cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3784593
title: MMORS Final Year Project: Supplementary Materials

description: The two files uploaded here are data sets obtained from a final year project completed at Cardiff University, School of Mathematics. For more information regarding this project and the data sets, please see the corresponding GitHub Repository. &lt; https://github.com/shapperzsm/final-project &gt; - The `main.db' file contains results obtained from a large Iterated Prisoner's Dilemma experiment focusing on the probability of defection for varying game-ending probabilities. - The `main.csv' file was created from the database. It contains the game-ending probabilities at which playing `defector' was no longer a rational action.
DOI: 10.5281/zenodo.3784594
title: MMORS Final Year Project: Supplementary Materials

description: The two files uploaded here are data sets obtained from a final year project completed at Cardiff University, School of Mathematics. For more information regarding this project and the data sets, please see the corresponding GitHub Repository. &lt; https://github.com/shapperzsm/final-project &gt; - The `main.db' file contains results obtained from a large Iterated Prisoner's Dilemma experiment focusing on the probability of defection for varying game-ending probabilities. - The `main.csv' file was created from the database. It contains the game-ending probabilities at which playing `defector' was no longer a rational action.
DOI: 10.7910/dvn/ce2530/3wnegc
title: coronavirus-tweet-id-2020-01-23-05.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/4otwbm
title: coronavirus-tweet-id-2020-01-24-21.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/k3wyxk
title: coronavirus-tweet-id-2020-01-24-03.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/lpxcmc
title: coronavirus-tweet-id-2020-01-31-21.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/n1kipe
title: coronavirus-tweet-id-2020-01-29-02.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/t4ms1l
title: coronavirus-tweet-id-2020-01-22-21.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/xxqhcc
title: coronavirus-tweet-id-2020-01-26-00.jsonl.gz

description: :unav
DOI: 10.25625/ko7yte/3g6sc2
title: 11_Structure.Calc.Fxns.R

description: :unav
DOI: 10.25625/ko7yte/bydpfp
title: CorrelationWithSdGPPSat_5days_05percent.jpeg

description: :unav
DOI: 10.25625/ko7yte/cu3cvc
title: 22_FluxNetData_GS.Rdata

description: :unav
DOI: 10.25625/ko7yte/jeu5rs
title: 22_FluxNetData_GS.R

description: :unav
DOI: 10.25625/ko7yte/jf8fye
title: US-Ha1_2015_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/kdwijy
title: 26_TSplots.Daily.Rmd

description: :unav
DOI: 10.25625/ko7yte/rsjmwp
title: US-NR1.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/snuzjh
title: US-Syv_2015_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/wlr8zx
title: Figure_8_EffectofStrucutreAnomalies.jpeg

description: :unav
DOI: 10.25919/5eb092a0dcb43
title: Parkes observations for project P1050 semester 2020APRS_07

description: This is a request for observing time for the initial follow-up of pulsar discoveries from the re-processing of the low-latitude Galactic plane section of the HTRU survey (P630). We have now completed a first pass re-processing of the 60 % of the survey with GPU based coherent acceleration search and template bank search pipelines, and have discovered potential 54 previously unknown pulsars. Interesting science can usually only be derived from a new pulsar after confirmation and a follow-up timing campaign is carried out. One year of initial timing is the minimum timespan required to fully characterise any newly-discovered pulsars, essential for deriving pulsar parameters such as the characteristic age, magnetic field strength, spin-down rate, as well as to detect any unexpected behaviour of the pulsar which might result from emission instabilities. This follow-up and timing project is necessary for following up on any interesting pulsar systems discovered from the HTRU Galactic plane survey. Since all of the pulsars on the observing list here are being followed-up for the first time, they will produce completely new and exciting results. In addition, this timing project will enable a large-scale examination of the Galactic plane pulsar population, exploring the true boundaries of pulsar parameter space.
DOI: 10.7910/dvn/ce2530/3qktsm
title: coronavirus-tweet-id-2020-01-22-23.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/arv132
title: coronavirus-tweet-id-2020-01-27-11.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/eonrvp
title: coronavirus-tweet-id-2020-01-31-06.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/foqz54
title: coronavirus-tweet-id-2020-01-23-02.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/m1ri5n
title: coronavirus-tweet-id-2020-01-30-00.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/rldimc
title: coronavirus-tweet-id-2020-01-28-14.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/xtrl4y
title: coronavirus-tweet-id-2020-01-22-02.jsonl.gz

description: :unav
DOI: 10.21227/95fg-fy48
title: Corona Virus (COVID-19) Turkish Tweets Dataset

