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13,499 results for “researcher”
Research data supporting: "Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges"
<p>This repository contains the set of modelling data shown in the paper:<strong> "Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges"</strong>, published on Chemical Science (DOI: 10.1039/d3sc03930g)</p>
Human auditory ecology : Extending hearing research to the perception of natural soundscapes by humans in rapidly-changing environments
<p>The audiomaterial corresponding to boreal, tropical and temperate forests, desert, savannah, sub-alpine meadow, and the construction site in New York is copyrighted (license from Wild Sanctuary) and cannot be used without explicit agreement of Bernie Krause. Additional audiomaterial (urban park and street traffic in Paris, France; fast street traffic in Marseille, France; English and French speech material) may only be used with the explicit agreement of the following authors: Jérôme Sueur and Sylvain Haupert (Museum National d'Histoire Naturelle in Paris, France); Sabine Meunier (LMA/CNRS in Marseille, France); Franck Ramus (CNRS in Paris, France) (see Figure legends).</p>
Research Data Alliance Interest Group Professionalising Data Stewardship Career Tracks Survey Dataset
<p>This is the final dataset resulting from the data steward Career Tracks survey that the Reseach Data Alliance (RDA) Interest Group Professionalising Data Stewardship carried out in 2022. Data stewards were defined as professionals who aim at guaranteeing that data is appropriately treated in all stages of the research cycle (i.e., design, collection, processing, analysis, preservation, data sharing and reuse); we invited responses from participants who either now or in the past carried out data stewardship functions, regardless of their job title. The survey asked respondents about their job titles, the organizational context in which they work(ed) including contract types and domains, their educational background, and how they perceive their professional future. </p><p>This dataset publication includes:</p><ol><li>Survey response data in CSV format. The file includes data from 241 respondents who consented to participate in the survey and share the data via a repsoitory, who indicated that they either currently work or have worked in the past in a data stewardship role, and who responded to at least one further question.</li><li>Thematic analysis of the qualitative questions Q11 and Q12 in PDF format.</li></ol>
A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications - Supplementary Data
<p><strong>Videos</strong></p><ul><li><strong>Video 1</strong> A video going through the Z stack in single slices. This is a cross- sectional view of the XRH image stack along the XY plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 2 </strong>A video going through the Y stack in single slices. This is a cross- sectional view of the XRH image stack along the XZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 3 </strong>A video going through the X stack in single slices. This is a cross- sectional view of the XRH image stack along the YZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 4 </strong>3D X-ray histology (XRH) is a µCT -based workflow tailored to fit seamlessly into current histology workflows in biomedical and pre-clinical research, as well as clinical histopathology. Microanatomical detail can be captured from standard (non-stained) formalin-fixed and paraffin-embedded (FFPE) tissue blocks.</li><li><strong>Video 5</strong> Average Intensity Projection (AIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Average Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 6 </strong>Maximum Intensity Projection (MIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Maximum Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 7 </strong>Standard deviation projection of the sample going through the histologically relevant plane. This is a 2D visualisation rendering the Standard Deviation of 20x single XY slices along the z- axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li></ul><p><i>* <strong>Videos 5 -7</strong> are also referred to as "thick-slice rolls" </i>- <i>Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack.</i><br> </p><p><strong>The questionnaire used to collect feedback about the needs of the XRH community.