cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26808865
title: Empirical study on the correlation between urbanization and the cultural industry: A case study of China

description: DEA Model Metadata and Results, Tobit Model Variables and Results
DOI: 10.6073/pasta/34cff25ca8e268cb2b66f9da4118dd9b
title: Prieta streams- Discharge and water level

description: Water level measuraments as part of the Stream Flow Reduction Experiment (StreamFRE). Background: Water level data has been collected in two tributaries of the Quebrada Prieta stream since 2016, at the inception of the StreamFRE project. This file contains water level data since 2016. Water level is measured using an Onset HOBO depth water level data logger (modelo U20L-04). Raw data files are corrected using a reference stage height at the site and Barometric data collected at Prieta with a similar sensor placed outside the water. Water level data is logged continuously every 15 minutes.
 
 
 This is a long-term monitoring of water discharge and water level at Prieta Stream. The monitoring point is located at Pool 0: which is the same location where we sample stream chemistry. Measurements are taken automatically with a pressure transducer: every 15 minutes. Measure are originally of pressure: which are translated to water level using the reference gauge. Using an equation provided by Dr. Fred Scatena: water level is converted into discharge. Stage height to discharge equation created by Fred N Scatena used from 2006 to 3-26-2013 10 AM: discharge (m3/sec) = 1.095 - 1.585 * stage + 0.578 * stage * stage Stage height to discharge equation created by William H McDowell and Jody Potter used from 3-26-2013 10:15 to present: discharge (m3/sec) = 0.0005*((stage)^8.8846)*35.314666
 
 
 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DOI: 10.15468/dl.azkv7z
title: Occurrence Download

description: A dataset containing 314379 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((99.06715 21.11348,102.01529 21.08162,102.36535 22.02895,104.72872 22.51544,106.05271 22.42296,106.184 29.25,104.51852 28.9525,103.3426 28.47997,103.06342 27.70595,102.46519 26.64156,101.6476 26.64156,100.88984 26.39176,100.4312 27.97045,99.84444 27.46681,99.2637 29.08921,98.78308 29.17667,97.52 29.25,97.46023 23.54588,98.45703 23.79256,98.94593 22.79748,99.06715 21.11348))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2015-2024"
 ]
}

 The dataset includes 314379 records from 511 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078064-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8nx6cd
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Cyclops vicinus Uljanin, 1875",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 127 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078075-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r4nqm7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Mesocyclops leukarti (Claus, 1857)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078076-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pyfgr8
title: Occurrence Download

description: A dataset containing 64516500 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.58737 -33.90335,-33.90625 -33.90335,-33.90625 7.26742,-75.58737 7.26742,-75.58737 -33.90335))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 64516500 records from 6455 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078067-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34820/fk2/wqygkp/2ylf86
title: ND_54.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/z4kkkt
title: HFD_21.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/vtocnt
title: ND_88.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/20jtro
title: HFD_82.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/8rttlu
title: HFD_38.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/66eron
title: ND_16.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/9pzrnr
title: ND_93.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/vwwidr
title: HFD_62.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/pvfzee
title: HFD_40.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ohi1x3
title: HFD_24.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/fvl9pj
title: HFD_15.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/idtljo
title: ND_62.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ugyxed
title: ND_47.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/gxmw1e
title: ND_35.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/6ormbl
title: ND_27.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/biuhv2
title: ND_18.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ofgvqy
title: HFD_94.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/dmeyeh
title: ND_68.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/9ifc3f
title: ND_4.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/tamocl
title: ND_70.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/k6uoi5
title: ND_65.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/jfmlzf
title: HFD_100.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/kn5jib
title: ND_31.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/dhdzud
title: ND_37.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/aziuze
title: HFD_66.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/d8vrvi
title: ND_20.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/f6spix
title: ND_41.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/8mrobl
title: HFD_58.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/vij5au
title: ND_64.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/xdrz5e
title: ND_59.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/7xvcg2
title: HFD_6.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/j2ctbl
title: ND_49.jpg

description: :unav
DOI: 10.15139/s3/dxjgy7
title: Replication Data for: Complexities and costs of floodplain buyout implementation

