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2017 seasonal high frequency monitoring of Upper Clark Fork River (Montana, USA) dissolved carbon dioxide, pH, and electrical conductivity

These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river metabolic regime to the floodplain restoration via inference of whole-ecosystem metabolism from the daily variation in carbon dioxide concentrations. Data are primarily measurements of dissolved carbon dioxide, pH, electrical conductivity, and temperature in well-mixed river thalweg water. Additional miscellaneous data were collected for quality control. Data are from late in the 2017 field season. Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at the Galen and Racetrack sites northeast of Anaconda, Montana, USA. High frequency carbon dioxide and pH data were collected using field deployments SAMI sensors from Sunburst Sensors (Missoula, Montana, USA). Electrical conductivity data was collected with a HOBO sensor from Onset Computer Corporation (Bourne, Massachusetts, USA).

Water year 2019 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)

These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at 13 project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.

Water year 2017-18 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)

These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2017 and 2018 water years (1 Oct 2016 to 30 Sep 2018). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.

Water year 2020 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)

These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2020 water year (1 Oct 2019 to 30 Sep 2020). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.

opencc-by-4.0Sep 2009View details →

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Titration_DB: NMR-monitored protein pH titration chemical shift data.

This archive contains a backup of the NMR titration_db database raw chemical shift data. Reference: D. Farrell et al., “Titration_DB: Storage and analysis of NMR-monitored protein pH titration curves.,” Proteins, vol. 78, no. 4, pp. 843–857, 2009. This data was formerly hosted on an interactive website which is no longer active. The files are stored as csv format, one per protein. They are also split into three folders according to the nuclei whose chemical shifts were used to detect the measurement (1H,13C or 15 N). Much of this data was obtained from literature. A table in the zip file (titration_dataset_info.csv) associates the protein name with the original paper.  Note that the database stored the pka values fit using a model to each residue dataset. This is not stored here. Also present is a file titdb.fs. This is a binary file that can only be opened using the PEATDB software. PEATDB (Protein Engineering Analysis Tool - Database) was an application for sharing, analysing and storing experimental and theoretical data from Protein Engineering experiments. This is now legacy software and no longer maintained. It was used to allow interactive fitting of the titration data using the Ekin module. It is still possible to install this software if you really need to. It is stored on github at https://github.com/dmnfarrell/peat. D. Farrell May 2019

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Data from: Monitoring microarthropods assemblages along a pH gradient in a forest soil over a 60 years' time period

The goal of this study was to assess the development, over 60 years, of microarthropod communities over a pH gradient in forest soil. Site Description Hackfort is an oak coppice grove in the East-Southeast of the city of Zutphen in the province of Gelderland, the Netherlands, 52°06′09.7″ N, 6°15′56.0″ E (see Figure 1). The experimental area is about 1.5 ha and is divided in a 10 m × 10 m grid. Vegetation is dominated by common oak (Quercus robur), mixed with birch (Betula pendula), and had in 1959, an understory of wood sage plugs (Teucrium scorodonia), wood anemone (Anemone nemorosa), bracken (Pteridium aquilinum), and wavy-hair grass (Deschampsia flexuosa). In later years, the understory became more dominated by bramble species (Rubus fruticosus and R. idaeus) and common nettles (Urtica dioica) at the edges of the forest, due to increased N deposition from adjacent farmland. The forest is situated at the transition from western riverine deposits and eastern periglacial cover sands. The soil is a riverine deposit with a few elevation differences, making a number of gradients in clay and loam content, which results in many short-distance gradients in soil types, varying from typic haplaquolls with the largest loam contents, via psammaquentic haplorthods to humaqueptic spodic psammaquents, slightly elevated and low in loam contents. Microarthropod Sampling and pH Measurement In 1959, samples were taken at three subsequent dates: 11 September, 9 October, and 30 October. Samples in 1987 were taken on one date, 9 October, just as on 30 October 2019. Samples were taken following a standard procedure, developed at the Institute for Applied Biological Research in Nature, Wageningen, the Netherlands (later merged into the Research Institute for Nature management, Institute for Forestry and Nature Research and Alterra resp., now known as Wageningen Environmental Research); this procedure has been published by Siepel and van de Bund in 1988 (Siepel and van de Bund, 1988). Each mineral soil sample has 100 cc: a volume of 5 cm diameter and 5 cm depth plus litter on top. In 1959, two samples per date were taken on each plot, making a total of 6 samples (only pooled data are available); in 1987 and in 2019, 4 and 5 samples for each plot were taken on, respectively (data per sample available). Soil cores were put on a Tullgren funnel for 1 week, during which temperature was increased from 35 to 45 °C, and then, microarthropods were collected in 70% alcohol and later put into 20% lactic acid for clarification and identification (Siepel, 1990; Siepel and van de Bund, 1988). The Tullgren funnel used for extraction (Siepel, 1990) has been used ever since 1936 and efficiency has not changed as the tool and protocol was the same all over the years. Identification was done to the species level as much as possible using at present the keys for Oribatida(Weigmann and G., 2006), for Gamasina (Lehtinen, 1994), for Uropodina (Karg, 1989), and for Collembola (Hopkin, 2007). Material from the extractions of 1959 and 1987 was re-examined as far as possible to check the correct species identification. In the 1959 and 1987 samples, only oribatid mites were identified to the species level, whereas in 1959, all species of Quadroppiidae, Oppiidae, and Suctobelbidae were pooled. In 2019, all microarthropods were identified to the species level. Sorting and identification of the 1959 microarthropods was carried out by an experienced acarologist (J.G. de Gunst), in 1987, this was done by a student (C. Arnold) and completed and checked by the second author. For the 2019 samples, we decided to demonstrate the potential difference in picking out the microarthropods from the extraction fluid into the slides for identification as part of the experiment: the first author made a first series of slides including all distinguished animals (dataset 2019 a), while the second author made an extra set of slides with the animals missed by the first (dataset 2019 b). The first author did know since the beginning that the second author would check all samples after her sorting session. In this way, we intended to demonstrate the potential difference in this crucial part of the procedure by a starting and an experienced professional. In the analysis, we compare dataset (2019 a) with (2019 a + b), in order to highlight the difference between a starting and an experienced acarologist. Nomenclature adopted was updated according to current standards, following, e.g., the checklists for Oribatida (Siepel et al., 2009), for Astigmatina (Siepel et al., 2016), and for Mesostigmata (Siepel, 2018). Values of pH-KCl were measured in the core material after the extraction of the microarthropods, both in 1959, 1987, and 2019.   We have four data files: 1959 hackfort microarthropods data.csv 1989 hackfort microarthropods data.csv 2019 hackfort microarthropods data.csv pH data Hackfort 1959-2019.csv.   Explanation of the variables in the datasets: higher taxon: Oribatida, Astigmata, Mesostigmata, Prostigmata, Collembola or Protura Name in De Gunst 1959: taxonomic identification by De Gunst in 1959 Valid name: Henk Siepel re-checked these species names in 2019 Plot: plot 1, plot 2, plot 3, plot 4, plot 5 a: identified by Yuxi Guo b: re-checked by Henk Siepel from remaining soil microarthropods in slide pH(KCL) and pH(H2O): pH values based on indicated methods

