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901 results for “Modality”
Datasets for Material Modal Composite Analysis results in Duanjiapo loess section
<p>The dataset is affilicated to the manuscript titled "Genesis of Loess Particles on the Chinese Loess Plateau" that is submitted for publication in the journal of Geochemistry, Geophysics, Geosystems. The original instrumental data packages were provided in the excel files (Data1, Data2, Data3, and Data4).</p>
Data and code availibility for the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al.
<p>The data and codes used in the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al., published in <em>Science Advances</em> are included in this collection. Detailed descriptions of the files are given in the readme file.</p>
Cross-modal representation of identity in primate hippocampus
<p>Faces and voices are the dominant social signals used to recognize individuals amongst human and nonhuman primates. Yet, evidence that information across these signals can be integrated into a modality-independent representation of individual identity in the primate brain has been reported only in human patients. Here we show that, like humans, single neurons in the marmoset monkey hippocampus exhibit invariant neural responses when presented with the faces or voices of specific individuals. However, we also identified a population of single neurons in hippocampus that was responsive to the cross-modal identity of multiple conspecifics, not only a single individual. An identity network model revealed population-level, cross-modal representations of individuals in hippocampus, underscoring the broader contributions of many neurons to encode identity. This pattern was further evidenced by manifold projections of population activity which likewise showed separability of individuals, as well as clustering for family members, suggesting that multiple learned social categories are encoded as related dimensions of identity in hippocampus. The constellation of findings presented here reveals a novel perspective on the neural basis of identity representations in primate hippocampus as being both invariant to modality and comprising multiple levels of acquired social knowledge.</p>
Multi-Modal Population Dataset for Sub-Saharan Africa
<p>A multi-modal population dataset for three cities in Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania). The dataset contains patches of rasterized building footprints (0.5 m), Sentinel-2 MSI data (10 m) and population labels at the patch level (100 m).</p>
HKI Buku Konsep Yang Mempengaruhi Alokasi Belanja Modal, 2022
<p><strong>Hak atas Kekayaan Intelektual (HaKI) : Pengertian dan Jenisnya</strong></p> <p>Istilah HaKI atau Hak atas Kekayaan Intelektual merupakan terjemahan dari Intellectual Property Right (IPR), sebagaimana diatur dalam undang-undang No. 7 Tahun 1994 tentang pengesahan WTO (Agreement Establishing The World Trade Organization). Pengertian Intellectual Property Right sendiri adalah pemahaman mengenai hak atas kekayaan yang timbul dari kemampuan intelektual manusia, yang mempunyai hubungan dengan hak seseorang secara pribadi yaitu <a href="http://uma.ac.id/">hak asasi manusia (human right).</a></p> <p>HaKI atau Hak atas Kekayaan Intelektual adalah hak eksklusif yang diberikan suatu hukum atau peraturan kepada seseorang atau sekelompok orang atas karya ciptanya. Pada intinya HaKI adalah hak untuk menikmati secara ekonomis hasil dari <a href="http://lp2m.uma.ac.id/">suatu kreativitas intelektual</a>. Objek yang diatur dalam HaKI adalah karya-karya yang timbul atau lahir karena kemampuan intelektual manusia.</p> <p>Setiap hak yang digolongkan ke dalam HaKI harus mendapat kekuatan hukum atas karya atau ciptannya. Untuk itu diperlukan tujuan penerapan HaKI. Tujuan dari penerapan HaKI yang Pertama, antisipasi kemungkinan melanggar HaKI milik pihak lain, Kedua meningkatkan daya kompetisi dan pangsa pasar dalam komersialisasi kekayaan intelektual, Ketiga dapat dijadikan sebagai bahan pertimbangan dalam penentuan strategi penelitian, usaha dan industri di Indonesia.