description: This data set includes Covid-19 related Tweet messages written in Turkish that contain at least one of four keywords (Covid, Kovid, Corona, Korona). These keywords are used to express Covid-19 virus in Turkey. Tweets collection was started from 11th March 2020, the first Covid-19 case seen in Turkey. Currently dataset contain 4,5 million tweets with 6 different attribute of each tweets that were sent from 9 March 2020 until 30 April 2020. The data file contains comma separated values (CSV). It contains the following information (6 Column) for each tweet in the data file: Created-At: Exact creation time of the tweetFrom-User-Id: Sender User IdTo-User-Id: if it is sent to a user, its user IDLanguage: All TurkishRetweet-Count: number of retweetsId: ID of tweet that is unique for all tweets Search Twitter Operator of RapidMiner Software was used to collect tweets via the Twitter API. Due to the differentiation of keywords used in the time period and because of some technical constraints, the number of tweets collected daily for some days was less than normal until 30 March. After March 30, all Turkish tweets about covid-19 were collected continuously. The details of this subject are explained at the below. The data collection study started on March 17. In the Twitter API used, there is a 10,000-tweet upper limit and a last week time limit in each search to collect past tweets. Therefore, the oldest tweets that can be collected belong to March 11. Detecting the first cases in Turkey were also held on 11 March. So, tweets been collected since the first cases detected in Turkey. In order to collect data, RapidMiner Data mining software was used, and a maximum of 10,000 tweets were collected for each day, from 11 March until 17 March. In this way, after the past data of the last week were collected, the last sent 10,000 tweets were taken at intervals of twenty minutes (Twitter API can be used with 15 minutes interval, added 5 minutes more for precautionary). Thus, if more than 10,000 tweets were not posted within 20 minutes, it was possible to gather all the tweets. Of course, in less than 10,000 tweets were sent within 20 minutes, the same tweets were repeatedly drawn in different iterations. For this reason, duplicate records were deleted using the Tweet ID number. RapidMiner Turbo Prep application was used for this process.While Turkish tweets containing the word "Corona" were collected as of March 11, the ones containing the word "Covid" started to be collected after March 16. With the widespread use of the words "Kovid" and "Korona", since March 30, all Turkish Tweets containing at least one of 4 keywords were collected using the search phrase "Covid OR Kovid OR Korona OR Corona".
DOI: 10.25625/ko7yte/2dkxnp
title: 01_PlotFxns.R

description: :unav
DOI: 10.25625/ko7yte/8ozgz0
title: US-Me4.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/98fpzo
title: 62_Annual.stability.forAllVar.AndSites.tab

description: :unav
DOI: 10.25625/ko7yte/aibqlh
title: US-Ha1_2012_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/tmavi4
title: 03_Site.problem.Years.tab

description: :unav
DOI: 10.25625/ko7yte/wenw41
title: IT-Ren.Inventory.Data.tab

description: :unav
DOI: 10.5255/ukda-sn-854313
title: A study of professional conservationists' perspectives on poverty 2015-2017
DOI: 10.5281/zenodo.3786599
title: Pulsational pair-instability supernovae in very close binaries

description: Models of pulsational pair-instability supernovae arising from the evolution of metal poor helium stars. These simulations have been made with the MESA software instrument for stellar evolution, and are used in the paper "Pulsational pair-instability supernovae in very close binaries". Data includes machine-readable tables, both history and profile files from the simulations, as well as the terminal output and a movie for each. All MESA output files have been post-processed to reduce their size. All the code used to process the MESA simulations and produce figures is provided in a jupyter-notebook. WARNING: Simulations were done using a development MESA version (11123) rather than an official release. List of files: - template.tar.xz: MESA work folder to reproduce the simulations. - xxx_xx.tar.xz: Each of these files contains stellar profiles at different moments of evolution for one simulation. - histories.tar.xz: MESA history files for each simulation, containing the time evolution of quantities that are single-valued at each moment in time. - preSN.tar.xz: profiles for stars before the onset of pulsations (see paper for definition). - movies.tar: Movies for all simulations, produced using MESAs pgstar. - terminal_output.tar.xz: Terminal output from the simulations. - masses_table.txt: Machine readable version of Table 1 in the paper. - pulse_tables.tar.xz: machine readable tables containing mass lost, time, and kinetic energy of each pulse. - notebook.tar.xz: jupyter-notebook plus complementary files used to post-process the data and produce Figures. This version of the files has an updated notebook.tar.xz to produce the figures in the published version of the paper.
DOI: 10.7910/dvn/ce2530/0hkfnx
title: coronavirus-tweet-id-2020-01-30-21.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/76wdac
title: coronavirus-tweet-id-2020-01-23-00.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/7rvtjw
title: coronavirus-tweet-id-2020-01-23-21.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/7vlzis
title: coronavirus-tweet-id-2020-01-24-07.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/ebexrc
title: coronavirus-tweet-id-2020-01-25-17.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/nwpapt
title: coronavirus-tweet-id-2020-01-27-10.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/vis12v
title: coronavirus-tweet-id-2020-01-30-08.jsonl.gz

