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opencc-by-4.0Feb 2017View details →

Individual work of Bachelor students monitored using a dynamic assessment dashboard

This document explain how data were generated and how to interpret them. LICENSE: CC0 But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license. Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available. HOW DATA WERE COLLECTED Data provided in this dataset were collected in Fall 2016 as part of the Bachelor course 'Formation des usagers en bibliothèques' taught at the University of applied sciences Geneva (Information Sciences Department). Data were generated by the interaction of the students with DAD, the Dynamic assessment dashboard, software to be released in 2017 (https://github.com/grolimur/DAD). Only data related to individual activities are published in this set. They were then cleaned and anonymised. The activities.csv file is provided for information purpose. Data are provided in 2 formats: * Comma-spearated values (submissions.csv) * SQLite (submissions.sqlite) CSV is not correctly read by Excel, it's recommended to convert it into an .xslx file before using it. SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/). CODEBOOK Here is the name, the meaning and the possible values of the columns (name - description [possible values]). ID - database row ID [(database unique row ID)] ActivityID - unique activity ID [1, 2, 3, 4, 10, 14, 16, 21, 22, 23, 41] StudentID - unique anonymous student ID [#50 to #72] date - recorded by the database [timestamp] year - year extracted from date [2016, 2017] month - month extracted from date [01, 11, 12] day - day extracted from date [1 to 31] weekday - weekday calculated from date [1, 2, 3, 4, 5, 6, 7] hour - hour extracted from date - 00 to 23] minute - minute extracted from date [00 to 59] second - second extracted from date [00 to 59] gr - group activity or not [0] validated - was the submission validated or not [-1 or 1] week - week number [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, H1, H2] ActivityID meaning Activities are described in activities.csv. This file conatins only individual activities description. The 2 activities iwth the bonus particularity are not present in the data because students didn't need to apply for them. A bonus was automatically added when activity 1 AND 2 AND 3 AND 4 (bonus = activity 5) as well as when activities 21 AND 22 AND 23 (activity 24) were completed. Date format The date is formatted like YYYY-MM-DD hh:mm:ss. Weekday meaning 1 stands for Monday, 2 for Tuesday and so on until 7 for Sunday. Group meaning 0 stands for 'individual activity' and 1 would stand for group activity'. Only entries with gr="0" appears in this dataset. Validated meaning -1 stands for 'rejected' and 1 for 'validated'. Week meaning The course lasts 10 weeks. 1 stands for the 1st week and so on until 10 that stands for the 10th week. H1 and H2 stand for the Christmas holiday weeks that took place between week 8 and 9. No submission were sent in weeks 1, 2 and 10.

opencc-by-4.0Feb 2017View details →

Data and results for manuscript "Flow dynamics in hyper-saline aquifers: hydro-geophysical monitoring and modeling"

The paper presents a general methodology that will help understand how freshwater and saltwater may interact in natural porous media, with a particular view at practical applications such as the storage of freshwater underground in critical areas such as semi-arid zones around the Mediterranean sea. The methodology is applied to a case study in Sardinia and shows how a mix of advanced monitoring and mathematical modeling tremendously advance our understanding of these systems. This package contains the raw cross-hole time-lapse ERT data, additional field data, the ERT inversion results of the field data as well as the modeling data in terms of the concentration distribution of the density-dependent flow and transport model and the inverted synthetic ERT monitoring results.

opencc-by-4.0Aug 2023View details →

GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022

<div> GLC_FCS30D is the first global fine land cover dynamic product at a 30-meter resolution that adopts continuous change detection. It utilizes a refined classification system containing 35 land-cover categories and covers the time span from 1985 to 2022. Before the year 2000, the update cycle was every 5 years, while after 2000, it is updated annually. In specific, it developed by combining the continuous change detection method, local adaptive updating models and the spatiotemporal optimization algorithm from dense time-series Landsat imagery, and was validated to achieve an overall accuracy of 80.88% (±0.27%) for the basic classification system 10 major land-cover types) and 73.24% (±0.30%) for the LCCS level-1 validation system (17 LCCS land-cover types). The GLC_FCS30D has been compressed into 36 zip files, and the details about the GLC_FCS30D can be found in the User's guides. </div>

opencc-by-4.0Dec 2023View details →

Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2) in Structure and dynamics of the taxocenes of shrews in different habitats of the Norsky nature reserve

Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2)

opencc-by-4.0Jan 2018View details →

Data and results for manuscript "Imaging groundwater infiltration dynamics in karst vadose zone with long-term ERT monitoring"

This data set contains raw and inverted data from an Electrical Resistivity Tomography (ERT) monitoring experiment conducted over a period of three years at the Rochefort Cave Laboratory (RCL) site in South Belgium. It highlights variable hydrodynamics in the karst vadose zone of Lorette Cave. More conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) are also included in the package, which aims at provide a thorough understanding of the groundwater infiltration. Seasonal changes affect all the imaged areas leading to increases in resistivity in spring/summer attributed to enhanced evapotranspiration, whereas winter is characterised by a general decrease in resistivity associated with a groundwater recharge of the vadose zone. This study provides detailed images of the sources of drip discharge spots traditionally monitored in caves and aims to support modelling approaches of karst hydrological processes.

