Find research datasets worth reusing

opencc-zeroJun 2023View details →

Large-scale restoration of species-rich dry grasslands on arable land: Environmental filtering drives successful species establishment over a period of 10 years

Sowing of regionally specific seed mixtures on former arable land is increasingly used to restore species-rich grasslands, but it often faces important obstacles in environmental conditions and colonisation from preserved surrounding grasslands. Long-term landscape scale studies, which explore the processes governing plant establishment from seed mixtures and later colonisation from the surrounding landscape are scarce. We studied the species and functional trait composition of 32 grasslands restored 1–11 years before the first sampling by sowing regional seed mixtures in the White Carpathian Mts (SE Czech Republic). We compared them with 23 well-preserved permanent grasslands in their surroundings. In each grassland we estimated plant species cover in three plots of 25 m2, in restored grasslands in 2009, 2014 and 2019, in permanent grasslands in 2009. The species composition of the restored grasslands converged towards the permanent grasslands due to expansion of dry grassland species and retreat of mesic grassland and weed species. The majority (60–71%) of the vegetation cover of restored grasslands was formed by sown species, while the species number was mainly determined by colonising unsown species (61–63%). The species number of restored grasslands remained lower than that in permanent grasslands. Functional trait composition (Community Weighted Mean) of the sown seed mixtures, permanent grasslands and restored grasslands overlapped in all three samplings. With increasing grassland age, species with resource-retaining traits of low specific leaf area and high leaf dry matter content, lower stature, higher seed mass, and lower capacity for clonal reproduction expanded. Functional trait diversity of the plots was mostly lower than expected by null model randomisations of community, which indicates strong environmental filtering. Comparison of newly colonising species and extinct species indicated that mainly species with traits similar to the rest of the community are able to colonise the grasslands successfully. Synthesis and applications. Sowing of regionally specific seed mixtures was successful and demonstrated to be a fast way of grassland restoration, particularly in terms of functional trait composition. The resulting species diversity depended on the colonisation processes, which are controlled by the functional composition of the local communities.

opencc-zeroJun 2023View details →

Large-scale changes in macrobenthic biodiversity driven by mangrove afforestation

<ol> <li>Large-scale anthropogenic mangroves have been constructed in coastal regions worldwide, but our understanding of their ecological effects is limited. In particular, the question of whether and how anthropogenic mangroves influence biodiversity patterns remains elusive.</li> <li>Here, we investigated the influence of large-scale anthropogenic mangroves on biodiversity patterns of mangrove macrobenthos. Specifically, we measure and seek to explain differences in species richness, abundance, assemblage composition, and distance-decay effect before and after the construction of anthropogenic mangroves.</li> <li>We surveyed assemblages of gastropod, bivalve and crab species over a wide latitudinal extent (24–28°N) in subtropical China. For each, we calculated species richness, abundance, assemblage composition, and distance-decay relationship before and after the construction of anthropogenic mangroves.</li> <li> After the large-scale anthropogenic mangroves, we found species richness of gastropods, bivalves and crabs increased by 23.81%, 100% and 20%, respectively. The distance-decay effects of gastropods and bivalves decreased by 25% and 91.43%, while that of crabs remained virtually unchanged, indicating that large-scale monospecific planting changed spatial processes of macrobenthos (e.g., dispersal limitation). With mangrove plantation, compositional similarity of crab and bivalve assemblages increased by 28.57% and 38.46%, suggesting that large-scale monospecific planting exacerbates biotic homogenization. Altogether, these results indicate that large-scale anthropogenic habitats increase the diversity of mangrove macrobenthos and change taxonomic compositions by reducing distance-decay effects. </li> <li> Synthesis and applications. We emphasize that afforestation of coastal wetlands can drive major changes in benthonic communities. Monitoring and assessing the ecological effects of the anthropogenic habitats for the presence of functional faunas will be important in determining the future coastal restoration and maintaining economic aquaculture. Quantifying those effects in terms of regional biodiversity composition enables management decisions about habitat restoration to be based upon macro-evidence rather than wishful thinking or untested intuition even or a one-sided local perspective. </li> </ol>

opencc-zeroJun 2023View details →

Large-scale exploration of nitrogen utilization efficiency in Asia region for rice crop: variation patterns and determinants

