Find research datasets worth reusing

openCC (other)Dec 2015View details →

Chlorophyll a and primary productivity data for various lakes near Toolik Research Station, Arctic LTER. Summer 1983 to 1989.

Decadal file describing the chlorophyll a and primary production in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 1983 to 1989. Sample site descriptors include an assigned number (sortchem), site, date of analysis (incubation), time, depth and rates of primary production. The amount of chlorophyll a and pheophytin were also measured.

openCC (other)Apr 2023View details →

Size-fractionated net primary productivity (NPP) estimates based on 13C uptake during cruises along the Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect, ongoing since 2019

This dataset consists of primary production measurements based on uptake of carbon-13 added as 13C-bicarbonate during 24-h deckboard incubations of seawater. Sampling occurred on cruises along the Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect during summer, fall, and winter, starting in summer 2019. Net primary production (NPP) was determined from particulate organic carbon (POC) content and associated stable isotope enrichment. Three data tables are included: 1. Depth-specific primary productivity based on fractional light levels of the surface irradiance with reference to the profile of photosynthetically active radiation (PAR). 2. Integrated primary productivity. 3. Natural abundance POC. The tables derive from the raw data included as other entities.

openCC0Mar 2024View details →

Core Research Site Web Quadrat Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico

This dataset is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across four distinct ecosystems: creosote-dominant shrubland (Site C, est. winter 1999), black grama-dominant grassland (Site G, est. winter 1999), blue grama-dominant grassland (Site B, est. winter 2002), and pinon-juniper woodland (Site P, est. winter 2003). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and and foliage, over time and incoporates growth as well as loss to death and decomposition. To measure this change the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. A third sampling at Site C is performed in the winter. The data from these plots is used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV157, "Net Primary Productivity (NPP) Weight Data." This biomass data is included in SEV182, "Seasonal Biomass and Seasonal and Annual NPP for Core Research Sites." This dataset is designated as NA-US-011 in the Global Index of Vegetation-Plot Databases (GIVD). To aid tracking of the use of databases in this index, please also reference this number when citing this data. The GIVD report for SEV129 can be found in: Biodiversity and Ecology 4 - Vegetation Databases for the 21st Century (2012) by J. Dengler et al.

openCC0Feb 2024View details →

Core Research Site Web Seasonal Biomass and Seasonal and Annual NPP Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico

This long-term study at the Sevilleta LTER measures net primary production (NPP) across four distinct ecosystems: creosote-dominant shrubland (Site C, est. winter 1999), black grama-dominant grassland (Site G, est. winter 1999), blue grama-dominant grassland (Site B, est. winter 2002), and pinon-juniper woodland (Site P, est. winter 2003), which is now in its own dataset, SEV278 (Pinon-Juniper (Core Site) Quadrat Data). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots collected in SEV129, "Core Research Site Web Quadrat Data" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."

zenodo48/100

Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework

This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play.  In this data set, team level multi-modal behavioral data is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below:  <ul> <li>condition: This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li>Error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li>Learning Gain: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li>Usefulness Score: The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li>PE Score: It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li>Right_Suggestions: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li>Right_Time: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li>Exploration: This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li>Reflection: This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li>Communication: This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li>LG_status: This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> This dataset corresponds to the publication "Social robots as skilled ignorant peers for supporting learning": <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a>

opencc-by-4.0Oct 2023View details →

openCC (other)Mar 2018View details →

Chlorophyll a and primary productivity data for various lakes near Toolik Research Station, Arctic LTER. Summer 2000 to 2009.

Decadal file describing the chlorophyll a and primary production in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 2000 to 2009. Sample site descriptors include an assigned number (sortchem), site, date of analysis (incubation), time, depth and rates of primary production. The amount of chlorophyll a and pheophytin were also measured.

openCC (other)Jan 2020View details →

Chlorophyll a and primary productivity data for various lakes near Toolik Research Station, Arctic LTER. Summer 1990 to 1999.