description: Public acquisitions of floodplain properties, or “buyouts,” whereby governments purchase properties at risk of flooding from willing sellers and convert them to open space, are a widely used strategy for reducing risk. Since 1990, the U.S. Federal Emergency Management Agency (FEMA) has provided funding for more than 40,000 properties. Yet, little is known about the costs of buyout implementation, even though federal funding requirements mandate a complex set of activities undertaken by local, state, and federal government staff. This lack of understanding of buyout activity costs hinders development of evidence-based policy recommendations. To address this gap, we surveyed local and state government officials and consultants who have worked on floodplain buyout projects. Our survey results provide the first systematic, activity-level financial documentation of buyout projects in the U.S. Local and state government respondents reported median per-property activity costs of $14,428 and $8161 (or 9.64% and 6.95% of property purchase costs), respectively. Respondents also reported significant variation in the activities undertaken as part of each project; community engagement strategies were particularly diverse, suggesting some households may not be adequately informed as a result of insufficient funding, time, or technical capacity for these activities. The varied and complex structures of buyout projects, as well as the attendant activity costs, pose barriers to implementation for local governments. Our results suggest both that: a) additional support and flexibility may be needed for critical activities that improve the experience of buyout participants; and b) reducing other activity costs may produce significant savings, which in turn could be used to improve the quality and expand the scope of buyout projects.
DOI: 10.34820/fk2/wqygkp/rih4he
title: ND_36.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/xynv2i
title: HFD_86.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/8c9rlq
title: HFD_53.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/cjujco
title: ND_99.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/q0wqif
title: ND_55.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/aoi6i4
title: ND_69.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/jmsgm8
title: HFD_4.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/oc0rce
title: ND_92.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/gpsutk
title: HFD_54.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/zyge6i
title: ND_29.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ltoyzg
title: HFD_73.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/b0ecqf
title: ND_82.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/beywhg
title: HFD_64.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/a81cz9
title: HFD_60.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ec04as
title: HFD_10.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/fhsqar
title: ND_39.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ht3uf9
title: ND_67.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/wbvwun
title: HFD_8.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/063oh1
title: ND_1.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/zhlqtk
title: ND_74.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/aterup
title: HFD_99.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/hfdpib
title: HFD_43.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/wofujh
title: ND_96.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/hqby6y
title: ND_81.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/usuzxz
title: ND_23.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/rwwkoi
title: HFD_98.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/cs9qss
title: ND_7.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/j5uhmy
title: HFD_50.jpg

description: :unav
DOI: 10.34740/kaggle/dsv/3443465
title: Russian Invasion of Ukraine
DOI: 10.34820/fk2/wqygkp/mwhapz
title: HFD_80.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/rcopac
title: HFD_2.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/wwf34j
title: HFD_33.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/pr8x1q
title: HFD_22.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ypvzgv
title: HFD_23.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/d9rr2s
title: ND_34.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/cknzt0
title: HFD_57.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/zntme0
title: HFD_49.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/8m0kzw
title: HFD_96.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/gbkfsw
title: ND_43.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ukzfpw
title: HFD_18.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/vvqha8
title: HFD_9.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ugs4we
title: HFD_93.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/a4vjul
title: HFD_88.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/p3xwhf
title: HFD_77.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/aazx9n
title: HFD_76.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ykusf2
title: HFD_97.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/z92hpm
title: HFD_27.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/uxasnd
title: ND_72.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/jhoi0g
title: HFD_41.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/xvdyfd
title: HFD_74.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ls6ilh
title: ND_28.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/0ckfxx
title: ND_79.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/qqdhr9
title: ND_57.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/prrjnb
title: HFD_56.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/4ftgu7
title: ND_78.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/3uu37i
title: HFD_16.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/lvxusr
title: HFD_34.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/gdsuzh
title: HFD_92.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/9ibdni
title: ND_6.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/j1nflb
title: HFD_25.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/nfvido
title: ND_32.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/onxmqu
title: HFD_42.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/h8kxw5
title: ND_87.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/ubvbop
title: HFD_91.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/22hho9
title: HFD_39.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/2fun1e
title: HFD_85.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/actnbw
title: HFD_30.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/tuc4yn
title: HFD_81.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp/hjsfpu
title: HFD_47.jpg

