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361 results for “January”
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Maximum Extent
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2) and Landsat-8 (L8) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included in supplementary datasets. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p> <p> </p>
QUIZ PERTEMUAN 14 TANGGAL 04 JANUARY 2023_MANAJEMEM KEUANGAN SEKTOR PUBLIK_STIESIA SURABAYA
<p><strong>Capaian Pembelajaran Mata Kuliah (MKSP)</strong></p> <p><strong>Mahasiswa mampu memahami dari materi pertemuan ke delapan sampai dengan 12</strong></p> <p><strong>Indikator Penilaian</strong></p> <p><strong>1. Tes Lisan</strong></p> <p><strong>2. Tes Tulis</strong></p>
PERTEMUAN 2_05 JANUARY 2023_Perngertian dan Dasar Hukum Pph_PERPAJAKAN_UNIVERSITAS WIJAYA PUTRA PRIGEN JAM 18.00-21.30
<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p><strong>Setelah</strong><strong> </strong><strong>megikuti</strong><strong> </strong><strong>perkuliahan</strong><strong> </strong><strong>ini</strong><strong> </strong><strong>mahasiswa</strong><strong> </strong><strong>Mampu</strong><strong> </strong><strong>menjelaskan</strong><strong> </strong><strong>pengertian</strong><strong> </strong><strong>dan</strong><strong> </strong><strong>dasar</strong><strong> </strong><strong>hukum</strong><strong> </strong><strong>PPh</strong></p> <p><strong>Indikator</strong><strong> </strong><strong>Pembelajaran</strong></p> <p>Gambaran Umum tentang Pajak Penghasilan (PPh)</p> <p>1.Pengertian dan Dasar Hukum;</p> <p>2.Garis Besar Penghitungan PPh.</p> <p>3.Subjek PPh</p> <p>1) Subjek dan bukan subjek PPh;</p> <p>2) Mulai dan berakhirnya kewajiban pajak subjektif.</p>
PERTEMUAN 13_05 JANUARY_2023_PPh Pasal 4 Ayat (2)&PPh Final Lainya-PERPAJAKAN _UWP_WIYUNG_JAM 08.00-09.30
<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Mahasiswa Mampu menjelaskan siapa Pemungut dan dan teknis menghitung PPh Pasal 4 (ayat 2) dan PPh Final lainnya</p> <p><strong>Indikator</strong><strong> </strong><strong>Pembelajaran</strong></p> <p>PPh Pasal Pasaal 4 (ayat 2) dan PPh Final lainnya</p> <p>1.Pemungut dan pihak yang dipungut PPh Pasal 4 (ayat)2</p> <p>2.Objek Yang Dipotong PPh </p> <p>3.Pasal 4 (ayat 2);</p> <p>4.Tarif, Dasar Pengenaan Pajak dan Penghitungan PPh Pasal 4 (ayat 2);</p> <p>5. Contoh dan Penerapannya</p> <p> </p>
PERTEMUAN 14_12 JANUARY 2023_pengertian dan dasar hukum PPN dan PPn BM_UNIVERSITAS WIJAYA PUTRA_WIYUNG JAM 8.00-9.30
<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p>Mahasiswa Mampu menjelaskan pengertian dan dasar hukum PPN dan PPn BM;</p> <p>Objek, Pengusaha Kena Pajak;</p> <p><strong>Indikator Pembelajaran</strong></p> <p>Gambaran Umum Kebijakan PPN dan PPn BM</p> <ol> <li>Pengertian dan Dasar Hukum;</li> <li>Objek, Pengusaha Kena Pajak;</li> <li>Bukan Objek PPN dan Bukan Pengusaha Kena Pajak;</li> <li>Tarif PPN dan PPn BM;</li> <li>Mekanisme Penghitungan PPN;</li> <li>Pencatatan dan Pembukuan;</li> <li>Saat dan Tempat Terutang</li> </ol>
PERTEMUAN 4_19 JANUARY 2023_PENGURANGAN PENGHASILAN KENA PAJAK _UNIVERSITAS WIJAYA PUTRA_PRIGEN
<p><strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong></p> <p><strong>Mahasiswa</strong><strong> </strong><strong>mampu</strong><strong> </strong><strong>menjelaskan</strong><strong> </strong><strong>pengurangan</strong><strong> </strong><strong>dalam</strong><strong> </strong><strong>rangka</strong><strong> </strong><strong>menghitung</strong><strong> PKP: Yang </strong><strong>boleh</strong><strong> </strong><strong>dikurangkan</strong><strong>, Yang </strong><strong>tidak</strong><strong> </strong><strong>boleh</strong><strong> </strong><strong>dikurangkan</strong><strong>, </strong><strong>Kompensasi</strong><strong> </strong><strong>kerugian</strong></p> <p> </p> <p><strong>Indikator</strong><strong> </strong><strong>Pembelajaran</strong></p> <p>Penghitungan Penghasilan Kena Pajak (PKP)</p> <p>1.Yang Boleh Dikurangkan dalam rangka menghitung PKP;</p> <p>2.Yang Tidak Boleh Dikurangkan dalam rangka menghitung PKP.</p> <p>3.Kompensasi Kerugian.</p>