Decadal file describing the chlorophyll a and primary production in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 1990 to 1999. Sample site descriptors include an assigned number (sortchem), site, date of analysis (incubation), time, depth and rates of primary production. The amount of chlorophyll a and pheophytin were also measured.

openOpenMay 2022View details →

Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Phenology of Dominant Plant Species II - Berry Production 2013-2021

The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. In this dataset we specifically ask, how does warming of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).

openOpenMay 2022View details →

Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Phenology of Dominant Plant Species II - Berry Production 2013-2021

This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. In this dataset we specifically ask, how does warming and drying of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).

opencc-by-4.0Dec 2023View details →

National Open Access Monitor, Ireland - Research product metadata

This dataset contains the foundational data for the National Open Access Monitor under an open license. OpenAIRE will routinely provide monthly data dumps to Zenodo, encompassing a comprehensive set of data and indicators related to the Open Access Monitor. This collaborative effort ensures accessibility and openness in sharing the data, promoting transparency and facilitating its use for research and analysis purposes. The dataset comprises of the metadata of the research products metadata stored in the parquet format. The file contains two columns ("id", "xml"), the first of which is the OpenAIRE identifier of the research product and the the second the xml representation of the metadata. The schema of the metadata can be found in https://www.openaire.eu/schema/1.0/oaf-1.0.xsd and a detailed description of the contents can be found in https://graph.openaire.eu/docs/ and https://zenodo.org/records/2643199

opencc-by-4.0Feb 2024View details →

Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"

Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.

opencc-by-4.0Oct 2024View details →

Research data: Continuity Amid Transformation: An Analysis of Pottery Production from the Late La Tène to Early Roman Periods in Eastern Bohemia

Data used in the research presented in the article titled "Continuity Amid Transformation: An Analysis of Pottery Production from the Late La Tène to Early Roman Periods in Eastern Bohemia". Abstract of the article: At the end of the La Tène period and the beginning of the Roman period in the first century BC, society in Central Europe underwent a significant transformation, which included notable changes in pottery production. This transformation is often attributed to the collapse of the social structures of the La Tène period and the arrival of a new population. Pottery production, in particular, is generally considered to have undergone a complete transformation. However, previous studies on this transition have primarily focused on the stylistic analysis of shapes and decorations, as illustrated by the pottery assemblage from Slepotice (Eastern Bohemia). In order to obtain additional data on the transitional period, this study of pottery from Slepotice incorporates analyses of the materials used and the manufacturing process through macroscopic observation, X-ray fluorescence analysis, and thin-section analysis. These analyses provide new insights into the differences in pottery production and distribution during the first century BC. Our research indicates that while the transformation included the collapse of the La Tène socioeconomic network, it did not result in a complete break in the pottery production process. Link to the article: <a href="https://doi.org/10.1016/j.jasrep.2025.105073">https://doi.org/10.1016/j.jasrep.2025.105073</a>   List of the files: Supplementary Material 1 Settlement structure in the vicinity of Slepotice during the La Tène and Roman periods: 1 – Slepotice, 2 – České Lhotice, 3 – Brčekoly, 4 – Chrudim Supplementary Material 2 Values of pottery attributes (Mat, InMn, InVar, In, traces left from the shaping process, Po, Vy, and morphological groups) classified based on macroscopic observation Supplementary material 3 Schematic classification of rim attributes, illustrating different variants of rim direction (Op), thickening of the upper part of the rim (Oz), and trimming of the lip (Os) Supplementary material 4 Attributes of the 30 samples selected for XRF analysis based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary material 5 Figures of ceramic samples (with corresponding IDs) from feature 144/1998 showing preserved rims and bases Supplementary material 6 Figures of ceramic samples (with corresponding IDs) from feature 355/2001 showing preserved rims Supplementary Material 7 Chemical composition of 30 selected samples according to XRF analysis (main oxides in wt%, and elements in ppm) Supplementary Material 8 Principal Component Analysis (PCA) results: The scree plot (top left) visualises the proportion of variance explained by each principal component. The biplots (top right and bottom right) illustrate the distribution of samples, with arrows indicating the contribution of specific elements to the observed variance. The dendrogram (bottom left) shows hierarchical clustering of the samples, aiding in the selection of representative samples for thin-section petrographic analysis Supplementary Material 9 Relationships between the dating and other attributes of pottery classified based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary Material 10 Relationships between the chemical groups (determined by XRF analysis) and pottery attributes classified based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary Material 11 Petrography of fabric groups and subgroups, focusing on their properties. The evaluation begins with a general assessment of each fabric group as a whole, followed by a detailed examination of its subgroups Supplementary Material 12 Petrographic characterization of ceramics using a semiquantitative scale, simplified for statistical analysis (0.1 – trace, 0.5 – rare, 1 – occasional, 2 – common, 3 – frequent, 4 – abundant, 5 – dominant) Supplementary Material 13 Thin-section samples: Description of the ceramic matrix, natural inclusions, and added tempers Supplementary material 14 Variations in chemical composition among different fabric groups

