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188 results for “Items”
textual item similarty (video games and wines datasets)
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Estimasi Tingkat Kesukaran Butir Antara Item Response Theory Dan Rasch Model
<p><span>Penelitian ini menggunakan pendekatan deskriptif dengan tujuan untuk menyajikan hasil analisis butir soal satu parameter logistik (1-PL) menggunakan dua pendekatan yaitu pendekatan Item Respons Thoery dan Rasch model. Analisis pendekatan model IRT menggunakan bantuan software BILOG_MG dan Model Rasch menggunakan bantuan software Winsteps. Analaisis satu parameter yang dimaksud adalah tingkat kesukaran butir soal. Subjek dalam penelitian penelititan ini adalah peserta didik yang mengikuti ujian akhir mata pelajaran matematika kelas XII. Jumlah sampel dalam penelitian sebanyak 29 peserta. Pengumpulan data menggunakan pendekatan<span> </span>dokumentasi. Data diperoleh melalui google form yang berisi lembar jawaban dan kunci jawaban.</span><span> </span><span>Hasil penelitian mmenunjukan bahwa terdapat angka tingkat kesukaran butir soal pada model IRT lebih besar dari angka tingkat kesukaran butir<span> </span>pada model rasch. Sebanyak 35 butir soal yang dianalisis memiliki selisih yang berbeda. Selisih yang paling kecil terdapat pada butir soal nomor 3 sebesar 0,01. Indeks kesukaran pada model IRT sebesar 1,27 dan indeks kesukaran pada model Rasch sebesar 1,26. Selisih terbesar terdapat pada butir soal nomor 12 sebesar 1,25. Indeks kesukaran pada model IRT sebesar -0,29 dan indeks kesukaran pada model Rasch sebesar -1,54. Angka tingkat kesukaran butir soal semakin kecil maka selisih tingkat kesukaran IRT dan Rasch makin besar. Sebaliknya, Angka tingkat kesukaran butir soal semakin besar maka selisih tingkat kesukaran IRT dan Rasch makin kecil. Meskipun begitu, IRT dan Rasch memiliki kesamaanapabila butir soal diurutkan mulai dari angka terkecil hingga angka yang terbesar. Kedua model tersebut memiliki urutan soal yang sama yaitu terkecil mulai dari butir soal nomor 12 dan yang terbesar yaitu butir soal nomor 3.</span></p>
Implementation and Results of a Randomized Local Search on the 2D Rectangular Bin Packing Problem with Item Rotation using Different Objective Functions
<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) applied to the two-dimensional bin packing problem without orientation (where items can be rotated by 90 degrees). As benchmark dataset, we use the <code>beng</code>, <code>A</code>, and <code>class</code> instances from <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> as well as the four non-trivial Almost Squares in Almost Squares (<a href="https://math.vu.nl/~sbhulai/publications/data_analytics2016b.pdf"><code>Asqas</code></a>) instances</p> <p>These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Rui Zhao, Tianyu Liang, Zhize Wu, Daan van den Berg, Matthias Thürer, and Thomas Weise. 2024. Randomized Local Search on the 2D Rectangular Bin Packing Problem with Item Rotation. In <em>Genetic and Evolutionary Computation Conference (GECCO'24 Companion),</em> July 14–18, 2024, Melbourne, VIC, Australia. ACM, New York, NY, USA, 4 pages. doi:<a href="https://doi.org/10.1145/3638530.3654139">10.1145/3638530.3654139</a>.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> version 0.8.34 and <a href="https://thomasweise.github.io/moptipy">moptipy</a> version 0.9.98, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>data</code> is the directory with the results and their evaluation.</li> <li><code>data/results</code> is the directory with the log files generated by the experiment. In this folder, there are two sub-folders, <code>ibf1</code> and <code>ibf2</code>. We tested two different encodings, but found that the second one (<code>ibf2</code>) is too slow to do meaningful experiments. Thus, the experiments with it were abandoned and only one objective function was tested. We include <code>ibf2</code> for the sake of completeness, whereas <code>ibf1</code> was used in our paper. Either way, both <code>ibfX</code> folders contain one directory for each objective function applied to them. In each such directory, there is one folder (for the single algorithm applied) and this folder, in return, contains one folder per benchmark instance. The benchmark instance folders contain the three log files of the three runs that we applied to each instance/algorithm/objective combination.<br>Each log file contains information of one run, i.e., one execution of one algorithm on one problem instance. All improving moves of a run as well as the final solution are stored in the log file.