cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p93z392o
title: Vegetation and water classifications for a segment of the Paria River upstream of the Colorado River Confluence, Arizona, USA

description: These data represent total vegetation and surface water along approximately 12 kilometers of the Paria River upstream from the confluence of the Colorado River at Lees Ferry, Arizona. They are derived from airborne, multispectral imagery obtained in late May 2009, 2013, and 2021, collected with a push-broom sensor with 4 spectral bands depicting Blue, Green, Red and Near-Infrared wavelengths at a spatial resolution of 20 centimeters. The vegetation classification data were created using a supervised classification algorithm provided by Harris Geospatial in ENVI version 5.6.3 (Exelis Visual Information Solutions, Boulder, Colorado). The water data were created using a Green Normalized Difference Vegetation Index (gNDVI) threshold of gNDVI <= 0.2. Each classification dataset contains unclassified pixels (0) and vegetation or surface water pixels (1) and are published as Cloud Optimized GeoTIFF raster datasets.
DOI: 10.25549/usctheses-c3-625053
title: etd-LiNanzhou-3793~35
DOI: 10.25549/usctheses-c3-625022
title: etd-LiNanzhou-3793~04
DOI: 10.25549/usctheses-c3-624725
title: etd-DeeSylviaG-3790~231
DOI: 10.25549/usctheses-ouc11291410
title: Microsoft Word - Title Page .docx
DOI: 10.25549/usctheses-ouc11256658
title: 00001.tif
DOI: 10.25549/usctheses-ouc11256759
title: 00001.tif
DOI: 10.25549/usctheses-ouc11256761
title: 00001.tif
DOI: 10.25549/usctheses-ouc11256765
title: 00001.tif
DOI: 10.25549/usctheses-ouc11291422
title: Thesis Draft 74
DOI: 10.25549/usctheses-ouc11257726
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257736
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257738
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257742
title: Microsoft Word - Tsuilei - Dissertation Final v3.docx
DOI: 10.25549/usctheses-ouc11257741
title: 00001.tif
DOI: 10.25549/usctheses-ouc11291455
title: TITLE GOES HERE AND NEEDS TO BE DOUBLE-SPACED
DOI: 10.25549/usctheses-ouc11291446
title: thesis.dvi
DOI: 10.25549/usctheses-ouc11291461
title: Microsoft Word - $ASQ117808_supp_undefined_9EB8ACB8-12E4-11E1-A7BA-440BEF8616FA.docx
DOI: 10.25549/usctheses-ouc11291457
title: Microsoft Word - Pao-Chen Li thesis 111811.docx
DOI: 10.25549/usctheses-ouc11291623
title: Microsoft Word - PhD - Thesis - Omer Faruk Yilmaz -v27
DOI: 10.25549/usctheses-ouc11291641
title: babette-moreno-march8
DOI: 10.25549/usctheses-ouc11291497
title: youTHinkThesisSept27
DOI: 10.25549/usctheses-ouc11257104
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257165
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257080
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257164
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257189
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257110
title: 00001.tif
DOI: 10.25549/usctheses-ouc11291495
title: Microsoft Word - Nury diss Oct 20.doc
DOI: 10.25549/usctheses-ouc11257113
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257159
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257162
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257121
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257188
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257163
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257190
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257161
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257193
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257194
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257192
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257195
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257203
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257201
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257204
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257207
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257212
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257214
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257211
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257217
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257222
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257223
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257228
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257229
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257231
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257241
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257242
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257238
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257244
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257744
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257749
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257755
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257753
title: Microsoft Word - Thesis_Final.docx
DOI: 10.25549/usctheses-ouc11257754
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257756
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257758
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257759
title: 00001.tif
DOI: 10.5281/zenodo.10637381
title: Supplementary data of the paper: "Blockchain patent landscaping: An expert based methodology and search query." World Patent Information, 61 2020.