</strong></p><ul><li>Survey.docx</li><li>Survey.pdf</li></ul><p><br><strong>Exemplar report of a semi-automatically generated augmented PDF file</strong> that contain sample information, imaging settings, still images with descriptive figure legends, and links to corresponding online videos</p><ul><li>DEMO02019-FFPE_report_99EbPXG.pdf</li></ul><p> </p><p>= = = = = = = = = = = = = = = = <br><strong>System performance data ZIP</strong><br>= = = = = = = = = = = = = = = = </p><p>This ZIP file contains imaging data collected through different systems and setups at the XRH facility at the μ-VIS X-ray Imaging Centre at the University of Southampton for the purpose of acceptance and/or system performance characterisation. Below is an overview of the folder structure and its contents</p><p>The following files are X-ray imaging data collected on September 28, 2017, using the Med-X system and a Jima phantom at 55 kV peak and 7 Watts. </p><ul><li>20170928_MEDX_1642_JIMA_55kVp7W-2.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif.profile.xml</li></ul><p>This PDF document is related to a QRM MicroCT bar pattern phantom, and its specifications</p><ul><li>QRM-MicroCT-Barpattern-Phantom.pdf</li></ul><p>Graphs showing the calculated focal-spot size as a function of the X-ray power (W) for the Molybdenum rotating target calculated using Edge Modulation function testing. The performance is then compared with the performance of the Reflection target across the same range of powers. Raw data can be found in XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize folder. Test performed in July 2021. </p><ul><li>XRH_202107_MoRot-testing_EdgeModFunction-QRMrecons+RotReflCompar.png</li></ul><p> </p><p><i><strong>/ XRH-XT-H-225-ST_FocalSpots</strong></i><br>This directory contains radiographic data collected using the XRH system with a JIMA phantom and MoRt (Molybdenum rotating), TT (Transmission), and Reflection targets.</p><ul><li>20200113_XRH_Jima test MoRT 55kV 15W.tif, 20200113_XRH_Jima test MoRT 55kV 30W.tif, etc.: <br>These files represent radiographs taken on January 13, 2020, using the XRH system, Jima phantom, MoRT target at 55 kVp and varying wattages.</li><li>20200207_XRH_JIMA 80kV TT1a.tif, 20200207_XRH_JIMA 80kV TT1b.tif, etc.<br>Similar to the above, these files are from February 7, 2020, and use 80 kVp with a TT target.</li><li>20231115_XRH_reflW_80kVp6W.tif, 20231115_XRH_reflW_80kVp6W_02.tif, etc.<br>These files are from November 15, 2023, and collected using the XRH system with a Reflection target at 80 kVp and 6 Watts.</li></ul><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleRadioFromCTs_5umPixelSize</strong></i><br>This directory contains single radiographs taken with a pixel size of 5 micrometers using the Molybdenum rotating (MoRt), and the Reflection target using tungsten (W) and Molybdenum (Mo) metals.</p><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize</strong></i><br>This directory contains sinlge reconstruction slices of the setups mentioned above. Slices are exported from CT volumes and were used for the Edge Modulation function study. </p><p>For interpretation of the filenames in the folders listed above please see below and refer to specific files and folders for detailed information and results related to each imaging session:</p><ul><li><i><xx>kVp or <xx>kV </i>:Imaging at a peak voltage of <xx> kVp.</li><li><i><y>W</i> :Imaging at <y> Watts;<i> </i>"." is represented with "-"; i.e. 20210705_XRH_2766_PJB_TEST03552-EQPMT_W_6-9W is acquired using a power of 6.9 W</li><li><i>MoRt, TT, Refl </i> :Molybdenum, Transmission, and Reflection targets, respectively.</li><li><i>_W_ and _Mo_ </i> :Tungsten and Molybdenum target materials.</li><li><i>_horiz</i> :Reconstruction slices in line with the X-ray beam's propagation direction.</li><li><i>_vert</i> :Reconstruction slices normal to the X-ray beam's propagation direction and parallel to the detector plane.</li></ul>
Improved Converted Traces from Rebasing Microarchitectural Research with Industry Traces
