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617 results for “IR”
IR Lab Cologne/Jena/Kassel Winter Term 2024/2025
<h1>The Datasets for the Information Retrieval Courses in Cologne/Jena/Kassel in Winter Term 2024/2025</h1> <p>This repository contains resources coupled to <a href="https://arxiv.org/pdf/2103.02280.pdf">ir_datasets</a> and <a href="https://webis.de/publications.html?q=tira#froebe_2023e">TIREx</a> for IR courses that focus their hands-on labs on shared tasks. During the <a href="https://tira.io/task-overview/ir-lab-wise-2024">IR exercises in winter term 2023/2024</a>, we collaboratively developed and evaluated IR systems in a shared task style setup, covering corpus creation, system development, and statistical analysis. The resulting artifacts, i.e., the documents, topics, runs, relevance judgments can be browsed at <a href="https://tira.io/task-overview/ir-lab-wise-2024">https://tira.io/task-overview/ir-lab-wise-2024</a>. This zenodo artifact contains all of the underlying datasets used and produced during the course together with instructions on how to easily access the data using ir_datasets.</p> <p> </p> <p>The artifact in this dataset include the following files:</p> <ul> <li>subsampled-ms-marco-deep-learning-20241201-training-inputs.zip containing the training inputs, i.e., containing the document corpus and the topics.</li> <li>subsampled-ms-marco-deep-learning-20241201-training-truths.zip containing the training truth to evaluate and tune systems, i.e., the topics and relevance judgments.</li> </ul> <h2>Accessing the Data with ir_datasets</h2> <p>We provide wrapper code to easily access the resources with ir_datasets:</p> <pre><code># this loads a patched version of ir_datasets that can load resources from TIRA from tira.third_party_integrations import ir_datasets training_dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')</code></pre> <p>Similarly, the same is possible with the ir_datasets integration to PyTerrier:</p> <pre><code>from tira.third_party_integrations import ensure_pyterrier_is_loaded import pyterrier as pt # this patches ir_datasets and loads PyTerrier so that it can load resources from TIRA and can run in the TIRA sandbox ensure_pyterrier_is_loaded() training_dataset = pt.datasets.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')</code></pre> <p> </p>
Open data for publication Reconstruction of Ice Surface upon Acetone Adsorption: An in-situ ATR-IR Modulation Excitation Spectroscopy Study
<p>Original data for publication "Reconstruction of Ice Surface upon Acetone Adsorption: An in-situ ATR-IR Modulation Excitation Spectroscopy Study". Original data used for the Figures.</p>
Dataset of Tropical Cyclone IR-to-Rainfall Prediction (TCIRRP)
<p>Dataset of Tropical Cyclone IR-to-Rainfall Prediction (TCIRRP)</p>
Characterisation of Laser Wakefield Acceleration Efficiency with Octave Spanning Near-IR Spectrum Measurements
<p>The dataset and analysis codes for the publication "Characterisation of Laser Wakefield Acceleration Efficiency with Octave Spanning Near-IR Spectrum Measurements".</p> <p>Analysis code is stored as jupyter notebooks.</p>
Driving Traffic to Institutional Repositories: How Search Engine Optimization can Increase the Number of Downloads from IR
<p>The success of institutional repositories (IR) is measured in large part by the number of file downloads they sustain. Most traffic to IR is referred by Internet search engines, but referrals are hindered when IR are not properly optimized for search engine harvesting and indexing, leading to low visitation and downloads. This presentation will discuss search engine optimization techniques, especially for Google Scholar, which can be responsible for the majority of referrals that result in IR file downloads. The presentation will also introduce a new web service called RAMP (Repository Analytics & Metrics Portal) that accurately counts file downloads from IR and requires no installation or training.</p>
CARL-COAR Joint Webinar on IR Usage Statistics
<p>Institutional repositories (IRs), by virtue of their ability to give increased visibility to the institution’s scholarly outputs, are valued for their vast amount of open scholarly content. Libraries wishing to demonstrate use (and value) frequently report the number of file downloads sustained by their IR. However, commonly used analytics tools are unsuited for this purpose and produce results that dramatically under-count or over-count file downloads. As well, although statistics can sometimes be accessed through the various repository interfaces, without an agreed standard it is impossible to reliably assess and compare usage data across different IRs in any meaningful way.