description: :unav
DOI: 10.17632/8vfm4rddkb.1
title: Data for: Generalized phase calibration method of liquid crystal spatial light modulator with absolute reference system of obnoxious background light

description: figures in the manuscript
DOI: 10.25625/ko7yte/7iqdjz
title: 51_GPP1000dailyData.Rdata

description: :unav
DOI: 10.25625/ko7yte/ahoyql
title: US-UMB.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/bgdndq
title: BarPlotAnnualAnomalies.jpeg

description: :unav
DOI: 10.25625/ko7yte/og8jfs
title: Figure_3_SPEIexample.jpeg

description: :unav
DOI: 10.25625/ko7yte/suy4lr
title: JP-Tef.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/tivfig
title: 0000_DcorPF.tab

description: :unav
DOI: 10.25625/ko7yte/vc8hfn
title: US-Ha1_2013_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/vvfbd1
title: CH-Lae.Inventory.Data.tab

description: :unav
DOI: 10.7910/dvn/ce2530/2oevbd
title: coronavirus-tweet-id-2020-01-30-04.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/6hchb5
title: coronavirus-tweet-id-2020-01-30-09.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/blypbc
title: coronavirus-tweet-id-2020-01-28-17.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/catnzx
title: coronavirus-tweet-id-2020-01-28-10.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/dy9uov
title: coronavirus-tweet-id-2020-01-26-21.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/feugtq
title: coronavirus-tweet-id-2020-01-27-18.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/irnaqt
title: coronavirus-tweet-id-2020-01-27-08.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/luyaqa
title: coronavirus-tweet-id-2020-01-31-18.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/zd8lfb
title: coronavirus-tweet-id-2020-01-24-14.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/zom4pl
title: coronavirus-tweet-id-2020-01-26-11.jsonl.gz

description: :unav
DOI: 10.17632/8vfm4rddkb.2
title: Data for: Generalized phase calibration method of liquid crystal spatial light modulator with absolute reference system of obnoxious background light

description: figures in the manuscript
DOI: 10.21227/d1km-8183
title: Corona Virus (COVID-19) Turkish Tweets Dataset

description: This data set includes Covid-19 related Tweet messages written in Turkish that contain at least one of four keywords (Covid, Kovid, Corona, Korona). These keywords are used to express Covid-19 virus in Turkey. Tweets collection was started from 11th March 2020, the first Covid-19 case seen in Turkey. Currently dataset contain 4,5 million tweets with 6 different attribute of each tweets that were sent from 9 March 2020 until 30 April 2020. The data file contains comma separated values (CSV). It contains the following information (6 Column) for each tweet in the data file: Created-At: Exact creation time of the tweet From-User-Id: Sender User Id To-User-Id: if it is sent to a user, its user ID Language: All Turkish Retweet-Count: number of retweets Id: ID of tweet that is unique for all tweets Search Twitter Operator of RapidMiner Software was used to collect tweets via the Twitter API. Due to the differentiation of keywords used in the time period and because of some technical constraints, the number of tweets collected daily for some days was less than normal until 30 March. After March 30, all Turkish tweets about covid-19 were collected continuously. The details of this subject are explained at the below. The data collection study started on March 17. In the Twitter API used, there is a 10,000-tweet upper limit and a last week time limit in each search to collect past tweets. Therefore, the oldest tweets that can be collected belong to March 11. Detecting the first cases in Turkey were also held on 11 March. So, tweets been collected since the first cases detected in Turkey. In order to collect data, RapidMiner Data mining software was used, and a maximum of 10,000 tweets were collected for each day, from 11 March until 17 March. In this way, after the past data of the last week were collected, the last sent 10,000 tweets were taken at intervals of twenty minutes (Twitter API can be used with 15 minutes interval, added 5 minutes more for precautionary). Thus, if more than 10,000 tweets were not posted within 20 minutes, it was possible to gather all the tweets. Of course, in less than 10,000 tweets were sent within 20 minutes, the same tweets were repeatedly drawn in different iterations. For this reason, duplicate records were deleted using the Tweet ID number. RapidMiner Turbo Prep application was used for this process. While Turkish tweets containing the word "Corona" were collected as of March 11, the ones containing the word "Covid" started to be collected after March 16. With the widespread use of the words "Kovid" and "Korona", since March 30, all Turkish Tweets containing at least one of 4 keywords were collected using the search phrase "Covid OR Kovid OR Korona OR Corona".
DOI: 10.21227/xg7q-y061
title: Corona Virus (COVID-19) Turkish Tweets Dataset