PERTEMUAN 5_26 JANUARY 2023_PTKP DAN PPh TERUTANG_UNIVERSITAS WIJAYA PUTRA_PRIGEN
<p>Capaian Pembelajaran Mata Kuliah (CPMK)</p> <p><strong>Mahasiswa</strong><strong> </strong><strong>mampu</strong><strong> </strong><strong>menjelaskan</strong><strong> </strong><strong>dan</strong><strong> </strong><strong>menghitung</strong><strong>: </strong><strong>Penghasilan</strong><strong> </strong><strong>Tidak</strong><strong> </strong><strong>kena</strong><strong> </strong><strong>pajak</strong><strong> (PTKP), </strong><strong>PPh</strong><strong> </strong><strong>Terhutang</strong></p> <p><strong>Indikator</strong><strong> </strong><strong>Pembelajaran</strong></p> <p><strong>Penghasilan</strong><strong> </strong><strong>Tidak</strong><strong> </strong><strong>Kena</strong><strong> </strong><strong>Pajak</strong><strong> (PTKP)</strong></p> <p>1.Besarnya PTKP;</p> <p>2.Hal-hal Yang mempengaruhi</p> <p><strong>Penghitungan</strong><strong> </strong><strong>PPh</strong><strong> </strong><strong>Terhutang</strong></p> <p>1.Tarif PPh Orang Pribadi;</p> <p>2.Tarif PPh Badan;</p> <p>3.Fasilitas Pengurangan Tarif (Pasal 31 E UU PPh).</p>
MATERI SEMINAR METODE PENELITIAN KUANTITATIF_UNIVERSITAS NEGERI SURABAYA_ KAMIS 26 JANUARY 2023
<p><strong>Penelitian Kuantitatif</strong></p> <p><strong>Penelitian kuantitatif adalah</strong> suatu proses menemukan pengetahuan yang menggunakan data berupa angka sebagai alat menganalisis keterangan mengenai apa yang ingin diketahui. (Kasiram (2008: 149) dalam bukunya Metodologi Penelitian Kualitatif dan Kuantitatif).</p> <p>Penelitian Kuantitatif Adalah</p> <p>Asumsi Penelitian Kuantitatif Adalah</p> <p>Penelitian kuantitatif adalah didasarkan pada asumsi sebagai berikut (Nana Sudjana dan Ibrahim, 2001; Del Siegle, 2005, dan Johnson, 2005).</p> <p> </p> <p>a. Bahwa realitas yang menjadi sasaran penelitian berdimensi tunggal, fragmental, dan cenderung bersifat tetap sehingga dapat diprediksi.</p> <p>b. <a href="https://www.statistikian.com/2012/10/variabel-penelitian.html">Variabel</a> dapat diidentifikasi dan diukur dengan alat-alat yang objektif dan baku.</p> <p>Karakeristik Penelitian Kuantitatif Adalah</p> <p>Karakteristik penelitian kuantitatif adalah sebagai berikut (Nana Sudjana dan Ibrahim, 2001: 6-7; Suharsimi Arikunto, 2002 : 11; Johnson, 2005; dan Kasiram 2008: 149-150):</p> <p>a. Menggunakan pola berpikir deduktif (rasional – empiris atau top-down), yang berusaha memahami suatu fenomena dengan cara menggunakan konsep-konsep yang umum untuk menjelaskan fenomena-fenomena yang bersifat khusus.</p> <p>b. Logika yang dipakai adalah logika positivistik dan menghundari hal-hal yang bersifat subjektif.</p> <p>c. Proses penelitian mengikuti prosedur yang telah direncanakan.</p> <p> </p> <p>d. Tujuan dari penelitian kuantitatif adalah untuk menyususun ilmu nomotetik yaitu ilmu yang berupaya membuat hokum-hukum dari generalisasinya.</p> <p>e. Subjek yang diteliti, data yang dikumpulkan, dan sumber data yang dibutuhkan, serta alat pengumpul data yang dipakai sesuai dengan apa yang telah direncanakan sebelumnya.</p> <p>f. Pengumpulan data dilakukan melalui pengukuran dengan mengguna-kan alat yang objektif dan baku.</p> <p>g. Melibatkan penghitungan angka atau kuantifikasi data.</p> <p>h. Peneliti menempatkan diri secara terpisah dengan objek penelitian, dalam arti dirinya tidak terlibat secara emosional dengan subjek penelitian.</p> <p>i. Analisis data dilakukan setelah semua data terkumpul.</p> <p>j. Dalam analisis data, peneliti dituntut memahami teknik-teknik statistik.</p> <p>k. Hasil penelitian berupa generalisasi dan prediksi, lepas dari konteks waktu dan situasi.</p> <p>l. Penelitian jenis kuantitatif disebut juga penelitian ilmiah</p> <p>Prosedur Penelitian Kuantitatif</p> <p>Penelitian kuantitatif ini dalam pelaksanaannya adalah berdasarkan prosedur yang telah direncanakan sebelumnya.</p> <p><strong>Adapun prosedur penelitian kuantitatif terdiri dari tahapan-tahapan atau langkah-langkah kegiatan sebagai berikut:</strong></p> <ol> <li>Identifikasi permasalahan</li> <li>Studi literatur.