</p> <p><strong>Pentingnya HAKI</strong></p> <p>Lalu bagaimana apabila karya kita atau milik orang lain tidak dilindungi? Sudah pasti dipastikan akan terkena pembajakan. Sebegai contoh untuk di dunia pendidikan saat ini marak adanya pembajakan buku. Pembajakan buku ini makin marak terjadi di masyarakat, banyak faktor yang menyebabkan terjadinya pembajakan buku, salah satunya adalah kurangnya penegakan hukum, ketidaktahuan masyarakat terhadap perlindungan hak cipta buku, dan kondisi ekonomi masyarakat.</p> <p>Sudah banyak pelaku terjaring oleh aparat, dan masih banyak pula yang masih berkeliaran dan tumbuh, seiring tingginya permintaan oleh masyarakat. Untuk itu butuh kesadaran dari masyarakat untuk mengetahui HaKI agar karyanya tidak diambil oleh orang lain. Berikut ini terdapat macam-macam HaKI</p> <p><strong>Macam-macam HaKI (Hak atas Kekayaan Intelektual)</strong></p> <p>1. Hak Cipta</p> <p>Hak Cipta adalah hak khusus bagi pencipta untuk mengumumkan atau memperbanyak ciptaannya. Termasuk ciptaan yang dilindungi adalah ciptaan dalam bidang ilmu pengetahuan, sastra dan seni.</p> <p>Hak cipta diberikan terhadap ciptaan dalam ruang lingkup bidang ilmu pengetahuan, kesenian, dan kesusasteraan. Hak cipta hanya diberikan secara eksklusif kepada pencipta, yaitu “seorang atau beberapa orang secara bersama-sama yang atas inspirasinya lahir suatu ciptaan berdasarkan pikiran, imajinasi, kecekatan, keterampilan atau keahlian yang dituangkan dalam bentuk yang khas dan bersifat pribadi.</p> <p>2. Hak Kekayaan Industri yang Meliputi</p> <p>A. Paten</p> <p>Berdasarkan Undang-Undang Nomor 14 Tahun 2001 Pasal 1 Ayat 1, Paten adalah hak eksklusif yang diberikan oleh Negara kepada Inventor atas hasil invensinya di bidang teknologi, yang untuk selama waktu tertentu melaksanakan sendiri invensinya tersebut atau memberikan persetujuannya kepada pihak lain untuk melaksanakannya.</p> <p>Paten hanya diberikan negara kepada penemu yang telah menemukan suatu penemuan (baru) di bidang teknologi. Yang dimaksud dengan penemuan adalah kegiatan pemecahan masalah tertentu di bidang teknologi yang berupa : Proses, hasil produksi, penyempurnaan dan pengembangan proses, penyempurnaan dan pengembangan hasil produksi.</p> <p>B. Merek</p> <p>Berdasarkan Undang-Undang Nomor 15 Tahun 2001 Pasal 1 Ayat 1 Merek adalah tanda yang berupa gambar, nama, kata, huruf-huruf, angka-angka, susunan warna, atau kombinasi dari unsur-unsur tersebut yang memiliki daya pembeda dan digunakan dalam kegiatan perdagangan barang atau jasa.</p> <p>Jadi merek merupakan tanda yang digunakan untuk membedakan produk (barang dan atau jasa) tertentu dengan yang lainnya dalam rangka memperlancar perdagangan, menjaga kualitas, dan melindungi produsen dan konsumen.</p> <p>Terdapat beberapa istilah merek yang biasa digunakan, yang pertama merek dagang adalah merek yang digunakan pada barang yang diperdagangkan oleh seseorang atau beberapa orang secara bersama-sama atau badan hukum untuk membedakan dengan barang-barang sejenis lainnya.</p> <p>Merek jasa yaitu merek yang digunakan pada jasa yang diperdagangkan oleh seseorang atau beberapa orang secara bersama-sama atau badan hukum untuk membedakan dengan jasa-jasa sejenis lainnya.</p> <p>Merek kolektif adalah merek yang digunakan pada barang atau jasa dengan karakteristik yang sama yang diperdagangkan oleh beberapa orang atau badan hukum secara bersama-sama untuk membedakan dengan barang atau jasa sejenis lainnya.</p> <p>Hak atas merek adalah hak khusus yang diberikan negara kepada pemilik merek yang terdaftar dalam Daftar Umum Merek untuk jangka waktu tertentu, menggunakan sendiri merek tersebut atau memberi izin kepada seseorang atau beberapa orang secara bersama-sama atau badan hukum untuk menggunakannya.