Improving rice nitrogen utilization efficiency (NUtE) is imperative to maximizing future food productivity while minimizing environmental threats, yet knowledge of its variation and the underlying regulatory factors is still lacking. Here, we integrated a dataset with 21,571 data compiled by available data from peer-reviewed literature and a large-scale field survey to address this knowledge gap. The overall results revealed great variations in rice NUtE, which were mainly associated with human activities, climate conditions and rice variety. Specifically, N supply rate, temperature and precipitation were the foremost determinants of rice NUtE, and NUtE responses to climatic change differed among rice varieties. Further prediction highlighted the improved rice NUtE with the increasing latitude or longitude. The indica and hybrid rice exhibited higher NUtE in low-latitude regions compared to japonica and inbred rice, respectively. Collectively, our results evaluated the primary drivers of rice NUtE variations and predicted the geographical responses of NUtE in different varieties. Linking the global variations in rice NUtE with environmental factors and geographical adaptability provides valuable agronomic and ecological insights into the regulation of rice NUtE.

opencc-by-4.0Jul 2023View details →

1D-1V Vlasov-Poisson Simulations of Mutual Impedance Experiments in the presence of small-scale and large-scale plasma inhomogeneities - PART 3

PART 1 IS FOUND AT  https://doi.org/10.5281/zenodo.8118023 PART 2 IS FOUND AT  https://doi.org/10.5281/zenodo.8119090   ===================================================================================================== Author        :     L. Bucciantini Date         :    05/07/2023 Laboratory    :    CNRS-LPC2E, Orléans (France) ===================================================================================================== Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will find in it. In case you need help with using the dataset, or if you are interested in mutual impedance experiments, do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu) ===================================================================================================== Topic: This dataset contains the outputs of numerical simulations performed to assess the impact of plasma inhomogeneities on the diagnostic performance of mutual impedance experiments. Numerical model: The outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson system of equations. The scheme used to solve the model is the one developed by Mangeney et al. (2002),  A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071). The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind, Journal of Geophysical Research (Space Physics), 115, 6106 (2010) ----------------------------------------------------------------------------------------------------- What is inside the dataset: The dataset is composed of 7 different mutual impedance measurements, each corresponding to one directory (see below). The measurements (i.e. directory) correspond to small-scale plasma inhomogeneities at different positions with respect to the mutual impedance antennas, or to a large-scale plasma inhomogeneity. Each directory contains a number of folders. Each folder corresponds to the emission of a signal at different frequency. ----------------------------------------------------------------------------------------------------- List of directories: (Note : each directory corresponds to one mutual impedance measurement) L        :    These outputs correspond to one mutual impedance measurement in                 small antenna emission amplitude, which corresponds to a linear                 plasma response to the emission. s_xx     :    Each of these outputs corresponds to one mutual impedance measurement in correspondance of         a small-scale plasma inhomogeneity at xx Debye lengths of distance from the emitting antenna          ----------------------------------------------------------------------------------------------------- List of folders inside the directories: Folders begin with the name "000" and have increasing index. Inside the same directory, each folder represents a different simulation used to build the same mutual impedance measurement. ------------------------------------------------------------------------------------------------------ List of files inside the folders: density_e.npz        :    electron density inside the box, in function of time (tempo) density_p.npz        :    ion density inside the box, in function of time (tempo) E.npz            :    electric field in the box, in function of time (tempo) qrho.npz        :    electric potential in the box, in function of time (tempo) qrho_imposed.npz    :    electric charge imposed at the emitting antennas, in function of time (tempo) tempo.npz        :    time-vector for density, electric field, electric potential and charge vectors TEST_Luca.dat        :    parameters describing the characteristics of the simulated plasma box [ Note : the previous files can be opened as follows import numpy as np vector_file_name = np.load('file_name.npz') # Use these for the .npz files characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1) ] ------------------------------------------------------------------------------------------------------- Characteristics of the simulated plasma box (TEST_Luca.dat) nx        :    amount of spatial grid points xl        :    physical size of the spatial box, expressed in Debye length tt_w        :    time resolution for ion and electron density, electric field, electric potential and charge rap_m          :    ion-to-electron mass ratio R_p        :    ion-to-electron temperature ratio dt        :    time step used to evolve in time the numerical simulation emission    :    emission frequency power        :    amplitude of the electric charge imposed at the emitting antennas (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional     functionalities of the model. For the use of this dataset, they can be discarded.)