<p>Improved converted traces of the paper "Rebasing Microarchitectural Research with Industry Traces", published at the 2023 IEEE International Symposium on Workload Characterization. It includes the CVP-1 traces used in the paper converted with our improved converter.</p><p><i>Abstract</i>: Microarchitecture research relies on performance models with various degrees of accuracy and speed. In the past few years, one such model, ChampSim, has started to gain significant traction by coupling ease of use with a reasonable level of detail and simulation speed. At the same time, datacenter class workloads, which are not trivial to set up and benchmark, have become easier to study via the release of hundreds of industry traces following the first Championship Value Prediction (CVP-1) in 2018. A tool was quickly created to port the CVP-1 traces to the ChampSim format, which, as a result, have been used in many recent works. We revisit this conversion tool and find that several key aspects of the CVP-1 traces are not preserved by the conversion. We therefore propose an improved converter that addresses most conversion issues as well as patches known limitations of the CVP-1 traces themselves. We evaluate the impact of our changes on two commits of ChampSim, with one used for the first Instruction Championship Prefetching (IPC-1) in 2020. We find that the performance variation stemming from higher accuracy conversion is significant.</p>
Open Research Skills Workshops - GitHub basics
<p>This is the third workshop on GitHub basics<strong> </strong>in a series of workshop about Open Research Skills.</p><p>This workshop covers:</p><p><strong>-</strong> Introduction to Github and its uses</p><p>- Demonstration on using GitHub <strong> </strong></p><p>- Basic repo set up and editing</p><p><strong>List of training workshops in Open Research Skills:</strong></p><ul><li>24th February 2023 - Open access publishing</li><li>24th March 2023 - Using repositories</li><li><strong>21st April 2023 - GitHub basics</strong></li><li>28th April 2023 - GitHub collaborative workflows</li><li>26th May 2023 - Standard vocabularies and ontologies</li><li>30th June 2023 - FAIR data</li></ul><p><strong>Project overview:</strong></p><p>Our project aims to upskill participants in open research skills to increase the quality and reusability of phytolith research and related disciplines such as archaeology, palaeosciences and plant sciences. We will run six hands-on training workshops on open access publishing and research outputs, using repositories, ontologies and standard vocabularies, implementation of FAIR Guidelines for phytolith research, and two workshops on Github basic and advanced skills. The materials from all workshops will be archived as self-study courses on our website (<a href="https://open-phytoliths.netlify.app/">https://open-phytoliths.netlify.app/</a>). We will also provide translation during workshops and training materials into multiple languages. </p><p>This video is a basic course in Github. Github is a tool that is used for research project management and history tracking of your work during projects. It can be used to store and collaborate during projects with data, code and documentation. It covers the basic web interface of Github and how to make repositories, add files and folders. It will also include some examples of uses of Github.</p>
Open Research Skills Workshops - GitHub collaborative workflows
<p>This is the fourth workshop on GitHub collaborative workflows in a series of workshop about Open Research Skills.</p><p>This workshop covers:</p><p>- Introduction to version control</p><p>- How to fork a repository</p><p>- Forking exercises</p><p>- How to work in a team and create and merge branches</p><p>- Branching exercises</p><p><strong>List of training workshops in Open Research Skills:</strong></p><ul><li>24th February 2023 - Open access publishing</li><li>24th March 2023 - Using repositories</li><li>21st April 2023 - GitHub basics</li><li><strong>28th April 2023 - GitHub collaborative workflows</strong></li><li>26th May 2023 - Standard vocabularies and ontologies</li><li>30th June 2023 - FAIR data</li></ul><p><strong>Project overview:</strong></p><p>Our project aims to upskill participants in open research skills to increase the quality and reusability of phytolith research and related disciplines such as archaeology, palaeosciences and plant sciences. We will run six hands-on training workshops on open access publishing and research outputs, using repositories, ontologies and standard vocabularies, implementation of FAIR Guidelines for phytolith research, and two workshops on Github basic and advanced skills. The materials from all workshops will be archived as self-study courses on our website (<a href="https://open-phytoliths.netlify.app/">https://open-phytoliths.netlify.app/</a>). We will also provide translation during workshops and training materials into multiple languages. </p><p>Github is a collaborative, project management tool used to run reproducible research projects with version control. In these videos, you will learn how to use version control, how to branch and fork a repository, how to pull a request and how to collaborate as part of a team on GitHub.</p>
