabe. Der Projektfortschritt wird über die Projektseite (https://uni-marburg.de/DFpFGf) dokumentiert. Der Datensatz enthält einen Vorschlag für ein gemeinsames Metadatenschema für die Sammlungen der Philipps-Universität. Hierbei unterteilt sich das Modell in folgende Bereiche: 1. Automatische Administrationsfelder 2. Minimalerfassung 3. Empfohlene Erfassungsfelder 4. Zusätzliche Felder für Objekte aus sensiblen Kontexten 5. Akzession, Bilanzierung, Deakzession 6. Interne Dokumentation und Sammlungsmanagement 7. Individuelle sach- und fachspezifische Metadatenfelder Der Vorschlag gibt den Bearbeitungsstand im November 2024 wieder. Er ist ausdrücklich als Vorschlag konzipiert, auf dessen Grundlage weitere Absprachen erfolgen sollten.Research Data Open Access Analytical data(Philipps-Universität Marburg) Schikora, Juliane; Dort, Aaron; Nickel, Antonia; Bergert, Jasmin; Schayan-Araghi, Stella Y.; Banerjee, Pratiti; Wolf, Hannah N.; May-Simera, Helen; Pauly, DianaThis subset of the repository contains the analytical data generated in the study. This includes data of the transcript expression analysis, multiplex immunoassays, enzyme-linked immunosorbent assays, image analyses, and measurement of transepithelial resistance. Furthermore, the validation of the multiplex immunoassays with purified proteins and sera is included.Research Data Open Access In vitro-in situ comparison study - Data set(Philipps-Universität Marburg) Jablonski-Momeni, AnahitaThis data set presents the data which were analysed for the following publication: A comparison between in vitro and randomized in situ models for remineralization of artificial enamel lesions Jablonski-Momeni A, Lentz J, Jablonski B, Kiesow A, Morawietz M. Sci Rep. 2024 Oct 25;14(1):25295. doi: 10.1038/s41598-024-76387-w. Abstract: The randomized study aimed to evaluate the comparability of in situ (iS) and in vitro (iV) study protocols regarding remineralization of artificial enamel lesions. Two toothpastes (group A: 1450 ppm sodium fluoride, group B: placebo 0 ppm F-), were investigated. IV, a pH-cycling model with toothpaste slurry treatment was applied for 10d. IS, remineralization was performed in 9 participants wearing splints with embedded enamel samples for 10 and 21d, randomly allocated to groups A and B. Samples were scanned by X-ray micro-computed tomography (μCT) and grayscale value line profiles corresponding to mineral density (rel.ΔZ) were analyzed. T-Test for dependent and independent data and analysis of variance (ANOVA) were used for further analyses (α = 0.05). Rel.ΔZ of fluoride treated samples (A) were iV = 40.2%, iS 10d = 11.5% and iS 21d = 46.1% (p > 0.05). Rel.ΔZ of placebo treated samples (B) were: iV = − 6.2%, iS 10d = 25.2% and iS 21d = 11.0% (p > 0.05). Remineralization potential of both toothpastes was significantly different regarding iV (p < 0.001) and iS after 21d (p = 0.034), while in case of iS 10d no significant difference was detected (p = 0.4). Despite different study protocols the μCT results after remineralization were comparable between iV and iS. The results suggest that selected studies can be carried out in faster, simplified iV studies using pH-cycling instead of iS studies.Research Data Open Access Human Uniprot Sequences FastafileBhagwat, Aditya M; Graumann, JohannesResearch Data Open Access Structural reflection data (.mtz) of soaked Sudan virus VP40 crystals(Philipps-Universität Marburg, Institut für Virologie) Werner, Anke-Dorothee; Becker, StephanThis repository contains structural data (.mtz) of Sudan virus VP40 crystals, soaked with small molecules. Files are ordered names correspond to internal lab crystal IDs (e.g. XDS_ASCII_AW61_scaled1.mtz, with "AWxxx" indicating the crystal ID). The resolution and data quality indicators for each dataset are provided in an accompanying metadata file. Users should refer to this file to select appropriate datasets for their analyses. Dimeric VP40 (approx. 7 mg/ml in 25 mM Tris, 300 mM NaCl, pH 8) was mixed 1:1 with crystallization buffer (100 mM HEPES, 40 mM MgCl2, 10% v/v PEG400). Crystals grew overnight at 18 °C using the hanging drop method. Fragments originating from the FragXtal Screen (Jena Biosciences) were dissolved in DMSO to 1 M and diluted 1:10 in crystallization buffer (with or without 20% ethylene glycol as a cryoprotectant) to a final concentration of 100 mM. Crystals were then placed in a drop of the diluted fragments and soaked for either only seconds, minutes, 1 h, or overnight. Crystals were then harvested, flash-frozen in liquid nitrogen and analyzed at the Swiss Light Source, Paul-Scherrer Institute, Villigen, Switzerland (SLS BEAMLINE X06SA, DECTRIS EIGER X 16M detector, single wavelength, data collection temperature 100 K). Datasets were collected and processed using XDS and scaled using the ccp4i suite Aimless. Mtz-files can be used for molecular replacement (using PDB-ID 8B3X or other structures of VP40 as template). To use these mtz files for molecular replacement: 1) Download the desired mtz file(s) and the accompanying metadata. 