description: :unav
DOI: 10.34820/fk2/wqygkp
title: BAT-HFDMM_v1.0

description: wenty-four, 5-week-old male Deutsche Denken Yoken mice (Mus musculus) were randomly divided into four groups: The normal chow diet group was fed a normal diet, and the HFD group was fed a high-fat diet (HFD). The HFD groups were fed with Moringa Oleifera (MO) leaf extract (MOLE) in a dose of 280 and 560 mg/kg body weight (BW)/day, respectively. The experiment was performed for seven weeks. Histological analysis was performed on the interscapular Brown adipose tissue (BAT). In this version, the data consisted of 6,000 images grouped into two classes, the normal and HFD. Both classes has 3,000 images each. All the images are stored in *.jpg file format
DOI: 10.6084/m9.figshare.19575820.v1
title: Table_S2

description: The data for the number of chromosomal and plasmid-related PPIs per gene for 4,394 samples.
DOI: 10.5517/ccdc.csd.cc25z3y7
title: CCDC 2024949: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z3z8
title: CCDC 2024950: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z40b
title: CCDC 2024951: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z41c
title: CCDC 2024952: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z42d
title: CCDC 2024953: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z43f
title: CCDC 2024954: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z44g
title: CCDC 2024955: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z45h
title: CCDC 2024956: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc25z46j
title: CCDC 2024957: Experimental Crystal Structure Determination

description: Related Article: Shi-He Wang, Jia-Qi Li, Yan-Mei Wang, Shu Hou, Yu-Xian Chi, Jing Jin|2022|Inorg.Chim.Acta|537|120927|doi:10.1016/j.ica.2022.120927
DOI: 10.5517/ccdc.csd.cc27xjvk
title: CCDC 2083009: Experimental Crystal Structure Determination

description: Related Article: Bandar A. Babgi, Sammar Alsaedi, Doaa Domyati, Abdesslem Jedidi, Bambar Davaasuren, Abdul-Hamid M. Emwas, Mariusz Jaremko|2022|J.Mol.Struct.|1261|132933|doi:10.1016/j.molstruc.2022.132933
DOI: 10.5517/ccdc.csd.cc29zslp
title: CCDC 2144753: Experimental Crystal Structure Determination

description: Related Article: Shuyang Zhang, Maren Pink, Tobias Junghoefer, Wenchao Zhao, Sheng-Ning Hsu, Suchada Rajca, Arrigo Calzolari, Bryan W. Boudouris, Maria Benedetta Casu, Andrzej Rajca|2022|J.Am.Chem.Soc.|144|6059|doi:10.1021/jacs.2c01141
DOI: 10.5517/ccdc.csd.cc29zsmq
title: CCDC 2144754: Experimental Crystal Structure Determination

description: Related Article: Shuyang Zhang, Maren Pink, Tobias Junghoefer, Wenchao Zhao, Sheng-Ning Hsu, Suchada Rajca, Arrigo Calzolari, Bryan W. Boudouris, Maria Benedetta Casu, Andrzej Rajca|2022|J.Am.Chem.Soc.|144|6059|doi:10.1021/jacs.2c01141
DOI: 10.6084/m9.figshare.19525681.v2
title: Table_S5_158_samples

description: Bacterial samples (158, 5.3% of total) with a much higher aPMNLE for all proteins than the chromosomal ones alone. This indicated that the plasmid-related proteins contributed more indirect PPIs.
DOI: 10.34820/fk2/ee1p82/7u2wbr
title: Feed IG Bank BUMN.pdf

description: Poster feed akun instagram bank BRI, BNI dan BTN
DOI: 10.34820/fk2/ee1p82
title: Struktur Komunikasi Visual Feed Instagram Bank BUMN di Tahun 2020 “Studi Kasus Bank BRI, BNI dan BTN”

description: Dataverse berikut merupakan data visual hasil dari penghitungan jumlah poster pada feed akun Instagram bank BRI, BNI dan BTN berdasarkan likes terbanyak di setiap bulannya sepanjang tahun 2020.
DOI: 10.5281/zenodo.6414290
title: Datasets and R-codes complementing the Opinion of the Norwegian Scientific Committee for Food and Environment (VKM) "Risk-Benefit Assessment of Sunscreen"