description: The present study is concerned with the emergence of Blockchain related technologies in terms of patenting activity. Blockchain has captured the attention of the public and research has intensified in this field over the last few years, making it a highly interesting topic of study for a patent analysis in order to obtain insight into the developments of this emerging technology. In this paper we present a unique methodology and exhaustive search strategy for identifying Blockchain patent documents by using a combination of specific keywords and patent classifications. This query was built in cooperation with subject matter experts of the European Patent Office (EPO). Our keyword set was then analysed by relevance and was prioritised. The set of specific relevant patent classifications was furthermore combined with keywords in order to exclude false positives. With our methodology we present an exhaustive query for retrieving a highly relevant dataset of Blockchain related patents, extracted from the EPO databases that can be used for patent landscaping exercises or any other bibliometric analysis. In a case study we applied the search strategy to analyse worldwide Blockchain patenting from 2008 till 2018. The results are presented in this paper.
DOI: 10.5281/zenodo.10637382
title: Supplementary data of the paper: "Blockchain patent landscaping: An expert based methodology and search query." World Patent Information, 61 2020.

description: The present study is concerned with the emergence of Blockchain related technologies in terms of patenting activity. Blockchain has captured the attention of the public and research has intensified in this field over the last few years, making it a highly interesting topic of study for a patent analysis in order to obtain insight into the developments of this emerging technology. In this paper we present a unique methodology and exhaustive search strategy for identifying Blockchain patent documents by using a combination of specific keywords and patent classifications. This query was built in cooperation with subject matter experts of the European Patent Office (EPO). Our keyword set was then analysed by relevance and was prioritised. The set of specific relevant patent classifications was furthermore combined with keywords in order to exclude false positives. With our methodology we present an exhaustive query for retrieving a highly relevant dataset of Blockchain related patents, extracted from the EPO databases that can be used for patent landscaping exercises or any other bibliometric analysis. In a case study we applied the search strategy to analyse worldwide Blockchain patenting from 2008 till 2018. The results are presented in this paper.
DOI: 10.25549/usctheses-ouc11257248
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257250
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257253
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257258
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257265
title: 00001.tif
DOI: 10.25549/usctheses-ouc11291522
title: EFFECTIVE STRATEGIES FOR TEACHING MATHEMATICS
DOI: 10.25549/usctheses-ouc11257267
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257269
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257271
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257272
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257275
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257273
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257277
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257279
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257286
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257284
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257295
title: 00001.tif
DOI: 10.25549/usctheses-ouc11291543
title: Microsoft Word - McDonald Dissertation Jan 13 2012
DOI: 10.25549/usctheses-ouc11257333
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257341
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257331
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257306
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257310
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257312
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257315
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257342
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257345
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257349
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257350
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257355
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257362
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257361
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257369
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257380
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257383
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257417
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257442
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257446
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257450
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257459
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257456
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257458
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257464
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257471
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257472
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257467
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257479
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257481
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257484
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257482
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257492
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257489
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257497
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257494
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257500
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257502
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257504
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257511
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257515
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257517
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257519
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257533
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257523
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257524
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257528
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257536
title: Microsoft Word - Thesis_Mihir Shah_Final.docx
DOI: 10.25549/usctheses-ouc11257530
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257541
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257546
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257547
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257562
title: Microsoft Word - Maciel-Cervantes_FinalThesisDraft.docx
DOI: 10.25549/usctheses-ouc11257550
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257551
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257556
title: Microsoft Word - Ren_PhD_Thesis-5
DOI: 10.25549/usctheses-ouc11257554
title: 01_Murphy_Dis_Total_052517_Submittal_Final
DOI: 10.25549/usctheses-ouc11257555
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257560
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257561
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257567
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257568
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257571
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257583
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257580
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257588
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257589
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257593
title: Microsoft Word - Title Page
DOI: 10.25549/usctheses-ouc11257597
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257599
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257600
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257601
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257608
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257610
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257612
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257611
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257621
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257637
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257663
title: SaurovSyed_Dissertation_June2nd_2017
DOI: 10.25549/usctheses-ouc11257673
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257676
title: Microsoft Word - The British Are Coming_final.docx
DOI: 10.25549/usctheses-ouc11257680
title: Crosstown Traffic and Beyond
DOI: 10.25549/usctheses-ouc11257683
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257686
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257689
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257699
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257704
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257705
title: Microsoft Word - Masters thesis - politics.docx
DOI: 10.25549/usctheses-ouc11257714
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257713
title: Dissertation 041817b
DOI: 10.25549/usctheses-ouc11257716
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257737
title: Microsoft Word - YuRyanChen_Dissertation_v8.docx
DOI: 10.25549/usctheses-ouc11257734
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257719
title: THESIS TEMPLATE
DOI: 10.25549/usctheses-ouc11257720
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257725
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257760
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257762
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257764
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257766
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257778
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257768
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257786
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257787
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257769
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257770
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257773
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257772
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257785
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257776
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257777
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257780
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257782
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257783
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257781
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257788
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257789
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257804
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257792
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257793
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257795
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257796
title: Microsoft Word - Julia Ryan Thesis Final Draft.docx
DOI: 10.25549/usctheses-ouc11257815
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257798
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257799
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257800
title: 20170515+Complete+Thesis+Amanda
DOI: 10.25549/usctheses-ouc11257801
title: 00001.tif
DOI: 10.25549/usctheses-ouc11291647
title: submit_update1.pdf
DOI: 10.6084/m9.figshare.14480364
title: Compliance and side effects of tongue stabilizing device in patients with obstructive sleep apnea