description: This data set includes Covid-19 related Tweet messages written in Turkish that contain at least one of four keywords (Covid, Kovid, Corona, Korona). These keywords are used to express Covid-19 virus in Turkey. Tweets collection was started from 11th March 2020, the first Covid-19 case seen in Turkey.
DOI: 10.25625/ko7yte
title: Replication Data for: Chapter three of 'Implications of forest structure on carbon dioxide fluxes'

description: This dataset contains plots, data and Rcodes for the third chapter of 'Implications of forest structure on carbon dioxide fluxes'.
DOI: 10.25625/ko7yte/hclz4f
title: Figure_9_EffectofManagementonAnomalies.jpeg

description: :unav
DOI: 10.25625/ko7yte/jj3rwb
title: US-Me2_2015_Weibull.jpeg

description: :unav
DOI: 10.7910/dvn/ce2530/5trpup
title: coronavirus-tweet-id-2020-01-28-09.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/bzz1nt
title: coronavirus-tweet-id-2020-01-29-19.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/ebxbxc
title: coronavirus-tweet-id-2020-01-29-15.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/l6x1cz
title: coronavirus-tweet-id-2020-01-26-03.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/muzw75
title: coronavirus-tweet-id-2020-01-24-09.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/oo17mo
title: coronavirus-tweet-id-2020-01-28-20.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/qboqrq
title: coronavirus-tweet-id-2020-01-22-03.jsonl.gz

description: :unav
DOI: 10.18716/nmrshiftdb2/76791
title: NMR data for 3-(dimethylamino)benzaldehyde
DOI: 10.5281/zenodo.3785365
title: Datasets for Itemset, Sequence and Tree Mining

description: There are three different datasets included, that can be used for itemset, sequence and tree mining methods. <strong>dense_db.zip</strong> contains various real itemset datasets like <strong>chess</strong>, <strong>connect</strong>, <strong>mushroom</strong>, <strong>pumsb, T10I4D100K, T40I10D100K</strong> and so on, used in the papers on frequent, closed and maximal itemset mining. For example, <strong>Mohammed J. Zaki</strong> and Ching-Jui Hsiao. <strong>Efficient algorithms for mining closed itemsets and their lattice structure.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(4):462–478, April 2005. doi:10.1109/69.846291. Or Karam Gouda and <strong>Mohammed J. Zaki</strong>. <strong>Genmax: an efficient algorithm for mining maximal frequent itemsets.</strong> <em>Data Mining and Knowledge Discovery: An International Journal</em>, 11(3):223–242, November 2005. doi:10.1007/s10618-005-0002-x. <strong>plandata.zip</strong>: Planning dataset for sequence mining. It was used in the paper <strong>Mohammed J. Zaki</strong>, Neal Lesh, and Mitsunori Ogihara. <strong>PLANMINE: predicting plan failures using sequence mining.</strong> <em>Artificial Intelligence Review</em>, 14(6):421–446, December 2000. Special issue on Applications of Data Mining. doi:https://doi.org/10.1023/A:1006612804250. <strong>cslogs.zip</strong>: The CSLOGS data was used for tree mining, e.g., in <strong>Mohammed J. Zaki</strong>. <strong>Efficiently mining frequent trees in a forest: algorithms and applications.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(8):1021–1035, August 2005. Special issue on Mining Biological Data. doi:10.1109/TKDE.2005.125.
description: {"references": ["Mohammed J. Zaki and Ching-Jui Hsiao. Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Transactions on Knowledge and Data Engineering, 17(4):462\u2013478, April 2005. doi:10.1109/69.846291.", "Karam Gouda and Mohammed J. Zaki. Genmax: an efficient algorithm for mining maximal frequent itemsets. Data Mining and Knowledge Discovery: An International Journal, 11(3):223\u2013242, November 2005. doi:10.1007/s10618-005-0002-x.", "Mohammed J. Zaki. Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Transactions on Knowledge and Data Engineering, 17(8):1021\u20131035, August 2005. Special issue on Mining Biological Data. doi:10.1109/TKDE.2005.125.", "Mohammed J. Zaki, Neal Lesh, and Mitsunori Ogihara. PLANMINE: predicting plan failures using sequence mining. Artificial Intelligence Review, 14(6):421\u2013446, December 2000. Special issue on Applications of Data Mining.\u00a0doi:https://doi.org/10.1023/A:1006612804250."]}
DOI: 10.5281/zenodo.3786255
title: Effects of left ventricular assist device on cardiopulmonary exercise performance