</li> <li><code>data/evaluator</code> is the folder containing Python scripts that were used to evaluate these results. Two scripts are provided: <code>evaluator_short.py</code> was used for generating the tables used in the final paper version. <code>evaluator_full.py</code> provides larger tables, which could not be included in the final paper due to space reasons.</li> <li>Folder <code>evaluation_full</code> was generated using <code>evaluator_full.py</code> and contains tables and figures and a result summary in CSV format.</li> <li>Folder <code>evaluation_short</code> was generated using <code>evaluator_short.py</code> and contains both the tables used in the paper as well as a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> and other benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Rui ZHAO (赵睿) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:zr1329142665@163.com">zr1329142665@163.com</a>.</p>
Warehouse Item Picking Optimization
<h3>Dataset Description</h3> <p>This dataset contains four files that provide data related to warehouse orders and address reassignment optimization. The files are as follows:</p> <ol> <li><strong><code>orders.csv</code></strong>:<br>This file includes orders over a period of three months (Date). Each order is represented by an order number (Order_num) and contains multiple items identified by Product_ID. Each item is associated with an address (Address), which represents its location in the warehouse.</li> <li> <p><strong><code>addresses_avg.csv</code></strong>:<br>This file lists all addresses (Address) in the warehouse along with the average daily number of accesses to each address (product_count). An address in the warehouse is specified by a combination of an aisle number and a position number within the aisle.</p> </li> <li> <p><strong><code>warehouse_map.ods</code></strong>:<br>This file provides a schematic layout of the warehouse, illustrating the arrangement of aisles and positions.</p> </li> <li> <p><strong><code>reassignment.csv</code></strong>:<br>This file contains the results of an optimization algorithm, showing the new assignment of addresses in the warehouse.</p> </li> </ol>
Noncanonical Pronominal Items in Vietnamese and Chinese: Imposters or True Pronouns?
<p>In Vietnamese and Chinese, pronominal items are not only restricted to textbook ‘pronouns’ but include many other pseudo-forms such as kin terms, person names, as well as other non-canonical items that are hitherto undescribed. In this talk, we observe these non-canonical items in more detail and present a comparative analysis. We then refine the crosslinguistic pronominal item taxonomy on that basis and propose incorporating these alternative pronominal items into a unified syntactic theory.</p>
Sport im Lebensraum Schule (SLS): Dokumentation der Items und Skalen 2022
<p>In der vorliegenden Dokumentation sind die Skalen und Items zusammengestellt, welche in der vom Bundesamt für Sport (BASPO) geförderten Studie «Sport im Lebensraum Schule (SLS)» (01.09.2019–31.12.2021) verwendet wurden. Das Ziel dieser Studie, welche gemeinsam mit dem Sportamt der Stadt Zürich durchgeführt wird, ist, die Tagesschulen bei der Entwicklung, Implementierung und Evaluation von sportorientierten ausserunterrichtlichen Angeboten, die auf die Bedürfnisse der Schülerinnen und Schüler zugeschnitten sind, zu unterstützen. Zudem soll ein adäquates Aus- und Weiterbildungsangebot für Lehr- und Betreuungspersonen erarbeitet werden. Zu diesem Zweck werden an mehreren Schulen in der Stadt Zürich, die sich in der Umgestaltung zur Tagesschule befinden, Schulleitungen, Lehr- und Betreuungspersonen sowie die Kinder befragt. Die Dokumentation der Skalen und Items umfasst die Variablennamen, den zugehörigen Fragetext und die Kodierung, sowie Quellen zu den einzelnen Fragen bzw. Items. Zudem sind zum Teil Skalen- bzw. Itemkennwerte aufgeführt. Diese Skalendokumentation beinhaltet die Skalen und Items des im Sommer 2020 eingesetzten Befragungsinstruments. Zu einem zweiten Zeitpunkt soll eine weitere Befragung stattfinden, bei welcher eine angepasste Version dieser Dokumentation eingesetzt wird.</p> <p><em>→ Die Skalen und Items von 2020 sind <a href="https://doi.org/10.5281/zenodo.4302180">hier </a>zugänglich</em></p>
Replication package for "Finding the Wise and the Wisdom in a Crowd: Estimating Underlying Qualities of Reviewers and Items"
<p>Replication Package for the article "Finding the Wise and the Wisdom in a Crowd: Estimating Underlying Qualities of Reviewers and Items" by Nicolas Carayol and Matthew O. Jackson, to be published in the Economic Journal</p>
Four psychometrically validated datasets for benchmarking large language models, based on the TIMSS 2008 and 2011 released items.