description: To evaluate the long-term effectiveness, compliance, and side effects of tongue stabilizing devices (TSDs). Thirty-nine patients were followed up after 12 and 30 months. The subjective effectiveness was assessed using the Epworth Sleepiness Scale (ESS), the Functional Outcomes Sleep Questionnaire (FOSQ-10), the Chalder Fatigue Scale (CFQ), and a sleep-related quality of life questionnaire (QoL). Compliance and side effects were assessed. At 12-months, 35.9% of patients confirmed continuing the therapy, compared to only 15.4% of patients at 30 months. At 30 months, a significant average improvement of ESS (2.0 ± 2.8) was observed compared to baseline levels in six patients. Six patients demonstrated an average increase in blood pressure. The most frequently reported side effects were mouth dryness and excessive salivation. The 3D analysis revealed small tooth movements. The TSD therapy demonstrated a good long-term subjective effectiveness against OSA but had a relatively low treatment acceptance rate.
DOI: 10.6084/m9.figshare.14480364.v1
title: Compliance and side effects of tongue stabilizing device in patients with obstructive sleep apnea

description: To evaluate the long-term effectiveness, compliance, and side effects of tongue stabilizing devices (TSDs). Thirty-nine patients were followed up after 12 and 30 months. The subjective effectiveness was assessed using the Epworth Sleepiness Scale (ESS), the Functional Outcomes Sleep Questionnaire (FOSQ-10), the Chalder Fatigue Scale (CFQ), and a sleep-related quality of life questionnaire (QoL). Compliance and side effects were assessed. At 12-months, 35.9% of patients confirmed continuing the therapy, compared to only 15.4% of patients at 30 months. At 30 months, a significant average improvement of ESS (2.0 ± 2.8) was observed compared to baseline levels in six patients. Six patients demonstrated an average increase in blood pressure. The most frequently reported side effects were mouth dryness and excessive salivation. The 3D analysis revealed small tooth movements. The TSD therapy demonstrated a good long-term subjective effectiveness against OSA but had a relatively low treatment acceptance rate.
DOI: 10.6084/m9.figshare.25192793
title: Dynamic Causal Modelling of Face Processing with fMRI and M/EEG

description: This dataset consists of BIDS-formatted fMRI and M/EEG data from Wakeman & Henson (2015) that have been processed for DCM analysis. Multimodal data from fMRI, EEG, MEG magnetometers and MEG planar gradiometers from all 16 subjects have been processed with the analysis pipeline presented in Henson et al (2019). This dataset was prepared for demonstration of group-level estimation and inference of DCMs from fMRI and M/EEG data. The raw data can be found here: https://openneuro.org/datasets/ds000117fMRI data is provided in two formats:<i>fMRI_ProcessedData_Individual_Runs.tar.xz</i> contains processed data per run per participant, about 8.9GB total. <i>fMRI_DCMreadyData_VOI_TimeCourses.tar</i> contains VOI time courses for 3 regions - bilateral EVC, and left and right FFA, obtained after reparametrizing and concatenating processed data per run. This totals up to about 440MB.M/EEG data consists of averaged evoked sensor data for two conditions - faces and scrambled faces. Individual subjects' T1 images were used for creating head models. Forward models (leadfields) for all subjects are included in the dataset, which is provided in two formats:MEEG_DCMreadyData_with_GainMatrix.tar.xz consists of sensor data and leadfields - sufficient for inverting MEG, but not EEG data since forward models for the latter need BEM surfaces. This is compact, about 390MB.MEEG_DCMreadyData_with_GainMatrix_BEM.tar.xz consists of sensor data, leadfields and BEM surfaces - can be used for inverting either MEG or EEG data. This is considerably larger in size, about 2.5GB.All four data packages listed above have corresponding file lists for inspection, as well as SHA256 and MD5 checksums for verification of data integrity. Note that the data are highly compressed and will expand to about twice their size on decompression. Please use 7zip on Windows or `tar -xvf filename` on Linux to extract. Scripts used to produce this dataset from the raw data, as well as as scripts demonstrating group DCM analysis can be found here: https://github.com/pranaysy/MultimodalDCM.For more information or queries, please get in touch, or open an issue on the GitHub repository linked above.
DOI: 10.5281/zenodo.10058298
title: GPT annotated news dataset in IPTC news taxonomy

