cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6793112
title: Web requests analysis of Italy websites which use Google Analytics

description: List of 504,038 domains of Italy found to contain Google Analytics. The front page for Italy-related domain names has been accessed through HTTPS or HTTP and analysed with webbkoll and jq to gather data about third-party requests, cookies and other privacy-invasive features. Together with the actual URL visited, the user/property ID is provided for 495,663 domains (extracted either from the cookies deposited or the URL of requests to Google Analytics). MX and TXT records for the domains are also provided. The most common ID found was 23LNSPS7Q6, with over 35k domains calling it (seemingly associated with italiaonline.it). The most common responding IP addresses were 3 AWS IPv4 addresses (over 40k domains) and 2 CloudFlare IPv6 addresses (over 12k domains).
description: {"references": ["Victor Le Pochat, Tom Van Goethem, Samaneh Tajalizadehkhoob, Maciej Korczy\u0144ski, and Wouter Joosen. 2019. \"Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation,\" Proceedings of the 26th Annual Network and Distributed System Security Symposium (NDSS 2019). https://doi.org/10.14722/ndss.2019.23386", "Anders Jensen-Urstad (2022). webbkoll. https://github.com/andersju/webbkoll"]}
DOI: 10.5281/zenodo.6970596
title: Supporting experimental data for "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al.

description: Data (auto-correlation functions used for preparing Figure 5-8) accompanying manuscript "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al. 1. “<strong>Figure 5_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate heteroscedastic and angle-dependent uncertainties. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 5 is based on the analyses of subsets of this data set. 2. “<strong>Figure 6 & 7_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the impact of uncertainty on precision, and the impact of duration and photon counting rate on the signal-to noise ratio. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 6 and 7 are based on the analyses of subsets of this data set. 3. “<strong>Figure 8_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the positive impact of data averaging on precisions. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 8 is based on the analyses of subsets of this data set.
DOI: 10.5281/zenodo.6970595
title: Supporting experimental data for "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al.

description: Data (auto-correlation functions used for preparing Figure 5-8) accompanying manuscript "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al. 1. “<strong>Figure 5_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate heteroscedastic and angle-dependent uncertainties. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 5 is based on the analyses of subsets of this data set. 2. “<strong>Figure 6 & 7_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the impact of uncertainty on precision, and the impact of duration and photon counting rate on the signal-to noise ratio. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 6 and 7 are based on the analyses of subsets of this data set. 3. “<strong>Figure 8_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the positive impact of data averaging on precisions. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 8 is based on the analyses of subsets of this data set.
DOI: 10.6084/m9.figshare.20452008
title: RSL - Fact-checking - Selecionados.csv

description: Planilha de codificação RSL com AC sobre fact-checking
DOI: 10.6084/m9.figshare.20452008.v1
title: RSL - Fact-checking - Selecionados.csv

description: Planilha de codificação RSL com AC sobre fact-checking
DOI: 10.6084/m9.figshare.20452005
title: Data_WoodyInvaders

description: This dataset is the one used in the study of " Woody invaders from contrasted climatic origins distribute differently across the urban-to-rural gradient in oceanic Europe – Is it trait-related? " regarding the intraspecific trait variation part.
DOI: 10.6084/m9.figshare.20452005.v1
title: Data_WoodyInvaders

description: This dataset is the one used in the study of " Woody invaders from contrasted climatic origins distribute differently across the urban-to-rural gradient in oceanic Europe – Is it trait-related? " regarding the intraspecific trait variation part.
DOI: 10.5281/zenodo.6974483
title: Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID-19 CDMX – JULY 2021)

description: Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID19-related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the third dataset of the project, corresponding to July 2021, collected 15 months after the lockdown began in Mexico. Data collection was performed from July 19 to 31, 2021.
description: Funding for this porject provided by Universidad Iberoamericana and UNICEF.. If international researchers require additional information, please contact Victor Perez (victor.hernandez@ibero.mx).
DOI: 10.5281/zenodo.6974482
title: Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID-19 CDMX – JULY 2021)