<p>Four datasets validated according to psychometric principles that can be used to benchmark large language models in terms of achievements in advanced school math, advanced school physics, 8th grade math and 8th grade science.</p> <p>These four datasets are derived from items released by Trends in International Mathematics and Science Study Advanced 2008 and Trends in International Mathematics and Science Study 2011. See <a href="https://nces.ed.gov/timss/released-questions.asp">link</a>.</p> <p>For more information, see our paper <a href="https://arxiv.org/abs/2404.01799">PATCH! Psychometrics-AssisTed benCHmarking of Large Language Models: A Case Study of Mathematics Proficiency</a>.</p>
APPENDIX A: Measurement items and reliabilities
<p>Understanding the Adoption of Quantified Self-Tracking Wearable Devices in the Organization<br> Environment: An Empirical Case Study<br> APPENDIX A: Measurement items and reliabilities</p>
APPENDIX A: Measurement items and reliabilities
<p><strong>Understanding the Adoption of Quantified Self-Tracking Wearable Devices in the Organization Environment: An Empirical Case Study</strong></p> <p><strong>APPENDIX A:</strong> <strong>Measurement items and reliabilities</strong></p>
Webis Known-Item Question Corpus 2013 (Webis-KIQC-13)
<p>The Webis Known-Item Question Corpus 2013 (Webis-KIQC-13) contains annotations for 2,755 questions posted on Yahoo! Answers. For each question, 2 annotators were asked to categorize the question as having a known-item information need or not, to identify a ClueWeb09 website representing the known item, and whether false memories are contained in the description of the need. The corpus represents the decisions of the annotators who had discussions for the few questions on which they did not agree initially.</p> <p>The corpus contains the IDs of the ClueWeb09 documents representing the known item and an annotated categorization and correction for questions with a false memory.</p>
rho 0.5, b 1, T 100, 10 items
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Dangerous Items Dataset for 5-Class Object Detection (Pascal VOC annotation)
<p>This repository contains the data from the manuscript "Dangerous Items Detection in Surveillance Camera Images Using Faster R-CNN". It contains 4,000 images representing 5 classes of objects: baseball bat, gun, knife, machete and rifle (<a href="https://drive.google.com/file/d/1aG30Hpupctnp8ywhAprz313jmYbFnZk3/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1aG30Hpupctnp8ywhAprz313jmYbFnZk3/view?usp=drive_link</a>). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger one is no longer than 1,000 pixels. The full set was randomly divided into training and testing parts (75% and 25% of the full set respectively). As a result, the training part contains 3,000 images, and the testing part – 1,000 images. Both parts are balanced, that is, they contain similar number of objects to be detected. Images were annotated using bounding boxes in the Pascal VOC format, according to which the description about each image is included in the corresponding XML file.</p> <p>Using this dataset please cite:<br>Omiotek, Z. (2025). Dangerous items’ detection in surveillance camera images using Faster R‑CNN. Przegląd Elektrotechniczny, 101(5), 156-168. https://www.red.pe.org.pl/articles/2025/5/36.pdf</p>
Enhanced Accounting for Item Cost Variability in AASHTOWare Project Software Dataset
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Fig. 2 in A Report On Optimal pricing and replenishment strategies for non-instantaneous decaying items with selling price and current stock level dependent demand
Fig. 2. Experimental results of Callosobruchus analis parental crosses. A) Hatch rate, B) Apterous offspring ratio, C) Offspring sex ratio. Replicates were eight for ap/ap females mated with ap/ap, +/ap, and +/+ males, eight for +/+ females with +/+ males, seven for +/ap females mated with ap/ap, +/ap males, and 11 for +/+ females mated with ap/ap males.