</p> <p>C. Desain Industri</p> <p>Berdasarkan Undang-Undang Nomor 31 Tahun 2000 Pasal 1 Ayat 1 Tentang Desain Industri, bahwa desain industri adalah suatu kreasi tentang bentuk, konfigurasi, atau komposisi garis atau warna, atau garis dan warna, atau gabungan daripadanya yang berbentuk tiga dimensi atau dua dimensi yang memberikan kesan estetis dan dapat diwujudkan dalam pola tiga dimensi atau dua dimensi serta dapat dipakai untuk menghasilkan suatu produk, barang, komoditas industri, atau kerajinan tangan.</p> <p>D. Desain Tata Letak Sirkuit Terpadu</p> <p>Berdasarkan Undang-Undang Nomor 32 Tahun 2000 Pasal 1 Ayat 1 Tentang Desain Tata Letak Sirkuit Terpadu bahwa, Sirkuit Terpadu adalah suatu produk dalam bentuk jadi atau setengah jadi, yang di dalamnya terdapat berbagai elemen dan sekurang-kurangnya satu dari elemen tersebut adalah elemen aktif, yang sebagian atau seluruhnya saling berkaitan serta dibentuk secara terpadu di dalam sebuah bahan semikonduktor yang dimaksudkan untuk menghasilkan fungsi elektronik.</p> <p>E. Rahasia Dagang</p> <p>Menurut Undang-Undang Nomor 30 Tahun 2000 Tentang Rahasia Dagang bahwa, Rahasia Dagang adalah informasi yang tidak diketahui oleh umum di bidang teknologi dan/atau bisnis, mempunyai nilai ekonomi karena berguna dalam kegiatan usaha, dan dijaga kerahasiaannya oleh pemilik Rahasia Dagang.</p> <p>F. Indikasi Geografis</p> <p>Berdasarkan Undang-Undang No. 15 Tahun 2001 Pasal 56 Ayat 1 Tentang Merek bahwa, Indikasi-geografis dilindungi sebagai suatu tanda yang menunjukkan daerah asal suatu barang yang karena faktor lingkungan geografis termasuk faktor alam, faktor manusia, atau kombinasi dari kedua faktor tersebut, memberikan ciri dan kualitas tertentu pada barang yang dihasilkan.</p> <p><strong>Prinsip-Prinsip HaKI atau Hak atas Kekayaan Intelektual</strong></p> <p><strong>Prinsip-prinsip Hak atas Kekayaan Intelektual (HaKI) adalah sebagai berikut :</strong></p> <p><strong>1. Prinsip Ekonomi</strong><br> Dalam prinsip ekonomi, hak intelektual berasal dari kegiatan kreatif dari daya pikir manusia yang memiliki manfaat serta nilai ekonomi yang akan member keuntungan kepada pemilik hak cipta.</p> <p><strong>2. Prinsip Keadilan</strong><br> Prinsip keadilan merupakan suatu perlindungan hukum bagi pemilik suatu hasil dari kemampuan intelektual, sehingga memiliki kekuasaan dalam penggunaan hak atas kekayaan intelektual terhadap karyanya.</p> <p><strong>3. Prinsip Kebudayaan</strong><br> Prinsip kebudayaan merupakan pengembangan dari ilmu pengetahuan, sastra dan seni guna meningkatkan taraf kehidupan serta akan memberikan keuntungan bagi masyarakat, bangsa dan Negara.</p> <p><strong>4. Prinsip Sosial</strong><br> Prinsip sosial mengatur kepentingan manusia sebagai warga Negara, sehingga hak yang telah diberikan oleh hukum atas suatu karya merupakan satu kesatuan yang diberikan perlindungan berdasarkan keseimbangan antara kepentingan individu dan masyarakat/lingkungan.</p>
Pertemuan 3, MEMAHAMI PENGERTIAN PASAR MODAL, 13 Oktober 2022 Universitas Wijaya Putra MM
<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Mahasiswa Mampu Memahami Pengertian Pasar Modal</p> <p><strong>Pengertian Pasar Modal</strong></p> <p>Pengertian pasar modal yang berdasarkan isi Undang-Undang No. 21 tahun 2011 tentang Otoritas Jasa Keuangan, dalam undang-undang tersebut disebutkan bahwa pasar modal merupakan suatu bentuk kegiatan yang bersangkutan dengan penawaran umum dan perdagangan efek perusahaan publik yang berkaitan dengan efek yang diterbitkannya, serta lembaga dan profesi yang berkaitan dengan efek sebagaimana dimakslud dalam UU mengenai pasar modal</p>
Data Repository for the article: "Control of multi-modal scattering in a microwave frequency comb"