opencc-by-4.0Mar 2022View details →

Hyperspectral imagery Research Products - Toulouse urban area 2015 (French ANR HYEP project)

The HYEP project (ANR 14-CE22-0016-01) main goal was to propose a panel of methods and processes designed for hyperspectral imaging, which specificity makes a weighty auxiliary for the monitoring of the elements of the urban area.  The main results of the project can be found at <ul> <li><a href="http://doi.org/10.1080/01431161.2017.1410247">G. Roussel, C. Weber, X. Briottet and X. Ceamanos, "Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution", International Journal of Remote Sensing, vol. 39(5), pp. 1593-1614, 2018.</a></li> <li><a href="http://doi.org/10.1109/ECMSM.2017.7945884">F. Z. Benhalouche, M. S. Karoui, Y. Deville, I. Boukerch, A. Ouamri, ``Multi-sharpening hyperspectral remote sensing data by multiplicative joint-criterion linear-quadratic nonnegative matrix factorization'', Proceedings of the 2017 IEEE International Workshop on Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM 2017), May 24-26, 2017, Donostia - San Sebastian</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01903469">Gintautas Mozgeris, Vytaut ̇e Juodkien ̇e, Donatas Jonikaviˇcius, Lina Straigyt ̇e, S ́ebastien Gadal, and Walid Ouerghemmi. Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment. Remote Sensing, 10(10), October 2018.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-02281003">Christiane Weber, Thomas Houet, S ́ebastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Cl ́ement Mallet, and Arnaud Le Bris. HYEP HYperspectral imagery for Environmental urban Planning : principaux résultats. In 7ème colloque scientifique du groupe SFPT-GH, Toulouse, France, July 2019. ONERA - SFTP.</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01852844">Christiane Weber, Rahim Aguejdad, Xavier Briottet, Josselin Aval, Sophie Fabre, Jean Demuynck, Emmanuel Zenou, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra, Sébastien Gadal, Walid Ouerghemmi, Clément Mallet, Arnaud Le Bris, and Nesrine CHEHATA. Hyperspectral Imagery for Environmental Urban Planning. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, pages 1628–1631, Valencia, Spain, July 2018a. IEEE.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01854904">Christiane Weber, Rahim Aguejdad, X Briottet, J Avala, S. Fabre, J Demuynck, E Zenou, Y. Deville, M. Karoui, F Z Benhalouche, S Gadal, W Ourghemmi, C. Mallet, A. Le Bris, and N. Chehata. HYPERSPECTRAL IMAGERY FOR ENVIRONMENTAL URBAN PLANNING. In IGARSS 2018, Valencia, Spain, 2018b. </a></li> <li><a href="http://doi.org/10.5194/isprs-archives-XLII-1-W1-167-201">W. Ouerghemmi, A. Le Bris, Nesrine CHEHATA, and Clément Mallet. A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-1/W1, pages 167–174, Hanover, Germany, May 2017. Copernicus GmbH (Copernicus Publications).</a></li> <li><a href="https://hal.inria.fr/hal-02384455">Christiane Weber, Sébastien GADAL, Xavier Briottet, and Clément Mallet. Apport de l’imagerie hyperspectrale pour la planification urbaine. In Karine Emsellem, Diego Moreno, Christine Voiron-Canicio, and Didier Josselin, editors, SAGEO 2016 - Spatial Analysis and Geomatics, Actes de la conférence SAGEO’2016 - Spatial Analysis and GEOmatics, pages 454–462, Nice, France, December 2016. </a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01359643">Gintautas Mozgeris, S ́ebastien Gadal, Donatas Jonikaviˇcius, Lina Straigyte, Walid Ouerghemmi, and Vytaut ̇e Juodkiene. Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. In University of California Los Angeles, editor, 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pages 542–546, Los Angeles, United States, August 2016.