Identifying the core components and items to measure health workers' cultural competence in the Ethiopian context
<p>Abstract</p> <p>Background: Cultural competence (CC) is a crucial attribute in attaining quality healthcare service outcomes, mitigating malfunctioning practices, and improving patients’ satisfaction. Studies suggested comprehensive CC assessment requires appraising existing CC tools to measure health workers’ CC in the Ethiopian context. Selecting existing CC tools, identifying sub-constructs, pinpointing demographic characteristics, and evaluating items are the study objectives.</p> <p>Methods: 20 cultural competence tools of 20 to 83 items, 1-5 sub-constructs, and 4–10 Likert-Type rating options were identified, to be rated by eight experts in three groups. Consensus based Standards for the selection of health Measurement INstruments (COSMIN) and test construction literature are used to develop raters rating codes to compute Inter-Raters Reliability (IRR). The first group of three experts was rated to decide the inclusion of CC tools, factors, and demographic information. The second group of three experts selected six CC tools and 65 items. Two experts in the third group further evaluated the selected items.</p> <p>Results: Reliability for the inclusion of CC tools, factors, and demographic variables were found to be 75%–87%, 50%–93%, and 50%–86% respectively. 13 items that violate test construction principles like absoluteness, endorsement, proneness to multiple interpretation, ambiguity, and double barring were excluded. Cultural skill, cultural knowledge, and cultural awareness are the three most common sub-constructs. Cultural sensitivity, cultural desire, and cultural encounters are the least incorporated factors. Gender, age, language, department, patients encounter, education, and cultural diversity training are demographic variables to assess health workers' CC.</p> <p>Conclusions: Items of CC instruments cover cognitive, affective, and skill domains as prominent dimensions of CC tools. The reviewed tools consist of items that violate test construction principles. To conduct CC assessment in the Ethiopian context, apart from refining and validating selected items, generating additional items is recommended.</p>
WIA-SZZ: Work Item Aware SZZ - Cloned Repository Files (6/7)
<p>For replication purposes, these 7 Zenodo records contain a copy of the cloned repositories from Rosa et. al, ICSE 2021, <a href="https://github.com/grosa1/icse2021-szz-replication-package">Evaluating SZZ implementations through a developer-informed oracle</a>, downloaded as of to Dec. 2022. These repositories were used for the evaluation and analysis of our paper WIA-SZZ: Work Item Aware SZZ. A script to download and unzip all files in these Zenodo records is provided as well.</p> <p>1. https://zenodo.org/record/8285106 (doi: 10.5281/zenodo.8285106)<br> 2. https://zenodo.org/record/8285119 (doi: 10.5281/zenodo.8285119)<br> 3. https://zenodo.org/record/8285121 (doi: 10.5281/zenodo.8285121)<br> 4. https://zenodo.org/record/8285125 (doi: 10.5281/zenodo.8285125)<br> 5. https://zenodo.org/record/8285127 (doi: 10.5281/zenodo.8285127)<br> 6. https://zenodo.org/record/8285138 (doi: 10.5281/zenodo.8285138)<br> 7. https://zenodo.org/record/8285140 (doi: 10.5281/zenodo.8285140)</p>
