Find research datasets worth reusing

Computational Data for "Synthesis of (1-silyl)allylboronates by KOtBu-catalyzed ring-opening gem-silylborylation of cyclopropenes"

Computational data used for the article "Synthesis of (1-silyl)allylboronates by KOtBu-catalyzed ring-opening gem-silylborylation of cyclopropenes". This Includes the computational output files of geometries, optimizations, and frequency calculations of all mentioned structures.

Additional raw video and pose estimation data of top view open field mouse behavior recordings after diazepam injections

This repository contains raw data for 32 different open field recordings of mice. These include top view raw video .mp4 files (Videos.zip and Videos_2.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from a single diazepam injection experiment at Roche, where animals were injected with saline, 1 mg/kg, 2 mg/kg or 3 mg/kg of diazepam. The METADATA_ROCHE.csv or METADATA_ROCHE.xlsx files contain all grouping variables and help linking the pose estimation files (located in data/) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how we have used this data.

April 7, 2024 (v1) Image Open Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 5 relative to Figure 7 - Figure Supplement 4

Source data file relative to Figure 7 – figure supplement 4 Panel A Raw image of agarose gel showing the 2 alternative mRNAs encoding for ArhGEF11 in control animals (left track, control) and after injection of the MO at the one cell stage (right track, +MO at 2 and 5ng). The source data includes the raw files (native format .scn and open source format .tiff) as well as a pdf file showing both the full scale image and the cropped image selected for the figure.

opencc-zeroJun 2024View details →

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Selection favors high spread and asymmetry of flower opening dates within plant individuals

Natural selection on traits expressed repeatedly by individuals is usually investigated with a focus on mean values, although within-individual trait distributions often differ also in other aspects, such as their spread and shape. In plants producing multiple flowers during a season, there might not be a single optimal flowering time, but rather an optimal distribution of flower opening dates. This optimal distribution might depend on both resource allocation patterns and interactions with the abiotic and biotic environment. In this study, we quantified mean, variance, skewness and kurtosis of 495 individual flowering schedules (5287 flowers) over three years, and assessed phenotypic selection on these aspects of the within-individual distribution of opening dates in the perennial herb Lathyrus vernus. We also explored how selection on within-individual variation in flowering schedules was related to effects on two fitness components: fruit set and the proportion of seeds escaping pre-dispersal predation. Within-individual variation in phenology was larger than, or at least similar to, among-individual variation in all years. We found phenotypic selection on several aspects of individual flowering schedules. In one year, selection favoured plants with higher variance in opening dates, and this coincided with a higher fruit set in plants with an increased spread of the flowering schedule. In two of the study years, selection favoured a higher asymmetry of the flowering schedule, and plants with more right-skewed distributions had higher fruit set and higher proportions of seeds escaping predation. Both fruit set and seed predation increased with an earlier mean flowering, resulting in no net selection on mean flowering date. Synthesis. Our results suggest that phenotypic selection on the spread and shape of flower opening date distributions might be at least as important as selection on the mean flowering date. In a broad sense, this implies that we should consider the entire trait distribution if we aim to understand the evolution of traits that are expressed multiple times within individuals.

Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Collection of Forest Resilience Indicators)

This dataset represents the collection of forest indicators for my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor".  It contains a classification of inicators sourced from: <ul> <li> Nikinmaa, L., Lindner, M., Cantarello, E., Jump, A. S., Seidl, R., Winkel, G., & Muys, B. (2020). Reviewing the use of resilience concepts in forest sciences. Current Forestry Reports, 6, 61-80. </li> <li> European Commission. (2023a). Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on a monitoring framework for resilient European forests. Directorate-General for Environment. Lastly retrieved on December 17, 2023 from <a href="https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en">https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en</a>. </li> <li> European Parliament. (2024, February 27). European Parliament legislative resolution of 27 February 2024 on the proposal for a regulation of the European Parliament and of the Council on nature restoration (COM(2022)0304 – C9-0208/2022 – 2022/0195(COD)). Lastly retrieved on April 26, 2024 from <a href="https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html">https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html</a>. </li> </ul>

Monthly production and open-circuit string voltage measurements after 10-year operation of three photovoltaic plants in Southern Spain affected by severe potential-induced degradation