description: Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID19-related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the third dataset of the project, corresponding to July 2021, collected 15 months after the lockdown began in Mexico. Data collection was performed from July 19 to 31, 2021.
description: Funding for this porject provided by Universidad Iberoamericana and UNICEF.. If international researchers require additional information, please contact Victor Perez (victor.hernandez@ibero.mx).
DOI: 10.5281/zenodo.6974936
title: Educar en Contingencia: Docentes de Primaria

description: El objetivo de la investigación fue analizar el aprendizaje y el bienestar socioemocional de los(as) estudiantes y docentes de Instituciones de Educación Superior pública y particular en México, a partir del uso de estrategias didáctico-tecnológicas que emplean durante el periodo de cuarentena, tomando como punto de partida la identificación de incidentes críticos asociados a la vida escolar durante el período de la cuarentena. Se aplicaron cuestionarios autoadministrados, el tipo de muestra fue no probabilística, por conveniencia a través de Google Forms. Por cuestiones de confidencialidad y anonimato se eliminaron posibles variables de identificación. Estas bases corresponden a los docentes de primaria mexicanos.
DOI: 10.5281/zenodo.6974935
title: Educar en Contingencia: Docentes de Primaria

description: El objetivo de la investigación fue analizar el aprendizaje y el bienestar socioemocional de los(as) estudiantes y docentes de Instituciones de Educación Superior pública y particular en México, a partir del uso de estrategias didáctico-tecnológicas que emplean durante el periodo de cuarentena, tomando como punto de partida la identificación de incidentes críticos asociados a la vida escolar durante el período de la cuarentena. Se aplicaron cuestionarios autoadministrados, el tipo de muestra fue no probabilística, por conveniencia a través de Google Forms. Por cuestiones de confidencialidad y anonimato se eliminaron posibles variables de identificación. Estas bases corresponden a los docentes de primaria mexicanos.
DOI: 10.34740/kaggle/dsv/4045316
title: Bangladesh Daraz E-commerce Headphone dataset
DOI: 10.15482/usda.adc/1527816
title: Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) - Field trial data from University of Georgia Stripling Irrigation Research Park (SIRP)

description: Research was conducted at the University of Georgia’s Stripling Irrigation Research Park (SIRP) located near Camilla, Georgia in a 4 ha research field known as the Newton Lateral (31°16'46.24"N, 84°17'59.59"W). The field is divided into three blocks (North, Middle, South) with each block containing 27 plots. The plots were each 14.5 × 14. 5 m (48 × 48 ft or 48 ft long × 16 rows wide). The eight middle rows in each plot were used for data collection and the four rows on either side of the middle eight served as buffers. The soil in the Newton Lateral field is classified as a Lucy Loamy Sand with available water holding capacity of 0. 08 cm/cm with 0 to 5% slope. Soil texture varies slightly across the field with 83% Sand, 10% Silt and 7% Clay in the South block to 86% Sand, 8% Silt, and 6% Clay in the North block. The field is irrigated with a variable rate enabled lateral irrigation system which can apply a unique water application rate to each of the 81 plots. A portable liquid fertilizer injection system allows for application of unique fertilizer application rate to each of the 81 plots through the irrigation system (fertigation). The experimental design utilized a randomized complete block design. The 27 plots in each block were divided into nine treatments with three replicates each. A cotton-peanut-corn rotation was maintained with crops rotating from north to south each year. All crops were planted into a rye cover crop using strip tillage following burndown with glyphosate. Three irrigation × three fertilization treatments were evaluated in the corn and cotton plots. Since N fertilizer is not applied to peanut during the growing season, nine irrigation treatments were evaluated in peanut. During each growing season, soil moisture was monitored continuously in each plot using matric potential type soil moisture sensors in all crops. Soil nitrogen and rye cover crop biomass was measured prior to burndown. Soil nitrogen and crop biomass was measured at regular intervals during the growing season with the final sample being collected just prior to harvest. Biomass included dry weight of separate plant tissues as well as Total Kjeldahl Nitrogen (TKN) and yield. Soil samples were collected to 0.9 m in 4 increments and analyzed for Nitrate, Ammonium, and TKN.
DOI: 10.5281/zenodo.6974946
title: Test DataFed Upload-5