</li> <li>Pengembangan kerangka konsep</li> <li>Identifikasi dan definisi <a href="https://www.statistikian.com/2012/10/variabel-penelitian.html">variabel</a>, <a href="https://www.statistikian.com/2012/10/hipotesis.html">hipotesis</a>, dan pertanyaan penelitian.</li> <li>Pengembangan disain penelitian.</li> <li><a href="https://www.statistikian.com/2017/06/teknik-sampling-dalam-penelitian.html">Teknik sampling</a>.</li> <li>Pengumpulan dan kuantifikasi data.</li> <li>Analisis data.</li> <li>Interpretasi dan komunikasi hasil penelitian.</li> </ol> <p>Tipe-tipe Penelitian Kuantitatif</p> <p>Dalam melakukan penelitian, peneliti dapat menggunakan metoda dan <a href="https://www.statistikian.com/2012/05/desain-penelitian-pengantar.html">rancangan (design)</a> tertentu dengan mempertimbangkan tujuan penelitian dan sifat masalah yang dihadapi.</p> <p> </p> <p>Berdasarkan sifat-sifat permasalahannya, penelitian kuantitatif adalah dapat dibedakan menjadi beberapa tipe sebagai berikut (Suryabrata, 2000 : 15 dan Sudarwan Danim dan Darwis, 2003 : 69 – 78).</p> <p><strong>Jenis-jenis Penelitian Kuantitatif Adalah:</strong></p> <ol> <li>Penelitian deskriptif</li> <li>Penelitian korelasional</li> <li>Penelitian kausal komparatif</li> <li>Penelitian tindakan</li> <li>Penelitian perkembangan</li> <li><a href="https://www.statistikian.com/2012/10/penelitian-experimen.html">Penelitian eksperimen</a>.</li> </ol>
LPL17 - January 2023
<p>Measurements of LPL17 (Isla de Tabarca) in January 2023</p> <p>Alicante</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL17">info</a></p>
LPL12 - January 2023
<p>Measurements of LPL12 (Ilhas Desertas) in January 2023</p> <p>Madeira</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL12">info</a></p>
LPL4 - January 2023
<p>Measurements of LPL4 (Ilha do Corvo) in January 2023</p> <p>Corvo</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL4">info</a></p>
LPL3 - January 2023
<p>Measurements of LPL3 (Madeira East Coast) in January 2023</p> <p>Madeira</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL3">info</a></p>
LPL8 - January 2023
<p>Measurements of LPL8 (Timanfaya NP) in January 2023</p> <p>Lanzarote</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL8">info</a></p>
LPL1 - January 2023
<p>Measurements of LPL1 (Teide NP) in January 2023</p> <p>Tenerife</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
LPL5 - January 2023
<p>Measurements of LPL5 (Tejeda) in January 2023</p> <p>Gran Canaria</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
LPL14 - January 2023
<p>Measurements of LPL14 (Ilha das Flores) in January 2023</p> <p>Flores</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL14">info</a></p>
SG - January 2023
<p>Measurements of SG (Global) in January 2023</p> <p></p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/SG">info</a></p>
Occupancy history of Seville's bike-sharing network. January-June 2021
<p>Occupancy history of Seville's bike-sharing network and station coordinates. January-June 2021 </p>
Cone Glacier velocity and climate data from November 2017 to January 2022
<p>Glacier velocity (m/yr) data obtained using Sentinel-2 near-infrared band 8 satellite images and the Glacier Image Velocimetry (GIV) open-source toolkit (Van Wyk de Vries & Wickert, 2021; https://doi.org/10.5194/tc-15-2115-2021). Precipitation (mm) and temperature (degrees centigrade) data obtained from gridded ERA5 reanalysis data (Hersbach et al., 2020; https://doi.org/10.1002/qj.3803) for the 0.25° x 0.25° grid square within which Cone Glacier sits. (2023-05-09)</p>
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during January-March 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during January-March 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during January-March 2017. The file contains these variables:</p> <p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
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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)
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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.