description: The dataset consists of 4,672 news articles covering 17 first-level categories and 51 second-level categories. Each News item is annotated in IPTC news taxonomy using Gpt3.5 Turbo model in zero-shot setting. The prompting method can be found in our paper "Evaluating the Effectiveness of GPT Large Language Model for News Classification in the IPTC News Ontology"
DOI: 10.5281/zenodo.10058297
title: GPT annotated news dataset in IPTC news taxonomy

description: The dataset consists of 4,672 news articles covering 17 first-level categories and 51 second-level categories. Each News item is annotated in IPTC news taxonomy using Gpt3.5 Turbo model in zero-shot setting. The prompting method can be found in our paper "Evaluating the Effectiveness of GPT Large Language Model for News Classification in the IPTC News Ontology"
DOI: 10.25549/usctheses-ouc11291664
title: Microsoft Word - $ASQ129092_supp_undefined_3D4D65B8-6424-11E1-8373-891F2E1BA5B1.doc
DOI: 10.25549/usctheses-ouc11257890
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257900
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257894
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257895
title: 00001.tif
DOI: 10.34740/kaggle/dsv/7591447
title: Public Opinion Russia Ukraine War (Updated Daily)
DOI: 10.25549/usctheses-ouc11257875
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257876
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257877
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257882
title: Microsoft Word - Parker_Final Dissertation 10242016.docx
DOI: 10.34740/kaggle/dsv/7591458
title: US counties COVID 19 dataset
DOI: 10.5281/zenodo.10637458
title: KWX: scholarly articles with keywords from arXiv

description: A dataset based on arXiv Dataset, where keywords of papers are added in this dataset.
DOI: 10.5281/zenodo.7900482
title: KWX: scholarly articles with keywords from arXiv

description: A dataset based on arXiv Dataset, where keywords of papers are added in this dataset.
DOI: 10.5281/zenodo.7929439
title: KWX: scholarly articles with keywords from arXiv

description: A dataset based on arXiv Dataset, where keywords of papers are added in this dataset.
DOI: 10.5281/zenodo.7900481
title: KWX: scholarly articles with keywords from arXiv

description: A dataset based on arXiv Dataset, where keywords of papers are added in this dataset.
DOI: 10.5281/zenodo.8039724
title: KWX: scholarly articles with keywords from arXiv

description: A dataset based on arXiv Dataset, where keywords of papers are added in this dataset.
DOI: 10.25549/usctheses-ouc11257805
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257807
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257810
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257812
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257813
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257817
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257822
title: 00001.tif
DOI: 10.25549/usctheses-ouc11257820
title: 00001.tif
DOI: 10.25549/usctheses-ouc11258665
title: Microsoft Word - Holton.Mathew.EdD Dissertation.doc
DOI: 10.25549/usctheses-ouc11258666
title: Julian Ceipek - Paraspace Thesis
DOI: 10.25549/usctheses-ouc11292454
title: 5CHAPTER 4
DOI: 10.25549/usctheses-ouc11292464
title: Microsoft Word - $ASQ103252_supp_undefined_11C2E040-1A4F-11E1-847E-7B4CEF8616FA.doc
DOI: 10.25549/usctheses-ouc11292494
title: Microsoft Word - $ASQ129735_supp_undefined_ADB17F62-58D8-11E1-B504-8817EF8616FA.docx
DOI: 10.25549/usctheses-ouc11292331
title: Chapter 1
DOI: 10.25549/usctheses-ouc11292352
title: Microsoft Word - USC_DaoyuanZhai_PhDThesis_v4