cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2022-06.2531205/2121924
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533109/2121925
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529526/2121926
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533065/2121927
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531588/2121928
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533550/2121929
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531153/2121930
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534502/2121931
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533096/2121932
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531544/2121933
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534547/2121934
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531165/2121935
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2528829/2121936
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533004/2121937
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534943/2121938
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534369/2121939
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2532788/2121940
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531857/2121941
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534870/2121942
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534374/2121943
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531177/2121944
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2530082/2121945
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2539136/2121946
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533554/2121947
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2530073/2121948
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529536/2121949
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531446/2121950
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531062/2121951
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2528768/2121952
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2528236/2121953
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534373/2121954
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533675/2121955
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533783/2121956
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533018/2121957
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534395/2121958
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533759/2121959
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534865/2121960
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533119/2121961
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2532316/2121962
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534862/2121963
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531211/2121964
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534953/2121965
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529621/2121966
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534880/2121967
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534462/2121968
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531979/2121969
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531448/2121970
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531589/2121971
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529578/2121972
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534845/2121973
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534353/2121974
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534891/2121975
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534824/2121976
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533743/2121977
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534899/2121978
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531810/2121979
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529714/2121980
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533005/2121981
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534843/2121982
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534554/2121983
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2530035/2121984
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534570/2121985
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529796/2121986
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534885/2121987
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533533/2121988
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531246/2121989
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534909/2121990
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534560/2121991
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531262/2121992
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534431/2121993
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534417/2121994
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529686/2121995
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529943/2121996
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534873/2121997
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534988/2121998
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534433/2121999
title: Data for EMSL Project 51287 from June 2022
DOI: 10.21227/p96v-m791
title: computer vision aided beamforming for indoor scenario at sub-6GHz

description: The growing antenna array scale, the uncorrelated fadings between downlink and uplink of frequency division duplex (FDD) or analog beamforming design increases the difficulty of channel sounding or estimation. Non-wireless channel detection or beam weight prediction method is a promising solution to help obtain timely and accurate wireless channel state. Furthermore, beamforming can be enhanced by the powerful sensing capability of cameras.This dataset records the dataset used in the paper "Computer Vision Aided Beamforming Fused with Limited Feedback". In order to establish the link between visions and wireless channels, images captured by the camera, wireless channels and locations were recorded at the same time. The dataset was from indoor scenario at sub-6GHz.
DOI: 10.25582/data.2022-06.2531791/2122000
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534890/2122001
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534894/2122002
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534375/2122003
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2530404/2122004
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534836/2122005
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534977/2122006
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533039/2122007
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533471/2122008
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534344/2122009
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534972/2122010
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2532271/2122011
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534992/2122012
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534436/2122013
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2532799/2122014
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2532289/2122015
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534908/2122016
title: Data for EMSL Project 51287 from June 2022
DOI: 10.6084/m9.figshare.24419023
title: X.rydalmerensis MALDI-TOF main spectra

description: The associated paper reports on a novel species of Xanthomonas isolated in Australia. This file is the MALDI-TOF spectra required to identify this species.
DOI: 10.6084/m9.figshare.24419023.v2
title: X.rydalmerensis MALDI-TOF main spectra

description: The associated paper reports on a novel species of Xanthomonas isolated in Australia. This file is the MALDI-TOF spectra required to identify this species.
DOI: 10.25582/data.2022-06.2533029/2122017
title: Data for EMSL Project 51287 from June 2022
DOI: 10.6084/m9.figshare.24419023.v1
title: X.rydalmerensis MALDI-TOF main spectra

description: The associated paper reports on a novel species of Xanthomonas isolated in Australia. This file is the MALDI-TOF spectra required to identify this species.
DOI: 10.25582/data.2022-06.2533042/2122018
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533727/2122019
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2532202/2122020
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531815/2122021
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533106/2122022
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533399/2122023
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529924/2122024
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534916/2122025
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529591/2122026
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534434/2122027
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533053/2122028
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533100/2122029
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533099/2122030
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534994/2122031
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534459/2122032
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533008/2122033
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534465/2122034
title: Data for EMSL Project 51287 from June 2022
DOI: 10.6084/m9.figshare.24259482
title: Profiling of <i>Rosa hybrida</i> cv. Mileva essential oil, evaluation of its bioactivity <i>in vitro</i>, chemometric analysis and comparison to other non-commercial roses

description: Rose essential oil is one of the most valuable and pivotal raw materials in the perfume industry. However, the limited yield of essential oil in rose flowers increased the interest of both breeders and scientists to create and discover species that have potential for multifaceted applications aiming to increase the economic viability of rose cultivation. Consequently, garden roses (<i>Rosa hybrida</i>) through ongoing development emerge as possibly profitable for cultivation and processing. This opens possibilities for numerous applications in both food and pharmaceutical industries. The main objective of this study was to identify the essential oil composition of <i>R. hybrida</i> cv. Mileva and compare it against essential oil chemical compositions of 32 accessions of other non-commercial (wild and hybrid) roses available literature. The dominant compound detected in <i>R. hybrida</i> cv. Mileva essential oil was geranyl acetate (47.9%), followed by nonadecane and heneicosane. It was shown that the essential oil possesses moderate antimicrobial activity, good antioxidant potential and strong anti-inflammatory activity.
DOI: 10.6084/m9.figshare.24259482.v2
title: Profiling of <i>Rosa hybrida</i> cv. Mileva essential oil, evaluation of its bioactivity <i>in vitro</i>, chemometric analysis and comparison to other non-commercial roses