description: This dataset contains raw data of the article "Effects of left ventricular assist device on cardiopulmonary exercise performance"
DOI: 10.5281/zenodo.3786256
title: Effects of left ventricular assist device on cardiopulmonary exercise performance

description: This dataset contains raw data of the article "Effects of left ventricular assist device on cardiopulmonary exercise performance"
DOI: 10.5281/zenodo.3786368
title: The CoVidAffect dataset of mood variations following the COVID-19 outbreak

description: Regular update of project datasets
DOI: 10.5066/p9akwdkk
title: Counts, Fish Weights, and Spectral Data from a Juvenile Chinook Salmon Polyester Microplastic Fiber Exposure Study

description: This release includes all data collected in support of a juvenile Chinook salmon exposure study designed to understand the retention of microplastic clothing fibers (hereon referred to as "microfibers") after feeding. To understand the potential impacts of microfiber ingestion to fish, we ran a feeding experiment with juvenile Chinook salmon to determine if ingested fibers are retained and/or digestion rates altered over a full, experimentally determined, 10 day digestion period. Data includes: fish weights, microfiber counts, and quality assurance (QA) data for the experiment. QA steps are detailed in the process section of the metadata.
DOI: 10.48370/ofd/akwz7q/1vgddp
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4507.JPG

description: :unav
DOI: 10.48370/ofd/akwz7q/bxicon
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4507 (3).JPG

description: :unav
DOI: 10.48370/ofd/akwz7q/1qb24g
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4507 (2).JPG

description: :unav
DOI: 10.48370/ofd/akwz7q
title: Laxitextum bicolor (Pers.: Fr.) Lentz, 4507

description: Fungarium Macromycetes
DOI: 10.48370/ofd/akjcfo/l2hj0s
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4674 (2).JPG

description: :unav
DOI: 10.48370/ofd/akjcfo/cawin9
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4674.JPG

description: :unav
DOI: 10.48370/ofd/akjcfo/7wp7hb
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4674 (3).JPG

description: :unav
DOI: 10.48370/ofd/akjcfo
title: Laxitextum bicolor (Pers.: Fr.) Lentz, 4674

description: Fungarium Macromycetes
DOI: 10.48370/ofd/62rel0/bv6cku
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4559 (3).JPG

description: :unav
DOI: 10.48370/ofd/62rel0/0bcol0
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4559 (2).JPG

description: :unav
DOI: 10.48370/ofd/62rel0/eiiims
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4559.JPG

description: :unav
DOI: 10.48370/ofd/7c5zg2/onl2fq
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4507 (3).JPG

description: :unav
DOI: 10.48370/ofd/62rel0
title: Laxitextum bicolor (Pers.: Fr.) Lentz, 4559

description: Fungarium Macromycetes
DOI: 10.48370/ofd/7c5zg2/gcutti
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4507 (2).JPG

description: :unav
DOI: 10.48370/ofd/7c5zg2/s6ahxx
title: FMW Laxitextum bicolor (Pers. Fr.) Lentz M-4507.JPG

description: :unav
DOI: 10.48370/ofd/7c5zg2
title: Laxitextum bicolor (Pers.: Fr.) Lentz, 4507

description: Fungarium Macromycetes
DOI: 10.48370/ofd/nskdgy/jj3c8b
title: FMW Lentaria mucida (Pers. Fr.) Corner M-4643.JPG

description: :unav
DOI: 10.48370/ofd/nskdgy/ps4xp2
title: FMW Lentaria mucida (Pers. Fr.) Corner M-4643 (3).JPG

description: :unav
DOI: 10.48370/ofd/nskdgy/qjrwoc
title: FMW Lentaria mucida (Pers. Fr.) Corner M-4643 (4).JPG

description: :unav
DOI: 10.48370/ofd/nskdgy/bgpquw
title: FMW Lentaria mucida (Pers. Fr.) Corner M-4643 (2).JPG

description: :unav
DOI: 10.48370/ofd/nskdgy
title: Lentaria mucida (Pers.: Fr.) Corner, 4643

description: Fungarium Macromycetes