<p><strong>Abstract:</strong></p> <p>Control over the coupling between multiple modes of a frequency comb is an important step toward measurement-based quantum computation with a continuous-variable system.<br>We demonstrate the creation of square-ladder correlation graphs in a microwave comb with 95 modes.<br>The graphs are engineered through precise control of the relative phase of three pumps applied to a Josephson parametric oscillator. <br>Experimental measurement of the mode scattering matrix is in good agreement with theoretical predictions based on a linearized equation of motion of the parametric oscillator. <br>The digital methods used to create and measure the correlations are easily scaled to more modes and more pumps, with the potential to tailor a specific correlation graph topology.</p> <p> </p> <p>Article DOI:</p> <p>ArXiV: <a href="https://doi.org/10.48550/arXiv.2402.09068">arXiv.2402.09068</a></p> <p> </p> <p><strong>Content:</strong></p> <p>This repository contains the datased used in the article "Control of multi-modal scattering in a microwave frequency comb". Scripts to generate the figures are also included.</p>
VHAKG: Multi-modal Knowledge Graphs with Multi-view Videos of Daily Activities
<h2>Outline</h2> <ul> <li>This dataset is a multimodal knowledge graph (MMKG) of daily activity videos.</li> <li>This dataset integrates a KG with embedded multi-view videos created by <a href="https://github.com/aistairc/virtualhome_aist">VirtualHome-AIST</a>, an extended version of the VirtualHome simulator, and an event-centric KG generated by <a href="https://github.com/aistairc/virtualhome2kg">VirtualHome2KG</a>.</li> <li>We named this dataset <strong>VHAKG</strong> (VirtualHome-AIST-KG).</li> </ul> <h2>Details</h2> <ul> <li>VHAKG describes 2D bounding boxes of objects every five frames, compositional activities, primitive actions, target objects, object states, 3D bounding boxes, and their time-series changes.</li> <li>The videos are encoded in base64 and embedded as a literal value.</li> <li>VHAKG consists of 706 daily activity scenarios (e.g., clean desk, cook fried bread, and relax on sofa) and 3,530 videos captured by five synchronized cameras per scenario.</li> <li>The file format is <a href="https://www.w3.org/RDF/">RDF</a> (<a href="https://en.wikipedia.org/wiki/Turtle_(syntax)">Turtle</a>), which can be loaded into various <a href="https://en.wikipedia.org/wiki/Triplestore">Triplestores</a>.</li> <li>VHAKG's vocabularies are defined as an ontology and can be found in vh2kg_schema_v2.0.0.ttl.</li> </ul> <h2>Contents</h2> <ul> <li>vh2kg_video_base64.tar.gz <ul> <li><strong>{activity name}{scene}_{camera}_2dbbox.ttl</strong>: KG with video embedded in base64 format, including 2D bounding box data every 5 frames. <ul> <li>To learn more about {scene}, check <a href="https://github.com/xavierpuigf/virtualhome/tree/v2.2.0/simulation#environment">here</a>.</li> <li>To learn more about {camera}, check <a href="https://github.com/aistairc/virtualhome_unity_aist?tab=readme-ov-file#addition-of-new-four-cameras">here</a>.</li> </ul> </li> </ul> </li> <li>vh2kg_event.tar.gz <ul> <li><strong>{activity name}_{scene}.ttl</strong>: Event-centric KGs representing video content as sequences of events.</li> <li><strong>vh2kg_schema_v2.0.0.ttl</strong>: The ontology file of this dataset.</li> <li><strong>affordance.ttl</strong>: The affordance data of objects that were created by crowdsourcing. <ul> <li>Please see Section III.B of <a href="https://doi.org/10.1109/ACCESS.2023.3253807" target="_blank" rel="noopener">this paper</a> for more information.</li> </ul> </li> <li><strong>add_places.ttl</strong>: Events in which agents moved from one room to another.</li> </ul> </li> </ul> <h2>Tools</h2> <ul> <li><a href="https://github.com/aistairc/vhakg-tools">A set of tools</a> for searching and extracting videos from VHAKG is available.</li> </ul>
Representation learning for multi-modal spatially resolved transcriptomics data
<p>This folder contains the already unified input used for the models. The raw data is referenced here:</p> <ul> <li>LIBD Human DLPFC dataset is available at <a href="https://github.com/LieberInstitute/HumanPilot">https://github.com/LieberInstitute/HumanPilot</a> and <a href="http://research.libd.org/spatialLIBD" rel="nofollow">http://research.libd.org/spatialLIBD</a>;</li> <li>Human Breast Cancer - Zenodo <a href="https://doi.org/10.5281/zenodo.4739739" rel="nofollow">https://doi.org/10.5281/zenodo.4739739</a>,</li> <li>Human Liver Normal and Cancer - <a href="https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/" rel="nofollow">https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/</a>.</li> </ul>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