</p> <p>The first part of this webinar will explain the reasons for the inaccuracies in most IR download counts and will introduce a new web service called Repository Analytics and Metrics Portal (RAMP), which provides much more accurate counts of file downloads to IR managers, with almost no installation or training requirements. Aggregated data collected with RAMP also creates the potential for interesting new streams of research about IR. RAMP was developed with funding from the Institute of Museum and Library Services.</p> <p>The second half of this webinar will focus on another approach at standardizing institutional research data download statistics: IRUS-UK, a national aggregation service, which contains details of all content downloaded from participating IRs in the UK. By collecting raw usage data and processing them into item-level usage statistics, following rules specified by COUNTER, IRUS-UK provides comparable and authoritative standards-based data and also acts as an intermediary between UK repositories and other agencies.</p>
Laser welding IR data for ML
<p> </p> <p>HDF5 datasets of laser welding processes using different laser power and robot speed configurations.</p> <p>- excel sheet protocol </p> <p>- 500Hz aquisition rate</p> <p>- coaxial view</p> <p>- wavelength: 1750 +-250</p> <p> </p>
Raw data: The Chemical and Electronic Properties of Stability-Enhanced, Mixed Ir-TiOx Oxygen Evolution Reaction Catalysts
<p>Raw data for the publication:</p> <h4><em>The Chemical and Electronic Properties of Stability-Enhanced, Mixed Ir-TiO<sub>x</sub> Oxygen Evolution Reaction Catalysts</em></h4> <div>Marianne van der Merwe, Raul Garcia-Diez, Leopold Lahn, R. Enggar Wibowo, Johannes Frisch, Mihaela Gorgoi, Wanli Yang, Shigenori Ueda, Regan G. Wilks, Olga Kasian, and Marcus Bär</div> <div>ACS Catalysis <strong>2023</strong> <em>13</em> (23), 15427-15438</div> <p>DOI: 10.1021/acscatal.3c02948</p> <p><strong>Copyright © 2023 The Authors. Published by American Chemical Society</strong>. This publication is licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a>.</p>
Raw data: Electronic and Structural Property Comparison of a Novel vs. a Commercial Iridium-based OER Catalysts Enabled by Operando Ir L3-edge X-ray Absorption Spectroscopy
<p>Raw data for the manuscript titled:</p> <p><strong>Electronic and Structural Property Comparison of a Novel vs. a Commercial Iridium-based OER Catalysts Enabled by <em>Operando </em>Ir L3-edge X-ray Absorption Spectroscopy</strong></p> <p> </p>
IR data for the compounds published in "Dioxygen Activation by a Bioinspired Tungsten(IV) Complex"
Open the record for dataset details and reuse information.
IR and GC-MS data for catalytic studies in "Perchlorate reduction catalyzed by dioxidomolybdenum(VI) complexes: Effect of ligand substituents"
Open the record for dataset details and reuse information.
IR data of the compounds published in "Replacement of Molybdenum by Tungsten in a Biomimetic Complex Leads to an Increase in Oxygen Atom Transfer Catalytic Activity"
Open the record for dataset details and reuse information.
IR data of the compounds published in "The Effect of Pyridine-2-thiolate Ligands on the Reactivity of Tungsten Complexes toward Oxidation and Acetylene Insertion"
Open the record for dataset details and reuse information.
Raw data: Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides
<p>Raw data for the manuscript titled:</p> <p><strong>Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides</strong></p> <p> </p>
Low Ti Additions to Stabilize Ru‐Ir Electrocatalysts for the Oxygen Evolution Reaction
<p>Abstract:</p> <p>Anodic oxygen evolution reaction (OER) challenges large scale application of proton exchange membrane water electrolyzers (PEMWE) due to sluggish kinetics, high overpotential and extremely corrosive environment. While Ir oxides currently provide the best balance between activity and stability, the scarcity of Ir and corresponding high market price lead to poor cost-benefit factors. Mixing Ir with more stable non-precious Ti reduces the noble metal loading and may implicate stabilization, while addition of more catalytically active Ru ensures a high reaction rate. Here, we examine the activity-stability behavior of Ru-Ir-Ti thin film material libraries with low Ti content under the OER conditions. The high sensitivity to the dissolution of the individual alloy components was achieved by using online and offline inductively coupled plasma mass<br>spectrometry (ICP-MS) analysis. Our data reveal that even low Ti additions improve the stability of Ru-Ir catalysts without sacrificing activity. In particular, 5 at. % of Ti enable stability increase of Ir in the Ru-Ir catalyst by a factor of 3. Moreover, this catalyst exhibits higher activity compared to the Ti-free Ru-Ir alloys with similar Ir content. Observed activity-stability trends are discussed in light of X-ray photoelectron spectroscopy data.</p>
TAU Moving Sound Events 2019 - Ambisonic, Anechoic, Synthetic IR and Moving Source Dataset