Data are formated in a spreadsheet file.  Data are presented from three photovoltaic plants in Southern Spain (Córdoba province - Northern Andalucía) designed and installed by the same person, with the same photovoltaic module and the same model of inverter, deployed at the same time (end 2009). The plants are severely affected by potential-induced degradation (PID), so that secondary effects produce some by-pass diodes to activate, producing three families of open-circuit voltage (Voc) in the modules (~40V), (~26V) and (~12V) of a total of nominal Voc of 42,6V.  Monthly production is shown along 11 years (2010-2021), and the measurements of the open-circuit voltage of the strings after 10 years of operation along with the voltage range of the modules of each string. Sheet 1: configuration of the architecture of the three photovoltaic plants and the features of the photovoltaic module installed. Sheet 2: energy production for 11 years of opetation. In the last years some recovery is shown in plant 1 and 2 because a repowering project. Sheet 3: partial climate data of the towns were the plants are located. Sheet 4: open-circuit voltages of the strings and number of modules in each string with open-circuit voltage in the ranges (~40V) and (<=26V) in plant 1, july-2018. Sheet 5: open-circuit voltages of the strings and number of modules in each string with open-circuit voltage in the ranges (~40V), (~26V) and (~12V) in plant 2, july-2020.

Reproducible Evaluation of Open-Source Tools for Prostate Segmentation on Public Datasets

Segmentation of the prostate and surrounding regions is important for a variety of clinical and research applications. Our goal is to evaluate the generalizability of publicly available state-of-the-art AI models on publicly available datasets. To compare the AI generated segmentations to the available manually annotated ground-truth, quantitative measures such as Dice Coefficient and Hausdorff distance, along with shape radiomics features, were analyzed. Our study also aims to show how cloud-based tools can be used to analyze, store, and visualize evaluation results.  Three open-source pre-trained AI prostate segmentation tools were evaluated against expert annotations, on three publicly available MRI prostate collections, available in NCI Imaging Data Commons[1]. Two pre-trained models originate from the nnU-Net framework[2], the last pre-trained model originates from Prostate158 paper[4]. ProstateX[5], QIN-Prostate-Repeatability[6] and PROSTATE-MRI-US-Biopsy[7]. Expert annotations of the the whole prostate gland, peripheral zone (PZ) and transition zone (TZ) of the prostate  are available for ProstateX collection, whole prostate gland and PZ for  QIN-Prostate-Repeatability collection, and whole prostate gland for PROSTATE-MRI-US-Biopsy collection. We rely on the DICOM standard to encode our segmentation and radiomics results. The DICOM standard aims to achieve interoperability and FAIR[10] principles. Encoding our results in DICOM representation allows us to leverage DICOM-reliant tools, such as Google Cloud Computing tools for storage,computation, analysis and visualization. Open-source DICOM-based visualization tools such as OHIF[8] viewer can also be used to look qualitatively at the AI and expert annotations and the referenced images.. DICOM Segmentation objects are used to encode the AI models predictions, using dcmqi[11], DICOM Structured Reports on the other hand are used to encode radiomics features[3] extracted from the AI and expert annotations, using dcmqi and highdicom[12].   This dataset is organized in three parts:  AI_SEGMENTATIONS_DICOM.zip, AI_STRUCTURED_REPORTS_DICOM.zip and EXPERT_SRUCTURED_REPORTS_DICOM..zip. All zip files contain DICOM objects only, sorted based on DICOM attributes, following this pattern: PatientID/     └───Modality-%StudyInstanceUID/           └───%SeriesInstanceUID-%SeriesDescription.dcm. AI_SEGMENTATIONS_DICOM.zip contains all the pre-trained AI models evaluated segmentation results, encoded as DICOM Segmentation objects. AI_STRUCTURED_REPORTS_DICOM..zip contains firstorder and shape radiomics features extracted for the AI segmentation results, such as Segmentation Volume, encoded as DICOM Structured Reports. EXPERT_SRUCTURED_REPORTS_DICOM.zip contains firstorder and shape radiomics features extracted for the expert annotations (for ProstateX, QIN-Prostate-Repeatability and PROSTATE-MRI-US-Biopsy collections) stored a DICOM Structured Reports objects. Code repository containing evaluation cloud-based notebooks and results/metadata .csv tables is available here: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a> <h2>Additional Notes</h2>  This project has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l. https://portal.imaging.datacommons.cancer.gov/ nnU-Net: <a href="https://github.com/MIC-DKFZ/nnUNet">https://github.com/MIC-DKFZ/nnUNet</a> Prostate158: <a href="https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy">https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy</a> Pyradiomics: <a href="https://github.com/AIM-Harvard/pyradiomics">https://github.com/AIM-Harvard/pyradiomics</a> Highdicom: <a href="https://github.com/herrmannlab/highdicom">https://github.com/herrmannlab/highdicom</a> DCMQI: <a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a> Github repo: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a> <h2>Related information</h2> ProstateX - <a href="https://doi.org/10.7937/K9TCIA.2017.MURS5CL">https://doi.org/10.7937/K9TCIA.2017.MURS5CL </a> QIN-Prostate-Repeatability - <a href="https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND">https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND</a> PROSTATE-MRI-US-BIOPSY - <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">https://doi.org/10.7937/TCIA.2020.A61IOC1A</a> <h2>References </h2> [1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188. [2] Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021 Feb;18(2):203-11. [3] Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7. [4] Adams, Lisa C., Marcus R. Makowski, Günther Engel, Maximilian Rattunde, Felix Busch, Patrick Asbach, Stefan M. Niehues, et al. 2022. “Prostate158 - An Expert-Annotated 3T MRI Dataset and Algorithm for Prostate Cancer Detection.” Computers in Biology and Medicine 148 (September): 105817. [5] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A [6]  Fedorov, A; Schwier, M; Clunie, D; Herz, C; Pieper, S; Kikinis, R; Tempany, C; Fennessy, F. (2018). Data From QIN-PROSTATE-Repeatability. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2018.MR1CKGND [7]  Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A [8] Open Health Imaging Foundation Viewer: An Extensible Open-Source Framework for Building Web-Based Imaging Applications to Support Cancer Research. Erik Ziegler, Trinity Urban, Danny Brown, James Petts, Steve D. Pieper, Rob Lewis, Chris Hafey, and Gordon J. Harris [9] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013 Dec;26(6):1045-57. [10] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016 Mar 15;3(1):1-9. [11] Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research. 2017 Nov 1;77(21):e87-90. [12] Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: A python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology. Journal of Digital Imaging. 2022 Aug 22:1-9.

