This study employed an extensive literature review to identify commonalities in the data retrieval practices of users of observational data. This dataset consists of a BibTeX file with the 146 bibliographic references examined in:Gregory, K., Groth, P., Cousijn, H., Scharnhorst, A., & Wyatt, S. (2017). Searching Data: A Review of Observational Data Retrieval Practices. arxiv:1707.06937. [cs.DL]The body of literature in the dataset was retrieved using different combinations of keyword searches, primarily in the Scopus database, across all fields. Keyword searches related to information retrieval (e.g. user behavior, information seeking, information retrieval) and data practices (e.g. research practices, community practices, data sharing, data reuse) were combined with keyword searches for research data. As the terms “data” and “search” are ubiquitous in academic literature, title searches also were employed and combined with the controlled vocabulary of the database to locate relevant information. Searches in Scopus included strings such as:KEY ( user AND information ) AND TITLE-ABS-KEY ("research data" OR ( scien* W/1 data ) OR ( data W/1 ( repositor* OR archive* ) ) )TITLE ( data W/0 ( search OR retriev* OR discover* OR access* OR sharing OR reus* ) )AND ( LIMIT-TO ( EXACTKEYWORD , "Information Retrieval" ) OR LIMIT-TO ( EXACTKEYWORD , "Data Retrieval" ) OR LIMIT-TO ( EXACTKEYWORD , "Data Reuse" ) )Bibliometric techniques such as citation chaining and related records were also applied. Pertinent journals and conference proceedings not indexed within Scopus (e.g. the International Journal of Digital Curation) were searched directly using similar keywords.The approximately 400 retrieved documents were examined by close reading to identify articles referring to observational data for inclusion in the final dataset.AcknowledgementsThis work has funded by the NWO Grant 652.001.002 (programme Creative Industrie - Thematisch Onderzoek (CI-TO), Re-SEARCH: Contextual Search for Scientific Research Data)

This dataset contains the data used in the master's thesis with the above title. It consists of a BibTeX file with the bibliographic metadata of the publications and websites cited throughout the thesis, and a Markdown file with statistics of the data sources discussed in Chapter 4.

arguana-c-256-24-gpt-4o-2024-05-13-51550 Dataset

  Dataset Description

The dataset "academic research data retrieval" is a generated dataset designed to support the development of domain specific embedding models for retrieval tasks.

  Associated Model

This dataset was used to train the arguana-c-256-24-gpt-4o-2024-05-13-51550 model.

  How to Use

To use this dataset for model training or evaluation, you can load it using the Hugging Face datasets library… See the full description on the dataset page: https://huggingface.co/datasets/fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-51550.

Relevancy ranking is an important component of making a data repository's search system responsive to data seekers’ needs. The Research Data Alliance (RDA) Data Discovery Paradigms Interest Group (https://www.rd-alliance.org/groups/data-discovery-paradigms-ig) is a collaborative activity within our data community which aims to improve data searchability. This survey is intended to gather information about the current practices and lessons learnt by data repositories in implementing relevancy ranking in search systems. We expect that analysis of the survey results will:

* Help data repositories choose appropriate technologies when implementing or improving their search functionality;
* Provide a means for sharing experiences in improving relevancy ranking;
* Capture the aspirations, successes and challenges encountered from research data repository managers;
* Help the Data Discovery Paradigms Interest group align future activities on data search improvement with the interests of data search service providers.

For the above the purpose, we designed a survey instrument to answer the following topics (the numbers in brackets indicate the number of questions asked per topic):

* What are characteristics of each repositories (5)?
* What are system configurations (e.g., ranking model, index methods, query methods) (7)?
* Evaluation methods and benchmark (10)
  ** What has been evaluated?
  ** What evaluation methods have been applied?
  ** How was the evaluation collection built?
  ** What is approximate performance range of search systems with certain configuration?
* What methods have been used to boost searchability to web search engines (e.g., Google, Bing) (2)
* What other technologies or system configurations have been employed (5)?
* Wish list for future activities for the RDA relevance task force (2)?

This collection consists of survey instrument, survey responses and survey report.