The evolution and future of research on Nature-based Solutions to address societal challenges
<p>This dataset comprises the bibliographic text files used to analyse the Nature-based Solutions research landscape as presented in:</p> <ul> <li>Dunlop, T., Khojasteh, D., Cohen-Shacham, E., Glamore, W., Haghani, M., van den Bosch, M., Rizzi, D., Greve, P., Felder, S. The Evolution and Future of Research on Nature-based Solutions to Address Societal Challenges. <em>Communications Earth & Environment</em>. 2024.</li> </ul> <p>Excel spreadsheets containing data for the Global Water Security Index (Gain et al., 2016) presented in Figure 2 and the data required to reproduce Figures 1 and 2 in the paper above are also shared.</p>
Research Data and Code for "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles"
<p>This dataset documents results and code for the paper "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles" by Stefan Heßbrüggen-Walter, forthcoming in *Synthese*. The data to be processed are contained in four files, derived from a larger dataset related to German dissertations and sourced from the national bibliography of 17th century German prints *VD 17* that will be released at a later date. More information can be found in the file `README.md`. </p>
Water chemistry of LTER-Europe research site Lake Paione Inferiore LTER_EU_IT_088 (1984-2013)
<p>This dataset provides information about water chemical parameters for Lake Paione Inferiore LTER_EU_IT_088: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013.</p> <p>Lake Paione Inferiore (LPI) is a high altitude Alpine lake, located at 2002 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.86 ha and a maximum depth of 13.5 m. The Lake, together with Lake Paione Superiore (LPS), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy.</p> <p>This dataset includes the following files: Metadata LTER_EU_IT_088.xls and per each parameter one xls file with data records. Dataset for water chemistry of LPI for the period 2014-2020 is available at <a href="https://doi.org/10.5281/zenodo.10519349">https://doi.org/10.5281/zenodo.10519349</a></p> <p>Detailed description of the site LPI is available at https://deims.org/c128d2f9-beb0-45ba-89bb-df9e12f95b0f</p>
EnrichKit: a multi-omics tool for livestock research
<p>This is the backend database for the web application EnrichKit.</p> <p>This <a href="../api/records/10257552/draft/files/EnrichKitDB.sqlite/content">EnrichKitDB.sqlite </a>object is created following this repo - https://github.com/liulihe954/EnrichKitDB</p> <p>The main EnrichKit repo can be found there - https://github.com/liulihe954/EnrichKitWeb</p>
WKU experimental epidemic game using research version of Operation Outbreak app
<p>This dataset contains the full list of participants and events in the experimental epidemic game at Wenzhou-Kean University (WKU) in China, run between November 20 and December 4 of 2023 using a customized version of the Operation Outbreak mobile app and cloud backend for research uses. The following blog post provides some more information about this simulation:</p> <p>https://colabobio.medium.com/667295c43907</p> <p> </p>
Small Group and Survey Datasets: "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study"
<p>These datasets accompany the paper "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study" by Chad Curtis PhD and Kaytlin Gomez.</p> <p>The first dataset consists of free responses from n=12 students researchers during a Small Group Metacognitive Practice (SGMP) intervention conducted during the 2023 INBRE Summer Undergraduate Research Fellowship (iSURF) at Nevada State University. The files include:</p> <ul> <li><strong>transcription.docx</strong>: Transcriptions of both individual (n=12) and small group (n=4) responses from the SGMP session.</li> <li><strong>codingR1.xlsx</strong>: Codings used for the thematic analysis, as coded by the primary investigator.</li> <li><strong>codingR2.xlsx: </strong>Codings used for the thematic analysis, as coded by the co-author.</li> <li><strong>KrippendorffAlpha.xlsx</strong>: Inter-coder reliability calculations using Krippendorff's alpha.</li> </ul> <p>The survey data was collected from n=11 participants at the end of the summer research program. The files include:</p> <ul> <li><strong>surveyData.csv</strong>: Anonymized responses to survey questions regarding the SGMP intervention.</li> </ul> <p>This study was approved by the Nevada State University Review Board (Protocol #2305-0346). All procedures in the study were conducted in accordance with approved protocols. Written informed consent was obtained from the subjects for their anonymized information to be published with the article.</p>