opencc-by-4.0Nov 2021View details →

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Data for: Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors

Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89 to 91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.

opencc-zeroDec 2018View details →

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Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy - Raw Data

Raw data for publication Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy

opencc-by-nc-sa-4.0Jun 2023View details →

Normal Values of Ambulatory 24-hour Oesophageal pH-impedance Monitoring in an Indian Cohort of Participants

ClinicalTrials.gov study NCT06928350. IPD Sharing: NO. Countries: 1. Publications: 4.

Continuous Supraglottic pH Monitoring in Prolonged Intubated Intensive Care Patients and High Risk Aspiration Intraoperative Patients

ClinicalTrials.gov study NCT02604043. IPD Sharing: Not stated. Countries: 1. Publications: 2.

Twenty-Four Hour Combined Multi-Channel Impedance and pH Ambulatory Monitoring: Impedance Reflux Episodes of Patients On and Off Proton Pump Inhibitor Therapy

ClinicalTrials.gov study NCT01710800. IPD Sharing: Not stated. Countries: 0. Publications: 1.

The Effect of Monitoring PVP on Clinical Outcomes in Patients With PH

ClinicalTrials.gov study NCT04820166. IPD Sharing: Not stated. Countries: 1. Publications: 2.

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A Study of E3810 With Pre- and Post-Treatment pH Monitoring in Patients With Non-Erosive Gastroesophageal Reflux Disease

ClinicalTrials.gov study NCT00165672. IPD Sharing: Not stated. Countries: 1. Publications: 0.

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A Study to Monitor Intragastric pH in Patients Taking Rabeprazole vs. Patients Taking Pantoprazole

ClinicalTrials.gov study NCT00378287. IPD Sharing: Not stated. Countries: 0. Publications: 1.

Real-time Soft Tissue Assessment Using a Technologically Advanced pH Monitoring System

ClinicalTrials.gov study NCT04725604. IPD Sharing: NO. Countries: 1. Publications: 0.

Correlations Between Wireless pH Monitoring and Variation in Reflux Symptom Questionnaire, 7 Day Recall

ClinicalTrials.gov study NCT03407456. IPD Sharing: NO. Countries: 1. Publications: 0.

Nasopharyngeal 24 Hour pH Monitoring in Health Adult Volunteers

ClinicalTrials.gov study NCT00584675. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Development of Applications of the Bravo® pH Monitoring System and Evaluation of Its Performance

ClinicalTrials.gov study NCT01503476. IPD Sharing: Not stated. Countries: 1. Publications: 0.

This Study Evaluates the Superiority of Daily Self-pH Monitorization of Lit-control®pH Meter Compared to the Monitorization of Reactive Strips (Standard of Care).

ClinicalTrials.gov study NCT03539926. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Pre and Post-gastrostomy Evaluation of GER in Children Using 24-hour pH Monitoring

ClinicalTrials.gov study NCT04454112. IPD Sharing: NO. Countries: 1. Publications: 0.