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5,366 results for “Guinea”
Guinea baboon vocalizations dataset automatically extracted with a deep neural network from natural audio recordings
<p><strong>Abstract</strong></p> <p>The data collection process consisted of continuously recording during one month a group of Guinea baboons living in semi-liberty at the CNRS primatology center in Rousset-sur-Arc (France). Two microphones we placed nearby their enclosure to continuously record the sounds produced by the group. A convolutional neural network (CNN) was used on these large and noisy audio recordings to automatically extract segments of sound containing a baboon vocal production by following the method of <a href="https://arxiv.org/abs/2302.07640">Bonafos et al. (2023)</a>. The resulting dataset consists of one-second to several-minute wav files of automatically detected vocalizations segments. The dataset thus provides a wide range of baboon vocalizations produced at all times of the day. It can be used to study vocal productions of non-human primates, their repertoire, their distribution over the day, their frequency, and their heterogeneity. In addition to the analysis of animal communication, the dataset can also be used as a learning base for sound classification models.</p> <p> </p> <p><strong>Data acquisition</strong></p> <p>The data are audio recordings of baboons. The recordings were made with a H6 Zoom recorder, using the included XYH-6 stereo microphone. The sample size is 44100 Hertz, 16 bits. The microphones were placed in the vicinity of the enclosure for one month and recorded continuously on a PC computer. A CNN passed over the data with a sliding window of 1 second and an overlap of 80% to detect the vocal productions of the baboons. The dataset consists of the segments predicted by the CNN to contain a baboon vocalization. Windows containing signal less than one second apart were merged into a single vocalization.</p> <p> </p> <p><strong>Data source location</strong></p> <ul> <li>Institution: CNRS, Primate Facility</li> <li> <p>City/Town/Region: Rousset-sur-Arc</p> </li> <li> <p>Country: France</p> </li> <li> <p>Latitude and longitude for collected samples/data: 43.47033535251509, 5.6514732876668905</p> </li> </ul> <p> </p> <p><strong>Value of the data</strong></p> <ul> <li> <p>This dataset is relatively unique in terms of the quantity of vocalizations available.</p> </li> <li> <p>This massive dataset can be very useful to two types of scientific communities: experts in primatology who study the vocal productions of non-human primates, and experts in data science and audio signal processing.</p> </li> <li> <p>The machine learning research community has at its disposal a database of several dozen hours of animal vocalizations, which will make it possible to build up a large learning base, very useful for Environemental Sound Recognition tasks, for example.</p> </li> </ul> <p> </p> <p><strong>Objective</strong></p> <p>This dataset is a follow-up of two studies on the vocal productions of Guinea baboons (Papio papio) in which we carried out analyses of their vocal productions on the basis of a relatively large vocalization sample containing around 1300 vocalizations (<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169321">Boë, Berthommier, Legou, Captier, Kemp, Sawallis, Becker, Rey, & Fagot, 2017</a>; <a href="https://hal.science/hal-01649539">Kemp, Rey, Legou, Boë, Berthommier, Becker, & Fagot, 2017</a>). The aim was to collect a larger database using the technique of deep convolutional neural networks in order to 1) automatically detect vocal productions in a large continuous audio recording and 2) perform a categorization of these vocalizations on a more massive sample. A description of the pipeline that enabled these automatic detections and categorizations is given in <a href="https://arxiv.org/abs/2302.07640">Bonafos, Pudlo, Freyermuth, Legou, Fagot, Tronçon, & Rey (2023)</a>.</p> <p> </p> <p><strong>Data description</strong></p> <p>The data is a set of audio files in wav format. They are at least one second long (the size of the window), up to several minutes, if several windows are consecutively predicted as containing signal. Moreover, we add the labeled data we used to train the CNN which did the prediction. We also provide two hours of the continuous recordings to have an idea of the continuous recordings and test the code of the paper provided on <a href="https://gitlab.com/papers4375727/detection-and-classification-of-vocal-productions">gitlab</a>.</p> <p>In addition, there is a database in csv format listing all the vocalizations, the day and time of their production, and the prediction probabilities of the model.