Community Detection to Split Large-scale Assemblies in Subassemblies

The motivation for the preprocessing of large-scale CAD models for assembly-by-disassembly approaches. The assembly-by-disassembly is only suitable for assemblies with a small number of parts (n_{parts} < 22). However, when dealing with large-scale products with high complexity, the CAD models may not contain feasible subassemblies (e.g. with connected and interference-free parts) and have too many parts to be processed with assembly-by-disassembly. Product designers' preferences during the design phase might not be ideal for assembly-by-disassembly processing because they do not consider subassembly feasibility and the number of parts per subassembly concisely. An automated preprocessing approach is proposed to address this issue by splitting the model into manageable partitions using community detection. This will allow for parallelised, efficient and accurate assembly-by-disassembly of large-scale CAD models. However, applying community detection methods for automatically splitting CAD models into smaller subassemblies is a new concept and research on the suitability for ASP needs to be conducted. Therefore, the following underlying research question will be answered in this experiments: Underlying research question 2: Can automated preprocessing increase the suitability of CAD-based assembly-by-disassembly for large-scale products? A hypothesis is formulated to answer this research question, which will be utilised to design experiments for hypothesis testing. Hypothesis 2: Community detection algorithms can be applied to automatically split large-scale assemblies in suitable candidates for CAD-based AND/OR graph generation.}

Simulations for impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle

Four experiments were conducted to investigate the impact of a large scale Saharan solar farm on the global carbon cycle. Here a fully coupled Earth System model (ESM) EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of Photovoltaic (PV) solar panels over the Sahara desert. These experiments considered solar panel coverage at 5, 20 or 50% of a region of the Sahara Desert.  The dataset contains selected output data from the simulations. Simulations were run for 200 years. The last 60 years were used for analysis after the model reached an equilibrium.  The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).

Data accompanying the publication: Measurements and modelling of pore-pressure gradients in the swash zone under large-scale laboratory bichromatic waves

Data accompanying the publication: Measurements and modelling of pore-pressure gradients in the swash zone under large-scale laboratory bichromatic waves. See the README.txt file for more detail.

Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa

This is a set of datasets used for the publication of the article titled: Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa

Loghub-2.0: a collection of large-scale datasets for log parsing

See dataset details: <a href="https://github.com/logpai/LogPub">https://github.com/logpai/Loghub-2.0</a> The datasets are freely available for research or academic work, subject to the following condition: For any usage or distribution of the LogPub datasets, please refer to the LogPub repository URL (<a href="https://github.com/logpai/LogPub">https://github.com/logpai/Loghub-2.0</a>) and cite the LogPub paper (<a href="https://arxiv.org/abs/2308.10828">A Large-scale Evaluation for Log Parsing Techniques: How Far are We?</a>) where applicable.

opencc-zeroSep 2023View details →

Large-scale genomic data reveal the phylogeny and evolution of owlet moths (Noctuoidea)

Owlet moths (Noctuoidea; ~43–45K described species; Nieukerken et al. 2011) are one of the most ecologically diverse and speciose superfamilies of animals. Moreover, they comprise some of the world's most notorious pests of agriculture and forestry. Despite their contributions to terrestrial biodiversity and impacts on ecosystems and economies, the evolutionary history of Noctuoidea remains unclear because the superfamily lacks a statistically robust phylogenetic and temporal framework. We reconstructed the phylogeny of Noctuoidea using data from 1,234 genes (946.4 kb of nucleotides) obtained from the genome and transcriptome sequences of 76 species representing all six families. Relationships among the families of Noctuoidea were well resolved and consistently recovered based on both concatenation and gene coalescence approaches, supporting the following relationships: Oenosandridae + (Notodontidae + (Erebidae + (Nolidae + (Euteliidae + Noctuidae)))). A Yule tree prior with three unlinked molecular clocks was identified as the preferred BEAST analysis using marginal-likelihood estimations. The crown age of Noctuoidea was estimated to be 74.5 Ma, with most families originating before the end of Paleogene (23 Ma). Our study provides the first statistically robust phylogenetic and temporal framework for Noctuoidea, including all families of owlet moths, based on large-scale genomic data.