2) Initial Structure Solution: a. Use molecular replacement with programs such as Phaser or MOLREP from the CCP4 suite. b. Use PDB-ID 8B3X as the initial search model. Other VP40 structures may also be suitable. 3) Rapid Initial Model Building and Refinement: a. Use DIMPLE (Difference Map Pipeline) for quick initial refinement and map calculation. b. Run DIMPLE. c. This will produce refined models and maps for each dataset, suitable for initial analysis or as input for PanDDA. 4) Fragment Identification: a. For datasets suspected to contain bound fragments, use PanDDA (Pan-Dataset Density Analysis). b. Prepare input files as per PanDDA documentation, using DIMPLE output. c. Run PanDDA with appropriate parameters. d. Examine PanDDA event maps for evidence of bound fragments. 5) Model Building and Refinement: a. Build fragments into positive difference density or PanDDA event maps. b. Refine structures using programs like REFMAC5 or phenix.refine. Related datasets deposited to the Protein data bank (https://www.rcsb.org/) include 8B2U (Crystal structure of SUDV VP40 in complex with salicylic acid) and 8B1S (Co-crystal of SUDV VP40 with salicylic acid). This work was funded by the LOEWE Center DRUID (State of Hesse, Germany), project A1. For any questions regarding the use of these datasets or analysis methods, please contact the depositing authors Stephan Becker or Anke-Dorothee Werner at the insitutte for Virology, Marburg, Germany (https://www.uni-marburg.de/en/fb20/departments/ciii/virology).Research Data Open Access Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model- Springer2024(Philipps-Universität marburg, 07.05.2021) Endres, Dominik; Meibodi, Neda; Meibodi, NedaThis repository contains the files and data necessary to recreate the results from the paper Meibodi, N., Abbasi, H., Schubö, A. et al. Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model. Comput Brain Behav 7, 268–285 (2024). https://doi.org/10.1007/s42113-024-00197-6 Please go to Version 2 if you are interested to see the files related to the other pubplication N.Meibodi, H.Abbasi, A. Schuboe, D. Endres (2021) A Model of Selection History in Visual Attention, Proceedings of the 2021 Conference of the Society of Cognitive Science, Vienna, Austria.Research Data Open Access Recognition of European mammals and birds in camera trap images using deep neural networks(Philipps-Universität Marburg) Schneider, Daniel; Lindner, Kim; Vogelbacher, Markus; Bellafkir, Hicham; Mühling, Markus; Farwig, Nina; Freisleben, BerndThis record contains the trained models and the test data sets presented in the papers "Recognizing European mammals and birds in camera trap images using convolutional neural networks" (Schneider et al, 2023) and "Recognition of European mammals and birds in camera trap images using deep neural networks" (Schneider et al., 2024). In these publications, we present deep neural network models to recognize both mammal and bird species in camera trap images. In the archive files "model2023_ConvNextBase.tar" and "model2023_EfficientNetV2.tar" as well as "model2024_ConvNextBase_species.tar" and "model2024_ConvNextBase_taxonomy.tar" we provide downloads of the best trained models from our 2023 and 2024 papers, respectively. All models are provided in the Tensorflow2 SavedModel format (https://www.tensorflow.org/guide/saved_model). A script to load and run the models can be found in our Git-Repository: https://github.com/umr-ds/Marburg-Camera-Traps. There we also provide a code snippet to perform predictions with these models. In the archive files "data_MOF.tar" and "data_BNP.tar", we provide downloads for our Marburg Open Forest (MOF) and Białowieża National Park (BNP) data sets, consisting of about 2,500 and 15,000 labeled camera trap images, respectively. The files contain two folders named "img" and "md", respectively. The "img" folder contains the images grouped in subfolders by recording date and camera trap id. The "md" folder contains the metadata for each image, which constists of the bounding box detections obtained using the MegaDetector model (https://github.com/agentmorris/MegaDetector). The metadata is grouped into yaml-files for each label at different taxonomic levels.Research Data Open Access Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning(Philipps-Universität Marburg) Augustat, Nick; Endres, Dominik; Müller, Erik MalteThis repository includes code and data necessary for running the online survey and task, and the analyses of the manuscript "Augustat, N., Endres, D., Müller, E. M.: Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning". Abstract: The placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty, analogously to reward uncertainty, may affect reward learning after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward learning. In an online between-subjects experiment with N=141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.Research Data Open Access