WIA-SZZ: Work Item Aware SZZ - Cloned Repository Files (7/7)
<p>For replication purposes, these 7 Zenodo records contain a copy of the cloned repositories from Rosa et. al, ICSE 2021, <a href="https://github.com/grosa1/icse2021-szz-replication-package">Evaluating SZZ implementations through a developer-informed oracle</a>, downloaded as of to Dec. 2022. These repositories were used for the evaluation and analysis of our paper WIA-SZZ: Work Item Aware SZZ. A script to download and unzip all files in these Zenodo records is provided as well.</p> <p>1. https://zenodo.org/record/8285106 (doi: 10.5281/zenodo.8285106)<br> 2. https://zenodo.org/record/8285119 (doi: 10.5281/zenodo.8285119)<br> 3. https://zenodo.org/record/8285121 (doi: 10.5281/zenodo.8285121)<br> 4. https://zenodo.org/record/8285125 (doi: 10.5281/zenodo.8285125)<br> 5. https://zenodo.org/record/8285127 (doi: 10.5281/zenodo.8285127)<br> 6. https://zenodo.org/record/8285138 (doi: 10.5281/zenodo.8285138)<br> 7. https://zenodo.org/record/8285140 (doi: 10.5281/zenodo.8285140)</p>
WIA-SZZ: Work Item Aware SZZ - Cloned Repository Files (1/7)
<p>For replication purposes, a copy of the cloned repositories from Rosa et. al, ICSE 2021, <a href="https://github.com/grosa1/icse2021-szz-replication-package">Evaluating SZZ implementations through a developer-informed oracle</a> (downloaded as of to Dec. 2022), used in our evaluation and analysis for our WIA-SZZ: Work Item Aware SZZ paper, is provided through 7 Zenodo links. A script to download and unzip them together is provided as well.</p> <p>1. https://zenodo.org/deposit/8285106 doi: 10.5281/zenodo.8285106<br> 2. https://zenodo.org/deposit/8285119 doi: 10.5281/zenodo.8285119<br> 3. https://zenodo.org/deposit/8285121 doi: 10.5281/zenodo.8285121<br> 4. https://zenodo.org/deposit/8285125 doi: 10.5281/zenodo.8285125<br> 5. https://zenodo.org/deposit/8285127 doi: 10.5281/zenodo.8285127<br> 6. https://zenodo.org/deposit/8285138 doi: 10.5281/zenodo.8285138<br> 7. https://zenodo.org/deposit/8285140 doi: 10.5281/zenodo.8285140</p>
WIA-SZZ: Work Item Aware SZZ - Cloned Repository Files (2/7)
<p>For replication purposes, these 7 Zenodo records contain a copy of the cloned repositories from Rosa et. al, ICSE 2021, <a href="https://github.com/grosa1/icse2021-szz-replication-package">Evaluating SZZ implementations through a developer-informed oracle</a>, downloaded as of to Dec. 2022. These repositories were used for the evaluation and analysis of our paper WIA-SZZ: Work Item Aware SZZ. A script to download and unzip all files in these Zenodo records is provided as well.</p> <p>1. https://zenodo.org/record/8285106 (doi: 10.5281/zenodo.8285106)<br> 2. https://zenodo.org/record/8285119 (doi: 10.5281/zenodo.8285119)<br> 3. https://zenodo.org/record/8285121 (doi: 10.5281/zenodo.8285121)<br> 4. https://zenodo.org/record/8285125 (doi: 10.5281/zenodo.8285125)<br> 5. https://zenodo.org/record/8285127 (doi: 10.5281/zenodo.8285127)<br> 6. https://zenodo.org/record/8285138 (doi: 10.5281/zenodo.8285138)<br> 7. https://zenodo.org/record/8285140 (doi: 10.5281/zenodo.8285140)</p>
ScienceDex guides
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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.