description: Test DataFed Upload
description: id: "d/403447813" title: "YG044_Yichen_Guo_07172022" metadata: "{\"header\":{\"Base Pressure (Torr)\":\"8e-5\",\"Chamber\":\"Laser 1C\",\"Cool Down Atmosphere\":\"Oxygen\",\"Date\":\"07/17/2022\",\"Growth ID\":\"YG044\",\"Notes\":\"5th continuous growth set - 3rd sample (treated_set_2 - s3)\",\"Path\":\"C:/Image/\",\"Substrate_1\":\"SrTiO3\",\"User Name\":\"Yichen_Guo\",\"time\":\"14:56:34\"},\"target_1\":{\"Aperture\":\"12.5\",\"Atmosphere Gas\":\"Oxygen\",\"Attenuator(mm)\":\"1thin\",\"Focus(mm)\":\"130\",\"Frequency(Hz)\":\"10\",\"Laser Energy(mJ)\":\"100\",\"Laser Voltage(kV)\":\"19.5\",\"Measured Energy Mean(mJ)\":\"100\",\"Measured Energy Std(mJ)\":\"2.84mJ\",\"Pre-Frequency(Hz)\":\"20\",\"Pre-Gas Atmosphere\":\"Oxygen\",\"Pre-Pressure(mTorr)\":\"200\",\"Pre-Pulses\":\"1500\",\"Pre-Temperature(\302\260C)\":\"700\",\"Pressure(mTorr)\":\"200\",\"Pulses\":\"3500\",\"Target Height(mm)\":\"128.4\",\"Target Material\":\"SrRuO3(Berkeley_new)\",\"Temperature(\302\260C)\":\"700\"}}" source: "f1507eb2-9b44-11ec-bf8a-ab28bf5d96bb/C/Image/YG044_Yichen_Guo_07172022.h5" repo_id: "repo/lehigh-nhi" size: 1832.0 ext: ".h5" ext_auto: true ct: 1658097066 ut: 1658097072 dt: 1658097072 owner: "p/2022_pld_plume_recording" creator: "u/yig319" notes: 0
DOI: 10.5281/zenodo.6974945
title: Test DataFed Upload-5

description: Test DataFed Upload
description: id: "d/403447813" title: "YG044_Yichen_Guo_07172022" metadata: "{\"header\":{\"Base Pressure (Torr)\":\"8e-5\",\"Chamber\":\"Laser 1C\",\"Cool Down Atmosphere\":\"Oxygen\",\"Date\":\"07/17/2022\",\"Growth ID\":\"YG044\",\"Notes\":\"5th continuous growth set - 3rd sample (treated_set_2 - s3)\",\"Path\":\"C:/Image/\",\"Substrate_1\":\"SrTiO3\",\"User Name\":\"Yichen_Guo\",\"time\":\"14:56:34\"},\"target_1\":{\"Aperture\":\"12.5\",\"Atmosphere Gas\":\"Oxygen\",\"Attenuator(mm)\":\"1thin\",\"Focus(mm)\":\"130\",\"Frequency(Hz)\":\"10\",\"Laser Energy(mJ)\":\"100\",\"Laser Voltage(kV)\":\"19.5\",\"Measured Energy Mean(mJ)\":\"100\",\"Measured Energy Std(mJ)\":\"2.84mJ\",\"Pre-Frequency(Hz)\":\"20\",\"Pre-Gas Atmosphere\":\"Oxygen\",\"Pre-Pressure(mTorr)\":\"200\",\"Pre-Pulses\":\"1500\",\"Pre-Temperature(\302\260C)\":\"700\",\"Pressure(mTorr)\":\"200\",\"Pulses\":\"3500\",\"Target Height(mm)\":\"128.4\",\"Target Material\":\"SrRuO3(Berkeley_new)\",\"Temperature(\302\260C)\":\"700\"}}" source: "f1507eb2-9b44-11ec-bf8a-ab28bf5d96bb/C/Image/YG044_Yichen_Guo_07172022.h5" repo_id: "repo/lehigh-nhi" size: 1832.0 ext: ".h5" ext_auto: true ct: 1658097066 ut: 1658097072 dt: 1658097072 owner: "p/2022_pld_plume_recording" creator: "u/yig319" notes: 0
DOI: 10.5517/ccdc.csd.cc26qh9r
title: CCDC 2047404: Experimental Crystal Structure Determination