<p><strong>Tampere University (TAU) Moving Sound Events 2019 - Ambisonic, Anechoic and Synthetic Impulse Response (IR) and Moving Source Dataset</strong></p> <p>This dataset consists of simulated anechoic first order Ambisonic (FOA) format recordings with moving point sources each in 2D spherical space represented with azimuth and elevation angles. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), starting spatial location and directional spatial location in azimuth and elevation angles (in degrees), angular velocity of motion, and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the DCASE 2016 task 2 dataset. This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. Every event is assigned a spatial trajectory on an arc with a constant distance from the microphone (in the range 1-10 m) and moving with a constant angular velocity for its duration. Due to the choice of the ambisonic spatial recording format, the steering vectors for a plane wave source or point source in the far field are frequency-independent. Hence, there is no need for a time-variant convolution or impulse response interpolation scheme as the source is moving; the spatial encoding of the monophonic signal was done sample-by-sample using instantaneous ambisonic encoding vectors for the respective DOA of the moving source. The synthesized trajectories in the dataset vary in both azimuth and elevation and are simulated to have a constant angular velocity in the range [-90, 90]/s with 10-degree/s steps.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Localization, Detection and Tracking of Multiple Moving Sound Sources with Convolutional Recurrent Neural Networks'</a> work.</p>
text-fig. 39. Theropod pelves in left lateral view, illustrating several pelvic characters, a, Herrerasaurus ischigualastensis; redrawn (reversed) from Novas (1993). B, Syntarsus rhodesiensis; based on QG 1, QG 691, and Raath (1990). c, Allosaurus fragilis; modified from Molnar et al. (1990). D, generalized tyrannosaurid; based on Osborn (1916) and ROM 807. E, Segnosaurus galbinensis', redrawn from Barsbold and Maryanska (1990). F, Rahonavis ostromi', based on UA 8656. Abbreviations: ac, acetabulum; bs, brevis shelf; il, ilium; ir, iliac ridge; is, ischium; of, obturator foramen; op, obturator process; pf, pubic fenestra; pip, posterior ischial process; pu, pubis. Scale bars represent 50 mm (a-b), 100 mm (c-e), and 10 mm (f). in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 39. Theropod pelves in left lateral view, illustrating several pelvic characters, a, Herrerasaurus ischigualastensis; redrawn (reversed) from Novas (1993). B, Syntarsus rhodesiensis; based on QG 1, QG 691, and Raath (1990). c, Allosaurus fragilis; modified from Molnar et al. (1990). D, generalized tyrannosaurid; based on Osborn (1916) and ROM 807. E, Segnosaurus galbinensis', redrawn from Barsbold and Maryanska (1990). F, Rahonavis ostromi', based on UA 8656. Abbreviations: ac, acetabulum; bs, brevis shelf; il, ilium; ir, iliac ridge; is, ischium; of, obturator foramen; op, obturator process; pf, pubic fenestra; pip, posterior ischial process; pu, pubis. Scale bars represent 50 mm (a-b), 100 mm (c-e), and 10 mm (f).
IR Intensity Carrying Modes of 1S-Fenchone, 1S-Camphor, 1S-Methylenefenchone, 1S-Methylenecamphor
<p>Animation of the collective displacements associated to the IR intensity carrying modes of the title molecules. This file is associated to the paper "Evaluation of molecular polarizability and of intensity carrying modes contributions in circular dichroism spectroscopies".</p>
RAW DATA: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent Thin Layer Electrochemical Cell
<p><span>Data behind the figures in the Manuscript: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent </span><span>Thin Layer </span><span>Electrochemical Cell submitted to J. Phys. Chem. </span></p>
A visible-light promoted amine oxidation catalyzed by a Cp*Ir complex
<p>Data underlying the figures in the publication “A visible-light promoted amine oxidation catalyzed by a Cp*Ir complex”, published in <em>ChemCatChem,</em> <strong>2020</strong>, 12, 4512–4516. <a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cctc.202000488">https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cctc.202000488</a></p> <p>Table of contents:</p> <p><strong>1. Scheme 2d</strong>; Excel file with the numerical values of fluorescent intensities in <em>Scheme 2d</em> (raw data).</p> <p><strong>2. Scheme 3b</strong>; Excel file with the numerical values of time-course evolution of compound <strong>22</strong>, <strong>23</strong>, and <strong>24</strong> in <em>Scheme 3b</em>.</p> <p><strong>3. Scheme 3c</strong>; Excel file with the numerical values of time-course H<sub>2</sub>O<sub>2</sub> evolution in <em>Scheme 3c</em> (calculated based on UV-vis absorption by using a calibration curve).</p> <p><strong>4. Scheme 4a</strong>; Excel file with the numerical values of UV-vis spectra in <em>Scheme 4a</em> (raw data).</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.