QROWD/modal_split_ontology: QROWD Modal Split Ontology v0.1.0
<p>Initial version</p>
Addiitonal Files for The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling
<p>Additional Files 1-3 for "The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling"</p> <p> </p>
Modality-specific dysfunctional neural processing of social-abstract and non-social-concrete information in schizophrenia
<p>Schizophrenia is characterized by marked communication dysfunctions encompassing potential impairments in the processing of social-abstract and non-social-concrete information, especially in everyday situations where multiple modalities are present in the form of speech and gesture. To date, the neurobiological basis of these deficits remains elusive. In a functional magnetic resonance imaging (fMRI) study, 17 patients with schizophrenia or schizoaffective disorder, and 18 matched controls watched videos of an actor speaking, gesturing (unimodal), and both speaking and gesturing (bimodal) about social or non-social events in a naturalistic way. Participants were asked to judge whether each video contains person-related (social) or object-related (non-social) information. When processing social-abstract content, patients showed reduced activation in the medial prefrontal cortex (mPFC) only in the gesture but not in the speech condition. For non-social-concrete content, remarkably, reduced neural activation for patients in the left postcentral gyrus and the right insula was observed only in the speech condition. Moreover, in the bimodal conditions, patients displayed improved task performance and comparable activation to controls in both social and non-social content. To conclude, patients with schizophrenia displayed modality-specific aberrant neural processing of social and non-social information, which is not present for the bimodal conditions. This finding provides novel insights into dysfunctional multimodal communication in schizophrenia, and may have potential therapeutic implications.</p>
A multi-modal sensor dataset for continuous stress detection of nurses in a hospital
<p>Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, especially because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to the influence of many social, cultural, and individuals experience in dealing with stressful conditions. In order to address these concerns, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables, including electrodermal activity, heart rate, skin temperature, and accelerometer data of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is available upon request.</p>
PERTEMUAN 8_17 NOVEMBER 2022_EFISIENSI PASAR MODAL_ANALISIS INVESTASI DAN MANAJEMEN RISIKO (AIMR)_PASCA MM_UNIVERSITAS WIJAYA PUTRA
<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Mahasiswa mampu menjelaskan : Efisiensi Pasar Modal</p> <p><strong>A</strong><strong>pa itu Efisiensi Pasar Modal?</strong></p> <p><strong>Fama (1970)</strong>, <em>a market is efficient when “stock price” is always fully reflect all available information. </em>Pasar efisien ketika “harga saham” selalu sepenuhnya mencerminkan semua informasi yang tersedia.</p> <p></p> <p><strong> Husnan (2005)</strong>, pasar modal yang efisien merupakan <strong>pasar yang harga</strong><strong>-</strong><strong>harga sekuritas-sekuritasnya telah mencerminkan semua informasi yang relevan</strong>. Semakin cepat informasi baru tercermin pada harga sekuritas, maka semakin efisien pasar tersebut.</p> <p></p> <p><strong>Tandelilin (2017),</strong> <strong>konsep</strong><strong> </strong><strong>pasar</strong><strong> yang </strong><strong>efisien</strong><strong> </strong><strong>lebih</strong><strong> </strong><strong>ditekankan</strong><strong> </strong><strong>pada</strong><strong> </strong><strong>aspek</strong><strong> </strong><strong>informasi</strong>, artinya pasar yang efisien adalah pasar dimana harga sekuritas yang<br> diperdagangkan telah mencerminkan semua informasi yang tersedia.</p>