This dataset is part of Project MILDRED, Development Project of Research Data Infrastructure at University of Helsinki. The project started on April 29, 2016. Project aim is to provide University of Helsinki with state-of-the-art research data management service infrastructure. To gain knowledge about researchers' data storage and preservation practices in 2016, an e-survey was sent to the UH research staff about 1) what data repositories they use for depositing their research data; 2) what reasons they had for not depositing data and 3) what alternative storage devices and repository services they used for their data.The dataset consists of e-survey report master file and analysis of the original master file. The files have been anonymized. A readme.rtf file is included to provide full project and data level documentation.

Data from the Interactive Social Book Search Track Series 2014-2016

The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative—the Data Ark—to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006–2011 (n = 48) and 2014–2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship.

We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry. Finally, we also have done some in a Structural Bioinformatics, Bio-Image analysis and retrieval.

Iowa Public Employment Relations Board (PERB) electronic research and retrieval database system. This system provides access to full-text documents including: Contracts, Contracts Archive, PERB and Court Decisions, and Neutral Decisions.

Contracts – Contracts published and included in this database are only those forwarded to PERB by the parties. Contracts Archive – Contracts that were forwarded to PERB and have expired, beginning with those that expired in 2008, are included in this database.

PERB and Court Decisions – In this database, the PERB decisions do not include routine or preliminary rulings and orders issued by PERB, but include only substantive final agency decisions and non-final rulings and orders deemed informative. Court decisions are those on judicial review of PERB decisions.

Neutral Decisions – The neutral decisions database includes recent and a number of prior years’ fact-finding and interest arbitration decisions. Additionally, it includes only those grievance arbitration decisions forwarded to PERB for publication by the arbitrator with consent of the parties involved. Click on the Contents tab of the database to view all documents contained in the database.

PERB decisions do not include routine or preliminary rulings and orders issued by PERB, but include only substantive final agency decisions and non-final rulings and orders deemed informative. Court decisions are those on judicial review of PERB decisions.

Patterns of Scholarly Communication in Global Information Retrieval Research: A bibliometric analysis (1954-2021)

This package includes data, metadata, and script which enables us to provide comprehensive retrieval data from the USGS gages using the Jupyter notebook server. This code can retrieve the Discharge variable at the LITTLE BEAR RIVER AT PARADISE, UT site. Also, the results are visualized by different Python packages. More detailed information about the site name/code, parameter code, uSGS webpage, and temporal range of retrieved data is mentioned in the 'read_me' file.

This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology.

[210+ Pages Report] The global Automated Storage and Retrieval System (ASRS) market size is expected to grow from USD 7,294.50 million to USD 10,824.87 million by 2028, at a CAGR of 6.80% from 2022-2028

This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.

This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.

If you want to know more information or obtain complete files, please go to the official website of this platform:

Music Data Sharing Platform for Academic Research

Chinese Traditional Instrument Sound Database (CTIS)

This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.

Multi-functional Music Database for MIR Research

This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.

Midi-wav Bi-directional Database of Pop Music

This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.

The picture shows the operation result of image security retrieval. The experiment was validated on five common data sets.

Liberate the power of biodiversity literature as FAIR digital objects

This is an short tutorial to show how to download OBIS/GBIF occurrence data for multiple species. The code has been written to be used in H2020 Mission Atlantic (No 862428) Project Task 3.4.

The tutorial has been developed by Mireia Valle (github profile: MireiaValle, email: mvalle@azti.es) based on original code for sourcing OBIS and GBIF from Guillem Chust (email: gchust@azti.es) and some adaptations from Eduardo Ramirez.

Affiliation: AZTI, Marine Research, Basque Research and Technology Alliance (BRTA). Txatxarramendi ugartea z/g, 48395 Sukarrieta - Bizkaia, Spain

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan—another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

Data sets of the paper "Retrieval-Reliant Mechanisms of Item-Method Directed Forgetting: The Effect of Semantic Cues". The data of the pretest is in long and the data of study 1 and study 2 is in wide format.

The frequncies for the multinomial model are reported in the paper itself.

Data generated in the context of research over recovering information in a specific field (Arts) on Oasisbr < https://oasisbr.ibict.br/ >.