description: Rose essential oil is one of the most valuable and pivotal raw materials in the perfume industry. However, the limited yield of essential oil in rose flowers increased the interest of both breeders and scientists to create and discover species that have potential for multifaceted applications aiming to increase the economic viability of rose cultivation. Consequently, garden roses (<i>Rosa hybrida</i>) through ongoing development emerge as possibly profitable for cultivation and processing. This opens possibilities for numerous applications in both food and pharmaceutical industries. The main objective of this study was to identify the essential oil composition of <i>R. hybrida</i> cv. Mileva and compare it against essential oil chemical compositions of 32 accessions of other non-commercial (wild and hybrid) roses available literature. The dominant compound detected in <i>R. hybrida</i> cv. Mileva essential oil was geranyl acetate (47.9%), followed by nonadecane and heneicosane. It was shown that the essential oil possesses moderate antimicrobial activity, good antioxidant potential and strong anti-inflammatory activity.
DOI: 10.6084/m9.figshare.24270063
title: Chemical composition and antimicrobial activity of essential oil obtained from the rhizomes of <i>Kaempferia champasakensis</i>: <i>in vitro</i> and molecular docking studies

description: <i>Kaempferia champasakensis</i> Picheans. & Koonterm (Zingiberaceae) is locally known as “Dia Lien hoa trang” in Vietnam. Despite a previous study on the essential oils (EO) of <i>K. champasakensis</i> leaves, no other studies have reported the chemical components and biological activities of <i>K. champasakensis</i>. Therefore, this study aims to identify the chemical constituents of the EO from the rhizomes of <i>K. champasakensis</i> and evaluate their antimicrobial activity, which has been re-confirmed through molecular docking studies. The gas chromatography-mass spectrometry (GC/MS) analysis revealed sixty-one constituents (95.4%) present in the EO of <i>K. champasakensis</i> rhizomes. Among them, camphene (40.7%), α-pinene (10.2%), and β-pinene (7.6%) were identified as the main compounds in the EO. Bioassays indicated its antimicrobial activity in the order of inhibitory effectiveness: <i>Staphylococcus aureus</i> &gt; <i>Enterococcus faecalis</i> ≈ <i>Candida albicans</i> &gt; <i>Bacillus cereus</i> ≈ <i>Escherichia coli</i> &gt; <i>Salmonella enterica</i> &gt; <i>Pseudomonas aeruginosa</i>. The molecular docking study of ligand-protein interactions involving the major constituents of EO was conducted against target proteins related to <i>Staphylococcus aureus</i>, <i>Enterococcus faecalis, Candida albicans</i>, <i>Bacillus cereus</i>, and <i>Escherichia coli</i> main activities. The results revealed that α-pinene exhibited strong binding affinity against <i>E. coli</i>, β-pinene demonstrated more effective inhibition against <i>E. faecalis</i> and <i>B. cereus</i>, and camphene primarily exhibited activity against <i>C. albicans</i> and <i>S. aureus</i>. Furthermore, the analysis of interactions between the main compounds and the selected proteins was elucidated in the current study. This is the first time that chemical components of the EO of <i>K. champasakensis</i> rhizomes and their antimicrobial activity have been reported. These findings contributed to the basis for further studies on the chemical compositions and biological effects of <i>K. champasakensis</i>.
DOI: 10.6084/m9.figshare.24270063.v2
title: Chemical composition and antimicrobial activity of essential oil obtained from the rhizomes of <i>Kaempferia champasakensis</i>: <i>in vitro</i> and molecular docking studies