opencc-by-4.0Jan 2016View details →

Figure 10. - Different fish abundance dynamic profiles between 1983 and 2014 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia

Figure 10. - Different fish abundance dynamic profiles between 1983 and 2014 on the outer slope at Tiahura sector in Moorea.

opencc-zeroOct 2020View details →

Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances

Disturbances play a key role in driving forest ecosystem dynamics, but how disturbances shape wildlife habitat across space and time often remains unclear. A major reason for this is a lack of information about changes in habitat suitability across large areas and longer time periods. Here, we use a novel approach based on Landsat satellite image time series to map seasonal habitat suitability annually from 1986 to 2017. Our approach involves characterizing forest disturbance dynamics using Landsat-based metrics, harmonizing these metrics through a temporal segmentation algorithm, and then using them together with GPS telemetry data in habitat models. We apply this framework to assess how natural forest disturbances and post-disturbance salvage logging affect habitat suitability for two ungulates, roe deer (Capreolus capreolus) and red deer (Cervus elaphus), over 32 years in a Central European forest landscape. We found that red and roe deer differed in their response to forest disturbances. Habitat suitability for red deer consistently improved after disturbances, whereas the suitability of disturbed sites was more variable for roe deer depending on season (lower during winter than summer) and disturbance agent (lower in windthrow versus bark-beetle-affected stands). Salvage logging altered the suitability of bark beetle-affected stands for deer, having negative effects on red deer and mixed effects on roe deer, but generally did not have clear effects on habitat suitability in windthrows. Our results highlight long-lasting legacy effects of forest disturbances on deer habitat. For example, bark beetle disturbances improved red deer habitat suitability for at least 25 years. The duration of disturbance impacts generally increased with elevation. Methodologically, our approach proved effective for improving the robustness of habitat reconstructions from Landsat time series: integrating multi-year telemetry data into single, multi-temporal habitat models improved model transferability in time. Likewise, temporally segmenting the Landsat-based metrics increased the temporal consistency of our habitat suitability maps. As the frequency of natural forest disturbances is increasing across the globe, their impacts on wildlife habitat should be considered in wildlife and forest management. Our approach offers a widely applicable method for monitoring habitat suitability changes caused by landscape dynamics such as forest disturbance

opencc-zeroOct 2022View details →

Data from high throughput SNP-chip as cost effective new monitoring tool for assessing invasion dynamics in the comb jelly Mnemiopsis leidyi

High throughput low-density SNP arrays provide a cost-effective solution for population genetic studies and monitoring of genetic diversity as well as population structure commonly implemented in real time stock assessment of fish species. However, the application of high throughput SNP arrays for monitoring of invasive species has so far not been implemented. We developed a species-specific SNP array for the invasive comb jelly Mnemiopsis leidyi based on whole genome resequencing data. Initially, a total of 1,395 high quality SNPs were identified using stringent filtering criteria. From those, 192 assays were designed and validated, resulting in the final panel of 116 SNPs. Markers were diagnostic between the northern and southern M. leidyi lineages and highly polymorphic to distinguish populations. Despite using a reduced representation of the genome, our SNP panel yielded comparable results to using a whole genome resequencing approach (832,323 SNPs), recovering similar values of genetic differentiation between samples and detecting the same clustering groups when performing Structure analyses. The resource presented here provides a cost-effective, high throughput solution for population genetic studies, allowing to routinely genotype large number of individuals. Monitoring of genetic diversity and effective population size estimations in this highly invasive species will allow for the early detection of new introductions from distant source regions or hybridization events. Thereby, this SNP chip represents an important management tool in order to understand invasion dynamics and opens the door for implementing such methods for a wider range of alien invasive species. <div></div>

opencc-by-4.0Apr 2019View details →

Ion-Pair Dynamics upon Photoinduced Electron Transfer Monitored by Pump-Pump-Probe Spectroscopy

The files contain all the data that are shown in the figures of the main text and of the supporting information of the article: Beckwith, J.; Lang, B.; Grilj, J.; Vauthey, E. Ion-Pair Dynamics upon Photoinduced Electron Transfer Monitored by Pump-Pump-Probe Spectroscopy. J. Phys. Chem. Lett. 10 (2019), 10.1021/acs.jpclett.9b01431

opencc-by-4.0Sep 2024View details →

Data for "Entanglement Dynamics in Monitored Kitaev Circuits: Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling"

We provide the data and scripts used to produce the figures shown in our publication "Entanglement Dynamics in Monitored Kitaev Circuits: Loop Models, Symmetry Classification, and Quantum Lifshitz Scaling".