Concurrent tDCS with multi-modal MRI - Experiment 2
<p>The provided dataset includes 9 subjects (7 female) with DTI and pASL imaging before (run 1), during (run 2, run 3), and after (run 4, run 5) transcranial direct-current stimulation (tDCS). Subjects were recruited to CUNY IRB standards and provided informed consent on the publication of their data. The two sessions, Active and Sham, indicate whether subjects were stimulated with 4mA anodal tDCS (AF7,F7 - Anode, AF8, F8 - Cathode) or Sham (30 sec ramp-up and ramp-down) during the stimulation (run 2, run 3) time points. </p> <p>Raw DTI and pASL files are provided in Nifti (.nii) format with an associated T1w anatomical image. All data has been de-identified to preserve subject anonymity. Data is organized to BIDS standards for ease of processing. It is anticipated that MRI data with concurrent tDCS will benefit the field and allow others to analyze, validate tools, or compare with non-stimulated control measurements. </p>
Concurrent tDCS with multi-modal MRI - Experiment 1
<p>The provided dataset includes 20 subjects (6 female) with Arterial Transit Time (ATT), water exchange across the BBB (Kw) and cerebral blood flow (CBF) imaging before (run 1), during (run 2, run 3), and after (run 4, run 5) transcranial direct-current stimulation (tDCS). Subjects were recruited to CUNY IRB standards and provided informed consent on the publication of their data. The three sessions, (ses-1, ses-2, and ses-3), indicate whether subjects were stimulated Sham current (30 sec ramp-up and ramp-down), 2mA anodal tDCS (AF7 - Anode, AF8 - cathode) or 4mA anodal tDCS (AF7,F7 - Anode, AF8, F8 - Cathode), respectively.</p> <p>Pre-processed files are provided in Nifti (.nii) format with an associated T1w anatomical image. All data has been de-identified to preserve subject anonymity. Data is organized to BIDS standards for ease of processing. It is anticipated that MRI data with concurrent tDCS will benefit the field and allow others to analyze, validate tools, or compare with non-stimulated control measurements. </p>
National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets
<p>Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.</p>
The Role of Spatial Information in an Approximate Cross-Modal Number Matching Task
<p>Data for article:</p> <p>The Role of Spatial Information in an Approximate Cross-Modal Number Matching Task</p>
MMpedia: A Large-scale Multi-modal Knowledge Graph
<p><strong>List of files:</strong></p> <ul> <li>entity2image.json: The entity to map images file</li> <li>MMpedia_triples.ttl: The triples file</li> <li>EntlistXX.tar: The image file</li> </ul> <p>MMpedia dataset is split into 132 subsets and each subset is compressed into a <code>.tar</code> file. After unziping files under the folder "MMpedia", the data structure are as following:</p> <pre><code>|-MMpedia |-Entitylist1 |-Entity1 |-1.jpg |-2.jpg |-3.jpg ... |-Entity2 |-Entity3 ... |-Entitylist2 |-Entitylist3</code></pre> <p>For example, the path <code>MMpedia/Entlist141/Bart Tanski/Bart Tanski+1.jpg</code> means the image corresponding to the entity "Bart Tanski"</p> <p><strong>Other image files can be found in following URLs:</strong></p> <p><a href="https://zenodo.org/record/7854781#.ZEU7Uc7iu38">MMpedia2 | Zenodo</a></p> <p><a href="https://zenodo.org/record/7855010">MMpedia3 | Zenodo</a></p> <p><a href="https://zenodo.org/record/7855226">MMpedia4 | Zenodo</a></p> <p> <strong>For more details, please refer to </strong><a href="https://github.com/Delicate2000/MMpedia">Delicate2000/MMpedia (github.com)</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.