description: <i>Kaempferia champasakensis</i> Picheans. & Koonterm (Zingiberaceae) is locally known as “Dia Lien hoa trang” in Vietnam. Despite a previous study on the essential oils (EO) of <i>K. champasakensis</i> leaves, no other studies have reported the chemical components and biological activities of <i>K. champasakensis</i>. Therefore, this study aims to identify the chemical constituents of the EO from the rhizomes of <i>K. champasakensis</i> and evaluate their antimicrobial activity, which has been re-confirmed through molecular docking studies. The gas chromatography-mass spectrometry (GC/MS) analysis revealed sixty-one constituents (95.4%) present in the EO of <i>K. champasakensis</i> rhizomes. Among them, camphene (40.7%), α-pinene (10.2%), and β-pinene (7.6%) were identified as the main compounds in the EO. Bioassays indicated its antimicrobial activity in the order of inhibitory effectiveness: <i>Staphylococcus aureus</i> &gt; <i>Enterococcus faecalis</i> ≈ <i>Candida albicans</i> &gt; <i>Bacillus cereus</i> ≈ <i>Escherichia coli</i> &gt; <i>Salmonella enterica</i> &gt; <i>Pseudomonas aeruginosa</i>. The molecular docking study of ligand-protein interactions involving the major constituents of EO was conducted against target proteins related to <i>Staphylococcus aureus</i>, <i>Enterococcus faecalis, Candida albicans</i>, <i>Bacillus cereus</i>, and <i>Escherichia coli</i> main activities. The results revealed that α-pinene exhibited strong binding affinity against <i>E. coli</i>, β-pinene demonstrated more effective inhibition against <i>E. faecalis</i> and <i>B. cereus</i>, and camphene primarily exhibited activity against <i>C. albicans</i> and <i>S. aureus</i>. Furthermore, the analysis of interactions between the main compounds and the selected proteins was elucidated in the current study. This is the first time that chemical components of the EO of <i>K. champasakensis</i> rhizomes and their antimicrobial activity have been reported. These findings contributed to the basis for further studies on the chemical compositions and biological effects of <i>K. champasakensis</i>.
DOI: 10.6084/m9.figshare.24320785
title: Effect of xylanase addition and ultrasonic pretreatment on the yield and volatile profile of bay laurel and sage essential oil

description: In order to increase the yield of essential oils in hydrodistillation of bay laurel and sage, the effect of pretreatment with ultrasound and enzymes (under different conditions of xylanase pretreatment) was studied. The effects of the optimized conditions (concentration of xylanase 0.20 mg/mL in buffer pH=6.5 at 40°C for 4 h) during the enzymatic pretreatment showed that despite the optimization the activity of xylanase did not significantly affect the quantity and quality of essential oils of bay laurel and sage. Ultrasonic pretreatment prior to enzymatic pretreatment with the addition of optimized xylanase resulted in increased yields of dominant compounds (α-thujone 27.42%, β-thujone 12.15%, and camphor 20.94%) in sage essential oil, while in bay laurel the amount of dominant compounds (1,8-cineole 24.65% and α-terpenyl acetate 11.05%) was not significantly increased. Despite minor changes, ultrasonic and enzymatic pretreatment of the plant materials did not significantly affect the chemical composition of the essential oils of bay laurel and sage compared with the control without pretreatment, as confirmed by the Spearman rank test (<i>p</i> &lt; 0.05). The main advantage of ultrasonic extraction is the reduction in pretreatment time. Ultrasonic pretreatment for only 10 min before enzymatic pretreatment resulted in a 50% increase in sage essential oil yield and a 40% increase in bay laurel essential oil yield.
DOI: 10.6084/m9.figshare.24320785.v2
title: Effect of xylanase addition and ultrasonic pretreatment on the yield and volatile profile of bay laurel and sage essential oil

description: In order to increase the yield of essential oils in hydrodistillation of bay laurel and sage, the effect of pretreatment with ultrasound and enzymes (under different conditions of xylanase pretreatment) was studied. The effects of the optimized conditions (concentration of xylanase 0.20 mg/mL in buffer pH=6.5 at 40°C for 4 h) during the enzymatic pretreatment showed that despite the optimization the activity of xylanase did not significantly affect the quantity and quality of essential oils of bay laurel and sage. Ultrasonic pretreatment prior to enzymatic pretreatment with the addition of optimized xylanase resulted in increased yields of dominant compounds (α-thujone 27.42%, β-thujone 12.15%, and camphor 20.94%) in sage essential oil, while in bay laurel the amount of dominant compounds (1,8-cineole 24.65% and α-terpenyl acetate 11.05%) was not significantly increased. Despite minor changes, ultrasonic and enzymatic pretreatment of the plant materials did not significantly affect the chemical composition of the essential oils of bay laurel and sage compared with the control without pretreatment, as confirmed by the Spearman rank test (<i>p</i> &lt; 0.05). The main advantage of ultrasonic extraction is the reduction in pretreatment time. Ultrasonic pretreatment for only 10 min before enzymatic pretreatment resulted in a 50% increase in sage essential oil yield and a 40% increase in bay laurel essential oil yield.
DOI: 10.25582/data.2022-06.2534377/2122035
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533768/2122036
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2531167/2122037
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534958/2122038
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2530730/2122039
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534818/2122040
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534347/2122041
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533129/2122042
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534922/2122043
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533535/2122044
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2529584/2122045
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2539127/2122046
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2533101/2122047
title: Data for EMSL Project 51287 from June 2022
DOI: 10.6084/m9.figshare.20047756
title: Diffuse Kalman filtering with linear constraints on the state parameters

description: We give a general proof, based on Piet de Jong’s diffuse Kalman filter, that imposing linear constraints on state smoothing is still feasible under diffuse initialization. We also offer simple derivations of existing identities related to the diffuse Kalman filter and smoother.
DOI: 10.25582/data.2022-06.2534947/2122048
title: Data for EMSL Project 51287 from June 2022
DOI: 10.25582/data.2022-06.2534932/2122049
title: Data for EMSL Project 51287 from June 2022