opencc-by-4.0Jul 2023View details →

A Large-Scale AIS Datset from Finnish Water

The proposed AIS dataset encompasses a substantial temporal span of 20 months, spanning from April 2021 to December 2022. This extensive coverage period empowers analysts to examine long-term trends and variations in vessel activities. Moreover, it facilitates researchers in comprehending the potential influence of external factors, including weather patterns, seasonal variations, and economic conditions, on vessel traffic and behavior within the Finnish waters. This dataset encompasses an extensive array of data pertaining to vessel movements and activities encompassing seas, rivers, and lakes. Anticipated to be comprehensive in nature, the dataset encompasses a diverse range of ship types, such as cargo ships, tankers, fishing vessels, passenger ships, and various other categories. The AIS dataset exhibits a prominent attribute in the form of its exceptional granularity with a total of 2 293 129 345 data points. The provision of such granular information proves can help analysts to comprehend vessel dynamics and operations within the Finnish waters. It enables the identification of patterns and anomalies in vessel behavior and facilitates an assessment of the potential environmental implications associated with maritime activities. Please cite the following publication when using the dataset: TBD The publication is available at:  TBD A preprint version of the publication is available at  TBD <h3>csv file structure</h3> <h4><a href="#yyyy-mm-dd-locationcsv"></a>YYYY-MM-DD-location.csv</h4> This file contains the received AIS position reports. The structure of the logged parameters is the following: <code>[timestamp, timestampExternal, mmsi, lon, lat, sog, cog, navStat, rot, posAcc, raim, heading]</code> <code>timestamp</code> I beleive this is the UTC second when the report was generated by the electronic position system (EPFS) (0-59, or 60 if time stamp is not available, which should also be the default value, or 61 if positioning system is in manual input mode, or 62 if electronic position fixing system operates in estimated (dead reckoning) mode, or 63 if the positioning system is inoperative). <code>timestampExternal</code> The timestamp associated with the MQTT message received from <a href="http://www.digitraffic.fi/" target="_blank" rel="nofollow noreferrer noopener">www.digitraffic.fi</a>. It is assumed this timestamp is the Epoch time corresponding to when the AIS message was received by digitraffic.fi. <code>mmsi</code> MMSI number, Maritime Mobile Service Identity (MMSI) is a unique 9 digit number that is assigned to a (Digital Selective Calling) DSC radio or an AIS unit. Check <a href="https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity</a> <code>lon</code> Longitude, Longitude in 1/10 000 min (+/-180 deg, East = positive (as per 2's complement), West = negative (as per 2's complement). 181= (6791AC0h) = not available = default) <code>lat</code> Latitude, Latitude in 1/10 000 min (+/-90 deg, North = positive (as per 2's complement), South = negative (as per 2's complement). 91deg (3412140h) = not available = default) <code>sog</code> Speed over ground in 1/10 knot steps (0-102.2 knots) 1 023 = not available, 1 022 = 102.2 knots or higher <code>cog</code> Course over ground in 1/10 = (0-3599). 3600 (E10h) = not available = default. 3 601-4 095 should not be used <code>navStat</code> Navigational status, 0 = under way using engine, 1 = at anchor, 2 = not under command, 3 = restricted maneuverability, 4 = constrained by her draught, 5 = moored, 6 = aground, 7 = engaged in fishing, 8 = under way sailing, 9 = reserved for future amendment of navigational status for ships carrying DG, HS, or MP, or IMO hazard or pollutant category C, high speed craft (HSC), 10 = reserved for future amendment of navigational status for ships carrying dangerous goods (DG), harmful substances (HS) or marine pollutants (MP), or IMO hazard or pollutant category A, wing in ground (WIG); 11 = power-driven vessel towing astern (regional use); 12 = power-driven vessel pushing ahead or towing alongside (regional use); 13 = reserved for future use, 14 = AIS-SART (active), MOB-AIS, EPIRB-AIS 15 = undefined = default (also used by AIS-SART, MOB-AIS and EPIRB-AIS under test) <code>rot</code> ROTAIS Rate of turn <ul> <li>0 to +126 = turning right at up to 708 deg per min or higher</li> <li>0 to -126 = turning left at up to 708 deg per min or higher</li> <li>Values between 0 and 708 deg per min coded by ROTAIS = 4.733 SQRT(ROTsensor) degrees per min where ROTsensor is the Rate of Turn as input by an external Rate of Turn Indicator (TI). ROTAIS is rounded to the nearest integer value.</li> <li>+127 = turning right at more than 5 deg per 30 s (No TI available)</li> <li>-127 = turning left at more than 5 deg per 30 s (No TI available)</li> <li>-128 (80 hex) indicates no turn information available (default).