description: These datasets complement the Opinion of the Norwegian Scientific Committee for Food and Environment (VKM) "Risk-Benefit Assessment of Sunscreen". They contain: i) data on concentration of the UV filters bis-ethyl-hexyloxyphenol methoxyphenyl triazine (BEMT), butyl methoxydibenzoyl methane (BMDBM), 2-ethylhexyl salicylate (EHS), ethylhexyl triazone (EHT), octocrylene (OC), and titanium dioxide in nanoform (NP-TiO<sub>2</sub>) in commercially available sunscreens, ii) data on amount sunscreen used, and iii) data on dermal absorption of the UV filters bis-ethyl-hexyloxyphenol methoxyphenyl triazine (BEMT), butyl methoxydibenzoyl methane (BMDBM), 2-ethylhexyl salicylate (EHS), ethylhexyl triazone (EHT), octocrylene (OC), and titanium dioxide in nanoform (NP-TiO<sub>2</sub>). The data are used in the probabilistic exposure estimations in the Opinion. The data were retrieved from open sources. In addition, the R-codes used to perform the exposure estimation, are included.
description: NO; ZIP; efsafocalpoint-norway@vkm.no
DOI: 10.6084/m9.figshare.19369164
title: Parameter Estimation of Binned Hawkes Processes

description: A key difficulty that arises from real event data is imprecision in the recording of event time-stamps. In many cases, retaining event times with a high precision is expensive due to the sheer volume of activity. Combined with practical limits on the accuracy of measurements, binned data is common. In order to use point processes to model such event data, tools for handling parameter estimation are essential. Here we consider parameter estimation of the Hawkes process, a type of self-exciting point process that has found application in the modeling of financial stock markets, earthquakes and social media cascades. We develop a novel optimization approach to parameter estimation of binned Hawkes processes using a modified Expectation-Maximization algorithm, referred to as Binned Hawkes Expectation Maximization (BH-EM). Through a detailed simulation study, we demonstrate that existing methods are capable of producing severely biased and highly variable parameter estimates and that our novel BH-EM method significantly outperforms them in all studied circumstances. We further illustrate the performance on network flow (NetFlow) data between devices in a real large-scale computer network, to characterize triggering behavior. These results highlight the importance of correct handling of binned data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19369164.v1
title: Parameter Estimation of Binned Hawkes Processes

description: A key difficulty that arises from real event data is imprecision in the recording of event time-stamps. In many cases, retaining event times with a high precision is expensive due to the sheer volume of activity. Combined with practical limits on the accuracy of measurements, binned data is common. In order to use point processes to model such event data, tools for handling parameter estimation are essential. Here we consider parameter estimation of the Hawkes process, a type of self-exciting point process that has found application in the modeling of financial stock markets, earthquakes and social media cascades. We develop a novel optimization approach to parameter estimation of binned Hawkes processes using a modified Expectation-Maximization algorithm, referred to as Binned Hawkes Expectation Maximization (BH-EM). Through a detailed simulation study, we demonstrate that existing methods are capable of producing severely biased and highly variable parameter estimates and that our novel BH-EM method significantly outperforms them in all studied circumstances. We further illustrate the performance on network flow (NetFlow) data between devices in a real large-scale computer network, to characterize triggering behavior. These results highlight the importance of correct handling of binned data. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.6448050
title: La Palma volcano eruption

description: Tweets parsed during volcano eruption on La Palma island (23.09.2021 - 3.10.2021)
DOI: 10.5281/zenodo.6448049
title: La Palma volcano eruption

description: Tweets parsed during volcano eruption on La Palma island (23.09.2021 - 3.10.2021)
DOI: 10.5281/zenodo.6432225
title: Paintings information from Artmajeur

description: The data set includes the following information of all the paintings in https://www.artmajeur.com: <strong>author</strong>, <strong>country,</strong> <strong>title</strong>, <strong>year</strong>, <strong>H_dimension, <strong>W_dimension, <strong>technique, <strong>price</strong></strong></strong></strong>