Video delle lezioni - Open Science come e perché / Corso Universitario Aggiornamento Professionale per Data Steward, Università di Torino marzo 2024

Video delle lezioni su Open Science, FAIR data management, politiche europee per CUAP data steward Università di Torino Contenuto; <ol> <li>[DA AGGIUNGERE]</li> </ol>

Data underlying research paper: Exploring Governance Modes in Open Data Initiatives: Insights from France and Ireland

The folder contains the interview questions of the research paper “Exploring Governance Modes in Open Data Initiatives: Insights from France and Ireland” and consists of the interview questions.   <div> Note about the interview and the interview questions:  </div> <div> The semi-structured interviews were conducted with (three) experts selected through purposive sampling combined with snowballing approach to gain knowledge from key people who actively worked in the coordination bodies in France and Ireland. In the case of France, the first interviewee was reached following the reading of a blogpost on the governance of open data during the Covid-19 crisis. The first interviewee allowed the identification of a second expert who could complement his knowledge on the governance of the open data in France. In the case of Ireland, the expert was identified through the reading of an academic article that explained in great details the unfolding of the events that led to the Covid-19 open data strategy in Ireland. All the interviewees were approached through email exchange. Interviews took place in August 2022 online through the Microsoft Teams and Webinaire de l’État platforms, each lasting approximately forty-five minutes.  Acknowledgment: </div> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

opencc-by-4.0Sep 2022View details →

Figure 1 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 1. Examples of images collected for soybean in the VE-VC (A) and R2 (B) growth stages.

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Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge

The main problem in the operation of nuclear power plants is the scale formation of mineral impurities in an open recirculating system. However, water discharge from an open recirculating system into water bodies can lead to changes in the chemical equilibrium of wastewater components and requires constant monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts in water in an open recirculating system during water treatment using the example of the Rivne Nuclear Power Plant. Moreover, the analysis impact of their discharge with return water in the Styr River. The dissolved inorganic salt concentration has a significant impact on the efficiency of the system and the environment of an open recirculating system power plant. Altogether, each dissolved inorganic salt component was analysed separately using standard measurement methods, using statistical methods of data processing, and correlation analysis. In addition, the annual discharge of the dissolved inorganic salts components was calculated and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the influence of the formation of the dissolved inorganic salts and changes in their concentration value during the discharge of returned water into a natural water body was analysed.

opencc-zeroJun 2024View details →

Survey of library information science graduates' readiness to create an information system in the library (using open science as an example)