opencc-zeroNov 2022View details →

Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips

This data accompanies the study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" published in (Nature) Communications Engineering. This paper focuses on using large and inexpsensive datasets for obtaining information on the dynamics of bridges. In this study, data is collected by smartphones in moving vehicles as the cross over a bridge, in three distinct applications. Smartphone data was collected in controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and an analytical method was developed to accurately recover modal properties. The method was also successfully applied to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. The results suggest that larve and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure. The data provided includes the source data for the figures in the publication as well as the "controlled data" referenced in the study.

opencc-zeroDec 2022View details →

NDVI-based monitoring of long-term vegetation dynamics and responses to multi-time scales droughts in Inner Mongolia

The characteristics of vegetation and drought for different seasons between 1982 and 2015 in Inner Mongolia were studied based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The response of vegetation to drought over various time scales for different seasons and vegetation types was investigated using the maximum Pearson correlation, allowing a discussion about the possible causes of any changes. The results indicate that the vegetation NDVI in Inner Mongolia showed an increasing trend in different seasons, with spring vegetation NDVI (April to May) having the largest significant increasing rate, followed by the growing season (April to October), autumn (September to October), and summer (June to August). Accordingly, the proportion of stations with decreasing SPEI was, in descending order, summer, growing season, autumn, and spring. Additionally, the magnitude of the SPEI decrease was greater in eastern Inner Mongolia. NDVI and SPEI were positively correlated in most regions of Inner Mongolia, indicating that changes in vegetation in most parts of this region were affected by the spatial and temporal characteristics of drought, the correlation being them being strongest in the growing season, followed by summer, then spring and autumn. Considering the different types of vegetation, forests were less affected by drought, with broadleaf forests more affected than coniferous forests. The meadow steppes and typical steppes were more affected by 12-month droughts in the growing season and summer, 6-month droughts in spring, and 3-month droughts in autumn, with desert steppes mainly affected by 3-month droughts. The shrubs, sandy vegetation, and cropland were mostly affected by droughts in summer, and show a greater response to 3-month droughts in autumn. Finally, the water balance was found to be the most important factor affecting the response of vegetation to drought in Inner Mongolia.

opencc-by-4.0Jan 2020View details →

Datasets of the article "Femtosecond-to-nanosecond dynamics of flavin mononucleotide monitored by stimulated Raman spectroscopy and simulations"

Experimental time-resolved femtosecond-stimulated Raman spectra (FSRS) and visible transient absorption (visTA) spectra of flavin mononucleotide (FMN) in water, MES pH=6, and TRIS pH=8  buffers (DATA files). The dataset includes also the amplitudes and evolution-associated difference spectra (EADS) arising from global kinetic analysis using a sequential model.  In the DATA files, the first column contains the Stokes shift (in cm-1) or wavelength (in nm), and the first row contains the time delays (in seconds). In the AMPLITUDES files, the first column contains the time delays (in seconds), and the first row contains the lifetimes (in seconds). In the EADS files, the first column contains the wavenumbers (in cm-1) or wavelength (in nm), and the first row contains the lifetimes (in seconds). Note that the FSRS spectra have been normalized to 1. If you use these datasets please cite: Andrikopoulos, P. C.; Liu, Y.; Picchiotti, A.; Lenngren, N.; Kloz, M.; Chaudhari, A. S.; Precek, M.; Rebarz, M.; Andreasson, J.; Hajdu, J.; Schneider, B.; Fuertes, G.* Femtosecond-to-Nanosecond Dynamics of Flavin Mononucleotide Monitored by Stimulated Raman Spectroscopy and Simulations. Phys. Chem. Chem. Phys. PCCP 2020, 22, 6538–6552.

opencc-byAug 2023View details →

Population dynamics and seasonal migration patterns of Spodoptera exigua in northern China based on 11 years of monitoring data

The beet armyworm, Spodoptera exigua (Hübner) is an important migratory pest worldwide that has caused serious economic losses in the main crop-producing areas of China. To effectively monitor and control this pest, it is necessary to investigate its interannual and seasonal migration patterns in northern China. In this study, we weekly monitored the population dynamics of S. exigua using sex pheromone traps in Shenyang, Liaoning Province from 2012 to 2022 and simulated the migration trajectories using the HYSPLIT model. Overall, the migration numbers varied significantly among years, with large migrations in 2018 and 2020 that resulted in a total catch of more than 2000 individuals.

ClinicalTrials.gov36/100

Dynamic Data-Driven Management of Atrial Fibrillation Using Implantable Cardiac Monitors: The MONITOR-AF Study

ClinicalTrials.gov study NCT06352060. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

publicOct 2022View details →

Data from high throughput SNP-chip as cost effective new monitoring tool for assessing invasion dynamics in the comb jelly Mnemiopsis leidyi

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publicMay 2025View details →

Data from:Climate-specific dynamics of fall armyworm on maize: Implications for pest monitoring and management

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publicOct 2020View details →

Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances

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