description: Related Article: Xiu-Zhen Shi, Ai-Li Guo, Kai Fu, Yan He, Qin Li, Qiong Luo|2022|Indian J.Chem.|61|37|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28hzqh
title: CCDC 2100737: Experimental Crystal Structure Determination

description: Related Article: Xiaoqian Lv, Na Liu, Zhanru Yin|2022|Indian J.Chem.|61|771|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bkgr4
title: CCDC 2161746: Experimental Crystal Structure Determination

description: Related Article: Paola Marzullo, Alice Maiocchi, Giuseppe Paladino, Umberto Ciriello, Leonardo Lo Presti, Daniele Passarella|2022|Eur.J.Org.Chem.|2022|e202200392|doi:10.1002/ejoc.202200392
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34740/kaggle/dsv/4045346
title: Elon Musk Tweets
DOI: 10.5066/p9tk8kmm
title: Airborne horizontal magnetic gradient and radiometric survey over parts of southeast Missouri, southern Illinois and western Kentucky - The Gap survey, 2021

description: This publication provides digital flight line data for a high-resolution horizontal magnetic gradient and radiometric survey over an area of southeast Missouri, southern Illinois, and western Kentucky. The survey was conducted in collaboration with the Missouri Geological Survey as part of the U.S. Geological Survey (USGS) Earth Mapping Resource Initiative (Earth MRI) effort (Day, 2019). Earth MRI is a cooperative effort between the USGS, the Association of American State Geologists, and other Federal, State, and private sector organizations to improve our knowledge of the geologic framework of the United States. Data for this survey were collected by Terraquest, Ltd. under contract with the USGS using a fixed-wing aircraft with magnetometers [...]
DOI: 10.48567/28vq-fk81
title: AGO-4 Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/5rv6-z407
title: Austin Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/pb8v-ce37
title: Baldrick Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/kzzq-q923
title: Bear Peninsula Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/4xja-rw56
title: Bonaparte Point Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/59ms-yg82
title: Byrd Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/e17c-fg20
title: Cape Bird Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/m5zm-sb09
title: Cape Hallett Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/9mmv-rm51
title: D-10 Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/1nwa-2008
title: D-47 Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/mn3a-6134
title: D-85 Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/6pbc-jn89
title: Dome C II Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/5vg8-r107
title: Dome Fuji Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/w02q-pq93
title: Elaine Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/wxrp-cs32
title: Elizabeth Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/z6ej-fk11
title: Emilia Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/bkd6-2r64
title: Emma Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/yzgx-ec35
title: Erin Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/cdyr-k888
title: Evans Knoll Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/sbvb-a318
title: Ferrell Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/4mkh-w681
title: Gill Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/23wr-t616
title: Harry Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/qcpf-xh17
title: Henry Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/m6e9-nb75
title: Hugo Island Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/rkc9-gw89
title: JASE2007 Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/b589-9p58
title: Janet Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/p14x-ef64
title: Kathie Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/mz4w-a491
title: Kominko-Slade Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/sr0v-ta94
title: Laurie II Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/znq1-2b82
title: Lettau Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/wyrh-an58
title: Linda Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/6tbt-aa05
title: Lorne Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/hgbt-gv12
title: Manuela Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/gnt7-yk59
title: Marble Point Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/a2ab-5437
title: Marble Point II Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/5bbj-nx64
title: Margaret Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/89g5-gm91
title: Marilyn Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/sj3b-8x68
title: Minna Bluff Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/6aqj-3514
title: Mizuho Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/y1cf-c042
title: Mount Siple Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/ggy5-t645
title: Nico Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/6q3p-s708
title: PANDA-South Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/3aq9-eg74
title: Pegasus North Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/5vqw-0h80
title: Port Martin Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/05dr-6514
title: Possession Island Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/znv0-mq54
title: Relay Station Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/sfk3-qd17
title: Sabrina Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/tpze-vv83
title: Schwerdtfeger Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/ejh5-q862
title: Siple Dome Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/tm0m-wc41
title: Theresa Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/mbqp-yh05
title: Thurston Island Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/tep3-yy62
title: Vito Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/vv67-pg05
title: White Island Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/pvdb-t727
title: Whitlock Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/cya7-pw41
title: Willie Field Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/nsbe-ya40
title: Windless Bight Automatic Weather Station, 2016 Reader format three-hour observational data.
DOI: 10.48567/phwt-sd23
title: AGO-4 Automatic Weather Station, 2015 Reader format three-hour observational data.