Survey "Readiness of library information science graduates to create an information system in the library (on the example of open science)" The research is aimed at identifying the degree of readiness of future specialists in the field of library and information science to work in new and relevant areas of library activities related to the development of information infrastructure of open science. The survey consists of 4 thematic blocks and contains 21 questions. The survey was attended by 54 students from 10 Russian universities, studying in the direction of training 51.03.06 "Library and Information Science". The results of this survey will serve as a basis for the development of professional development programmes on various aspects of the functioning of open science for specialists in the field of library and information activity. Опрос «Готовность выпускников направления библиотечная информационная наука к созданию системы информирования в библиотеке (на примере открытой науки)» Исследование направленно на выявление степени готовности будущих специалистов в области библиотечно-информационной деятельности к работе в новых и актуальных направлениях деятельности библиотек, связанных с развитием информационной инфраструктуры открытой науки. Опрос состоит из 4-х тематических блоков и содержит 21 вопрос. В опросе приняли участие 54 студента из 10 российских вузов, обучающихся по направлению подготовки 51.03.06 «Библиотечно-информационная деятельность». Результаты данного опроса послужат основой разработки программ повышения квалификации по различным аспектам функционирования открытой науки для специалистов в области библиотечно-информационной деятельности.

Cataloging Distant Galactic Open Clusters: Identification of 739 New Star Clusters Beyond 5 kpc Utilizing GAIA DR3 Data

The figures of the 739 open clusters that report in our paper (Cataloging Distant Galactic Open Clusters: Identification of  739 New Star Clusters Beyond 5 kpc Utilizing GAIA DR3 Data)  complete King's model  profile fitting, sky charts and 5-panels ( spatial distribution, proper-motion distribution, parallax statistics, parallax distribution, and CMD) .

Structural determinants of ivabradine block of the open pore of HCN4

Dataset underlying findings published in: "Structural determinants of ivabradine block of the open pore of HCN4", PNAS (DOI: https://doi.org/10.1073/pnas.2402259121)

opencc-zeroJun 2024View details →

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Neural FoxP2 expression in an aging open-ended vocal learner

Most vocal learning species exhibit an early critical period during which their vocal control neural circuity can facilitate the acquisition of new vocalizations. Some taxa, most notably humans and parrots, retain this neurobehavioral plasticity throughout adulthood. Downregulation of the transcription factor FoxP2 in both songbird and parrot vocal control nuclei has been identified as a key expression pattern facilitating vocal learning. We hypothesize that open-ended vocal learning is vulnerable to cognitive decline, and that this deterioration will be reflected in age-related changes in neural FoxP2 expression. We tested this hypothesis in the budgerigar (Melopsittacus undulatus), a small gregarious parrot in which adults converge on shared call types in response to shifts in group membership. We formed novel flocks of 4 previously unfamiliar males belonging to the same age class, either "young adult" (6 mo-1 yr) or "older adult" (≥ 3 yr), and then collected audio-recordings over a 20-day learning period to assess vocal learning ability. Following behavioral recording, whole brains were extracted and immunohistochemistry was performed to measure FoxP2 protein expression in a parrot vocal learning center, the magnocellular nucleus of the medial striatum (MMSt), and its adjacent striatum. We find similarly downregulated FoxP2 expression and equivalent vocal plasticity and vocal convergence in young and older adults suggesting the maintenance of two components of open-ended learning into old age in the budgerigar. No relationship between individual variation in vocal learning measures and FoxP2 expression was detected.

openmit-licenseApr 2024View details →

eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)

Dataset and results used for the simulations in following publication: Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel: "eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies", Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024   This contains the input (scenario) files for the building & grid scenario and the results of the two simulations. It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library

opencc-by-4.0Oct 2016View details →

Figure 4 in Red-bellied Piranha Pygocentrus nattereri Kner, 1858 (Characiformes: Serrasalmidae) in open waters in R. Macedonia

Figure 4. Some of the osteological characters of P. nattereri from Dojran Lake.

opencc-by-4.0Oct 2016View details →

Figure 1 in Red-bellied Piranha Pygocentrus nattereri Kner, 1858 (Characiformes: Serrasalmidae) in open waters in R. Macedonia

Figure 1. Location map showing the first record of P. nattereri in R. Macedonia.

opencc-by-4.0Oct 2016View details →

Figure 6 in Red-bellied Piranha Pygocentrus nattereri Kner, 1858 (Characiformes: Serrasalmidae) in open waters in R. Macedonia

Figure 6. Light micrograph of the ovaries of a female P. nattereri. H&E. Bar line = 0.1mm.