</a></li> <li><a href="https://hal.inria.fr/hal-02384458">Alexandre Hervieu, Arnaud Le Bris, and Cl ́ement Mallet. Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume III-3, pages 457–464, Prague, Czech Republic, July 2016.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01888126">Christiane Weber, Thomas Houet, Sebastien GADAL, Rahim Aguejdad, Grzegorz Skupinski, Aziz Serradj, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Clément Mallet, and Arnaud Le Bris. ANR HYEP ANR 14-CE22-0016-01Hyperspectral imagery for Environmental urban Planning HyepProgramme Mobilité et systèmes urbains 2014. Research report, CNRS UMR TETIS, ESPACE, LETG ; ONERA ; GIPSA-lab ; IRAP ; IGN, October 2018c. </a></li> <li><a href="https://doi.org/10.1080/01431161.2019.1579937">Josselin Aval, Sophie Fabre, Emmanuel Zenou, David Sheeren, Mathieu Fauvel & Xavier Briottet (2019) Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data, International Journal of Remote Sensing, 40:14, 5339-5365, DOI: 10.1080/01431161.2019.1579937 </a></li> <li><a href="https://doi.org/10.3390/rs11111269">Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwenaël Morin, Kim Thanh Nguyen et al. Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification Remote Sensing, MDPI, 2019, 11 (11), pp.1269. &lang;10.3390/rs11111269&rang; hal-02191084v1 </a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191363v1">C. Brabant, Emilien Alvarez-Vanhard, Gwenaël Morin, Thanh Ngoc Nguyen, Achour Laribi et al. Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data IGARSS 2018, Jul 2018, Valencia, Spain halshs-02191363v1</a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191097v1">Charlotte Brabant, Emilien Alvarez-Vanhard, Thomas Houet. Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques Joint Urban Remote Sensing Event (JURSE 2019), May 2019, Vannes, France halshs-02191097v1</a></li> </ul> This Dataset contains five research outputs of this project that were produced on the basis of Hyperspectral data obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images. <ul> <li>Fields_samples.7z:  ESRI Shape Format.  Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 & 19 classes) and species (14 & 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 & 19 classes) and species (14 & 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (<a href="https://www.mdpi.com/470202">https://www.mdpi.com/470202</a>) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).</li> <li>HySPex-2m.7z: full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.</li> <li>HYPXIM-4m.7z,  HYPXIM-8m.7z,  Sentinel2-10m.7z: hyperspectral ENVI standard simulated images. Spatial and spectral configurations generated correspond to ESA SENTINEL-2 instrument that was lunched on 2015, and HYPXIM sensor which was under study at that time. </li> </ul>

opencc-by-4.0Jul 2024View details →

Data from: Collaborative Research: Influence of phosphorus deficiency on enigmatic biological methane production in oxic freshwater lakes

Data from: Collaborative Research: Influence of phosphorus deficiency on enigmatic biological methane production in oxic freshwater lakes NSF Projects 1951002 (PI: Matthew J. Church), 1950963 (PI: John E. Dore)

opencc-by-4.0Dec 2021View details →

The LOTUS Initiative for Open Natural Products Research: frozen dataset union wikidata (with metadata)

Dataset present on Wikidata used in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a>

opencc-by-4.0Dec 2021View details →

The LOTUS Initiative for Open Natural Products Research: waste to recycle

Dataset not uploaded to Wikidata.  Generated in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a> Shared for further curation.