</p> <p> </p> <p><strong>Experimental design, materials and methods</strong></p> <p>The original recordings represent one month of continuous audio recording. Seven hours of this month were manually labelled. They were segmented and labelled according to whether or not there was a monkey vocalization (i.e., noise or vocalization) and, if there was a vocalization, according to the type of vocalization (6 possible classes: bark, copulation grunt, grunt, scream, yak, wahoo). These manually labelled data were used as a training set for a CNN, which was automatically trained following the pipeline of Bonafos et al. (2023). This model was then used to automatically detect and classify vocalization during the whole month of audio recording. It processes the data in the same way when predicting new data as it does when training. It uses a sliding window of one second with an overlap of 80%. It does not take into account information from previous predictions, but calculates the probability of a vocalization in each one-second window independently. It then iterates through the month. For each window, the model predicts two outputs: the probability that there is a vocalization and the probability of each class of vocalization.</p> <p>For the purpose of generating the wav files, if a window has a probability of a vocalization greater than 0.5, it is considered to contain a vocalization. If it is the first one, a vocalization is started at that moment. If the time windows that follow a vocalization also contain a vocalization, then the signal they contain is added to the first segment for which a vocalization has been detected. As soon as a one-second segment no longer contains a signal corresponding to a vocalization, the wav file is closed. If windows are predicted to contain no vocalizations, but are between two windows that contain vocalizations within 1 second of each other, then all windows are merged.</p>
Phlorest phylogeny derived from Greenhill 2015 'TransNewGuinea.org: An Online Database of New Guinea Languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Greenhill, S. J. (2015). TransNewGuinea.org: An Online Database of New Guinea Languages. PLOS ONE, 10(10), e0141563. doi:10.1371/journal.pone.0141563</p> </blockquote>
CLDF dataset derived from Carroll et al. "Yamfinder: The Southern New Guinea Lexical Database"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Carroll, Matthew J., Barth, Wolfgang, Nicholas Evans, I Wayan Arka, Christian Döhler, Eri Kashima, Volker Gast, Tina Gregor, Kate L. Lindsey, Julia Miller, Emil Mittag, Bruno Olsson, Dineke Schokkin, Jeff Siegel, Charlotte van Tongeren, Kyla Quinn. [DATE ACCESSED]. Yamfinder: Southern New Guinea Lexical Database. Available online at: http://www.yamfinder.com</p> </blockquote>
Spatial distribution of oil slicks in the Gulf of Guinea between 2002 and 2012
<p>The map illustrates the spatial distribution of the 18,063 oil slicks that have been detected and then mapped in the Gulf of Guinea over the period 2002-2012 using Envisat ASAR images</p>
100 geolocated oil spills in the Gulf of Guinea
<p>Shapefile containing 100 geolocated oil spills in the Gulf of Guinea and including oil spill from ship and oil spill from platform</p>
Ende (Papua New Guinea) IDS wordlist by Kate Lynn Lindsey
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kate Lynn Lindsey. 2020. Ende (Papua New Guinea) dictionary. In: Key, Mary Ritchie & Comrie, Bernard (eds.) The Intercontinental Dictionary Series. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at https://ids.clld.org/)</p> </blockquote>
National Checklists 2017: Papua New Guinea Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Papua New Guinea collected using effechecka and geonames polygons
National Checklists 2017: Equatorial Guinea Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Equatorial Guinea collected using effechecka and geonames polygons
National Checklists 2017: Guinea Bissau Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>xxxThere are a couple of bugs in this file, line 1914 of taxa and 1927 of occurrences, which causes strange behaviour.xxx FIXED 27 March A list of species from Guinea Bissau collected using effechecka and geonames polygons
National Checklists 2017: Guinea Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Guinea collected using effechecka and geonames polygons
Water Body Checklists 2019: Gulf of Guinea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Gulf of Guinea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
National Checklists 2019: Papua New Guinea Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Papua New Guinea collected using effechecka and geonames polygons
National Checklists 2019: Guinea Bissau Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>xxxThere are a couple of bugs in this file, line 1914 of taxa and 1927 of occurrences, which causes strange behaviour.xxx FIXED 27 March A list of species from Guinea Bissau collected using effechecka and geonames polygons
National Checklists 2019: Equatorial Guinea Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Equatorial Guinea collected using effechecka and geonames polygons