</li> </ul> ROT data should not be derived from COG information. <code>posAcc</code> Position accuracy, The position accuracy (PA) flag should be determined in accordance with the table below: <ul> <li>1 = high (<= 10 m)</li> <li>0 = low (> 10 m)</li> <li>0 = default</li> </ul> See <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM</a> <code>raim</code> RAIM-flag Receiver autonomous integrity monitoring (RAIM) flag of electronic position fixing device; 0 = RAIM not in use = default; 1 = RAIM in use. See Table <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM</a> Check <a href="https://en.wikipedia.org/wiki/Receiver_autonomous_integrity_monitoring" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/Receiver_autonomous_integrity_monitoring</a> <code>heading</code> True heading, Degrees (0-359) (511 indicates not available = default) <h4><a href="#yyyy-mm-dd-metadatacsv"></a>YYYY-MM-DD-metadata.csv</h4> This file contains the received AIS metadata: the ship static and voyage related data. The structure of the logged parameters is the following: <code>[timestamp, destination, mmsi, callSign, imo, shipType, draught, eta, posType, pointA, pointB, pointC, pointD, name]</code> <code>timestamp</code> The timestamp associated with the MQTT message received from <a href="http://www.digitraffic.fi/" target="_blank" rel="nofollow noreferrer noopener">www.digitraffic.fi</a>. It is assumed this timestamp is the Epoch time corresponding to when the AIS message was received by digitraffic.fi. <code>destination</code> Maximum 20 characters using 6-bit ASCII; @@@@@@@@@@@@@@@@@@@@ = not available For SAR aircraft, the use of this field may be decided by the responsible administration <code>mmsi</code> MMSI number, Maritime Mobile Service Identity (MMSI) is a unique 9 digit number that is assigned to a (Digital Selective Calling) DSC radio or an AIS unit. Check <a href="https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity</a> <code>callSign</code> 7?=?6 bit ASCII characters, @@@@@@@ = not available = default Craft associated with a parent vessel, should use “A” followed by the last 6 digits of the MMSI of the parent vessel. Examples of these craft include towed vessels, rescue boats, tenders, lifeboats and liferafts. <code>imo</code> 0 = not available = default – Not applicable to SAR aircraft <ul> <li>0000000001-0000999999 not used</li> <li>0001000000-0009999999 = valid IMO number;</li> <li>0010000000-1073741823 = official flag state number.</li> </ul> Check: <a href="https://en.wikipedia.org/wiki/IMO_number" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/IMO_number</a> <code>shipType</code> <ul> <li>0 = not available or no ship = default</li> <li>1-99 = as defined below</li> <li>100-199 = reserved, for regional use</li> <li>200-255 = reserved, for future use Not applicable to SAR aircraft</li> </ul> Check <a href="https://www.navcen.uscg.gov/pdf/AIS/AISGuide.pdf" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/pdf/AIS/AISGuide.pdf</a> and <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic</a> <code>draught</code> In 1/10 m, 255 = draught 25.5 m or greater, 0 = not available = default; in accordance with IMO Resolution A.851 Not applicable to SAR aircraft, should be set to 0 <code>eta</code> Estimated time of arrival; MMDDHHMM UTC <ul> <li>Bits 19-16: month; 1-12; 0 = not available = default</li> <li>Bits 15-11: day; 1-31; 0 = not available = default</li> <li>Bits 10-6: hour; 0-23; 24 = not available = default</li> <li>Bits 5-0: minute; 0-59; 60 = not available = default</li> </ul> For SAR aircraft, the use of this field may be decided by the responsible administration <code>posType</code> Type of electronic position fixing device <ul> <li>0 = undefined (default)</li> <li>1 = GPS</li> <li>2 = GLONASS</li> <li>3 = combined GPS/GLONASS</li> <li>4 = Loran-C</li> <li>5 = Chayka</li> <li>6 = integrated navigation system</li> <li>7 = surveyed</li> <li>8 = Galileo,</li> <li>9-14 = not used</li> <li>15 = internal GNSS</li> </ul> <code>pointA</code> Reference point for reported position. Also indicates the dimension of ship (m). For SAR aircraft, the use of this field may be decided by the responsible administration. If used it should indicate the maximum dimensions of the craft. As default should A = B = C = D be set to “0” Check: <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic#_Reference_point_for" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic#_Reference_point_for</a> <code>pointB</code> See above <code>pointC</code> See above <code>pointD</code> See above <code>name</code> Maximum 20 characters 6 bit ASCII "@@@@@@@@@@@@@@@@@@@@" = not available = default The Name should be as shown on the station radio license. For SAR aircraft, it should be set to “SAR AIRCRAFT NNNNNNN” where NNNNNNN equals the aircraft registration number.

ClinicalTrials.gov36/100

Evaluate the Efficacy and Safety of Large-Scale Field-Directed Topical Therapy of Actinic Keratosis of the Chest w/Ingenol Mebutate 0.015%

ClinicalTrials.gov study NCT02446223. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

ClinSeq: A Large-Scale Medical Sequencing Clinical Research Pilot Study

ClinicalTrials.gov study NCT00410241. IPD Sharing: YES. Countries: 1. Publications: 5.

controlledIPD-YESFeb 2026View details →