(Rawdata) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Rawdata used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
The Cape São Tomé Eddy Transect during the ILHAS 3 Research Cruise
<h2><em>Description</em></h2> <p>During the ILHAS 3 expedition, a strong coherent cyclonic mesoscale meander was detected between 22ºS and 24ºS, about 100 km offshore of the Rio de Janeiro State, Brazil. The feature was surveyed with a hydrographic transect between 09 and 11 December 2019 (end of spring) onboard the University of São Paulo's R/V <em>Alpha Crucis</em>.</p> <p>The transect was oriented in the SW-NE direction, roughly following the 2000 m isobath. The ship moved NE, occupying 19 stations. The oceanographic sampling instruments included: a shipboard Acoustic Doppler Current Profiler (ADCP) RDI-75 kHz; a Profiling Natural Fluorometer System (PNF-300); and a Niskin rosette with a payload consisting of Niskin bottles for water sampling, and a Sea-Bird SBE 9 Conductivity-Temperature-Depth profiler (CTD) carrying sensors for temperature (SBE 3plus), conductivity (SBE4), dissolved oxygen (SBE43), and fluorescence (Turner Cyclops) for Chlorophyll-a. Water samples were collected at standard depths for the determination of nitrate, nitrite, phosphate, and silicate using an AutoAnalyzer-3 SEAL Analytical System. Aliquots of 1 L were retrieved, filtered onto Whatmann GF/F filters, and for later Chlorophyll-a measurements with a Tuner Design AU-10 Fluorometer. Flow cytometry was performed with an Attune Nxt Flow Cytometer for pico- and nano-phytoplankton community analyses. Primary production experiments were performed in the euphotic zone at two stations (in the eddy center and the outermost station).</p> <p> </p> <h3><em>Acknowledgments</em></h3> <p><em>The Brazilian Navy, Petrobras (Petróleo Brasileiro S.A.), and ANP (Brazilian National Agency of Petroleum, Natural Gas and Biofuels) for facilitating the fieldwork under the Cooperation Terms SIGITEC 2018/00451-6 and 2018/00452-2.</em></p> <p><em>Captain José Rezende, commanding officer of the R/V Alpha Crucis, and crew, as well as others of the technical staff, undergraduate and graduate students on board, whose assistance was indispensable for data collection.</em></p>
Survey: Qualitative FGI research on the processing, sourcing, utilisation and management of wood biomass (NCN) DEC-2020/39/I/HS4/03533
<p>The dataset contains the proceedings of a qualitative FGI of representatives of wood biomass processing and harvesting companies. The research was conducted from 5 July 2022 to 7 July 2022 using only the FGI method. The survey was conducted in face-to-face meetings among respondents.<br>The study was funded by National Science Centre in Poland under agreement National Center of Science (NCN) through grant DEC-2020/39/I/HS4/03533</p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 5
<p>Dataset containing four .xlsx and .csv files for the exercises in Episode 5 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
GEDII Wearable Sensors Dataset of 10 Research Teams
<p>The dataset contains Bluetooth (proximity), Infrared (face-to-face), Speech (microphone) and Accelerometer (body activity) data of 10 research teams collected during 5 working days in each team. Altogether N=105 team members. Socio-demographic data as well as round-robin ratings regarding friendship and advice seeking is included. Data was collected using Sociometric badges by Humanyze (formerly Sociometric Solutions).</p> <p>The present dataset has been produced within the context of a EU funded H2020 research project called “Gender-Diversity-Impact: Improving Research and Innovation through Gender Diversity. (GEDII)”. The project has been running from 2015 to 2018 with the aim to develop new tools and methods for doing research on the impact of gender diversity in R&D teams. In order to address these questions, GEDII makes use of a variety of research methods, including a cross country survey, bibliometric & patent analysis and detailed case studies with R&D teams.</p> <p>The dataset is distributed as R package.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.