Water Body Checklists: Gulf of Guinea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Gulf of Guinea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model: Supplementary Model Files
<p>Supplementary kinematic model input for the JGR: Solid Earth publication "New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model".</p>
Wordlist files of lexical data from Papua New Guinea and western Solomons Oceanic languages collated for Ross's 1986 PhD thesis and 1988 publication thereof
<p>It occurs to me that the files containing Western Oceanic lexical data that I collected in the late 70s/early 80s for my PhD (Ross 1988) might be useful to someone. They are also used in the volumes of <em>The lexicon of Proto Oceanic </em>(Ross, Pawley & Osmond 1998, 2003, 2011, 2016, 2023). In any case, it is right that they be made publicly available, something that wasn't so easy back then. Most of the material is from wordlists that I collected during fieldwork in Papua New Guinea from around 1978 to 1982. The file cor06 is omitted because it contains SE Solomonic data (outside Western Oceanic) drawn from Tryon & Hackman 1983.</p> <p>I keyed the data into text files in a format such that each line was the entry for a single word, and each field within an entry was marked by a backslash code (I adapted this format from SIL's conventions at the time), then arranged them in cognate sets, each set separated from the next by an empty line. This work was done between 1983 and 1985, when text files were the best way to store data. They were entered on a terminal connected to a mainframe computer at the ANU. I have converted the ASCII symbols used in the original files into UTF-8 here in the interests of readability. The conversion was largely automatic, and I have not done a full check of each file, so there may be glitches.</p> <p>Each file contains languages from a region, as listed below (and the regions sometimes cut across subgroups determined by the comparative method). Three-letter abbreviations are used for language names, and two key files are also provided, one (COR-abbrevs) ordered by regions (determined by the numerals that start each line), the other by alphabetical order of language name (COR-abbrevs-alph). Some three-letter codes are followed by a hyphen and an extra letter. These are dialects. For example, MUM stands for Mumeng and MUM-P for the Patep dialect of Mumeng.</p> <p>Data files are labelled with COR (for 'correspondence sets') plus a numeral. The numerals are: 1-3 New Ireland; 4 Willaumez Peninsula (New Britain) area; 5 NW Solomonic; 7+8 Papuan Tip; 9 Vitiaz Strait area and NG north coast; 10 Huon Gulf and Markham Valley; 11 South and west New Britain. 7+8 are partial only. When I keyed the files, I had to rely on a mainframe's nightly back-up onto tape spools. One night the system failed, and so did the restore, and I lost some data.</p> <p>The backslash codes in the data files are: \l language; \p protolanguage; \w word; \g gloss; \n note; \s source. The formatting of these files is a little odd, since they served as input to routines I wrote to pull out sound correspondences. Anything after '%' is the elicited form: what immediately precedes '%' has had something 'undone', e.g. metathesis.</p> <p>The orthography of the files is phonemic and largely obvious. The conventions are set out in the introductions to the volumes of <em>The lexicon of Proto Oceanic.</em></p> <p>Finally, the files also contain reconstructions at various interstages at the top of a cognate set. These were inserted for heuristic reasons during my research. Many of them did not survive into my PhD thesis, and they should preferably be ignored. The reader who is interested in current Oceanic reconstructions should turn to the volumes of <em>The lexicon of Proto Oceanic.</em></p>
Fig. 2 in Missing geographic link: minute lady beetles (Coleoptera: Coccinellidae: Microweiseinae) from Mount Wilhelm, New Guinea
Fig. 2. Morphology of Scymnomorphus species. A–H – S. bimaculatus sp. nov.: A – abdomen, female; B – antenna; C – ovipositor; D – spermatheca; E – tegmen; inner view; F – tegmen, lateral view; G – penis, lateral view; H – abdomen, male. I–P – S. kausi sp. nov.: I – abdomen, female; J – antenna; K – ovipositor; L – spermatheca; M – tegmen, inner view; N – tegmen, lateral view; O – penis, lateral view; P – abdomen, male (arrows indicate glandular opening pores).
Figures 1-4 in A New Species Of Eupholus Boisduval (Coleoptera: Curculionidae: Entiminae) From West New Guinea
Figures 1-4. Male genitalia of Eupholus bhaskarai sp. nov. holotype; 1. Aedeagus in dorsal view; 2. Aedeagus in lateral view; 3. Transfer apparatus, resting position; 4. Paramers of tegmen.
Figure 13 in A New Species Of Eupholus Boisduval (Coleoptera: Curculionidae: Entiminae) From West New Guinea
Figure 13. Locality of Eupholus species; E. bhaskarai (red dot); E. loriae (blue dot and blue areas).
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